Hindawi
Wireless Communications and Mobile Computing
Volume 2018, Article ID 8035065, 23 pages
https://doi.org/10.1155/2018/8035065
Review Article
Prolonging the Lifetime of Wireless Sensor Networks:
A Review of Current Techniques
Felicia Engmann ,1 Ferdinand Apietu Katsriku ,2 Jamal-Deen Abdulai ,2
Kofi Sarpong Adu-Manu,2 and Frank Kataka Banaseka2
1School of Technology, Ghana Institute of Management and Public Administration, Ghana
2Department of Computer Science, University of Ghana, Ghana
Correspondence should be addressed to Felicia Engmann; fnaengmann@st.ug.edu.gh
Received 9 March 2018; Revised 10 July 2018; Accepted 17 July 2018; Published 15 August 2018
Academic Editor: Zheng Chu
Copyright © 2018 Felicia Engmann et al.This is an open access article distributed under theCreativeCommonsAttribution License,
which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
There has been an increase in research interest in wireless sensor networks (WSNs) as a result of the potential for their widespread
use in many different areas like home automation, security, environmental monitoring, and many more. Despite the successes
gained, the widespread adoption of WSNs particularly in remote and inaccessible places where their use is most beneficial is
hampered by the major challenge of limited energy, being in most instances battery powered. To prolong the lifetime for these
energy hungry sensor nodes, energy management schemes have been proposed in the literature to keep the sensor nodes alive
making the networkmore operational and efficient. Currently, emphasis has been placed on energy harvesting, energy transfer, and
energy conservationmethods as the primary means of maintaining the network lifetime.These energymanagement techniques are
designed to balance the energy in the overall network. The current review presents the state of the art in the energy management
schemes, the remaining challenges, and the open issues for future research work.
1. Introduction able to perform its intended function [10]. This could be
when any of the following events occur: when the first sensor
Energy efficiency has become a major theme in wireless sen- node dies or when a number or percentage of the nodes
sor network (WSN) research.The interest in energy efficiency die or when the network is partitioned such that there is no
may be attributed to limitations imposed by the batteries used
to power such devices. These batteries are usually the main communication between the subnetworks or when coverage
source of power for these devices and are characterized by a is lost [10–12].
limited lifespan, after which they are recharged or discarded To help extend the lifetime of sensor nodes and networks,
WSNs form the backbone of ubiquitous computing applica- energy conservation methods are usually employed. In this,
tions such as military surveillance, disaster, environmental, an effort is made to reduce the energy consumed by the unit.
structural, health and security, and wildlife and habitat The authors in [3] broadly categorized energy conservation
monitoring as well as precision agriculture. Deployment of schemes under the three main headings: duty cycling, data
sensor nodes is usually in inaccessible environments, and driven, andmobility driven techniques. Duty cycling is aimed
with limited battery capacity their lifetime is usually an issue at reducing idle listening when the node’s radio waits in
of major concern. Several techniques have been proposed in vain for frames and overhearing when nodes stay active
the literature to increase the lifetime of sensor nodes as well as listening to uninterested frames. Data driven techniques use
the sensor networks [1–6]. In recent times, long lasting sensor some parameters of the data themselves to make decisions
nodes that may never die have been proposed [7–9]. to reduce energy consumption during communication while
Several definitions have been proposed for the lifetime of mobility schemes consider the mobility of the sink or relay
a sensor network; however, a generally accepted definition is nodes as a factor affecting the energy consumed in the
when the network degrades to a point when it is no longer network.
2 Wireless Communications and Mobile Computing
SENSING COMPUTING COMMUNICATION
Sensor
Unit CPU
Sensor TRANSCEIVER
Unit MEMORY
POWER ENERGY 
· · · MANAGEMENT PREDICTION
ENERGY ENERGY 
STORAGE STORAGE · · · POWER HARVESTER
POWER UNIT
Figure 1: A typical architecture of a wireless sensor node. Adapted from [64].
Energy Source Energy Harvesting
System
Energy Storage (s) Sensor Node
Figure 2: Ambient energy harvesting to store and use. Adapted from [37].
energy from energy rich node to the energy deficient nodes.
Energy Source Energy Harvesting Sensor Node
System Energy transfer may be done wirelessly from a specialized
energy harvesting node or an energy resourced node to an
Figure 3: Ambient energy harvesting for direct use. Adapted from energy hungry node in the same network.The energy transfer
[37]. may be continuous or on-demand but is limited by the cost
of charge and discharge losses associated with it. Several
approaches have been proposed in the literature to provide
In Figure 1 the architecture of a typical wireless sensor reliable energy transfer to increase the network lifetime [9,
node is shown. Each component of the sensor node as 16–22].
seen in MicaZ mote is presented. As may be observed, a The method of ensuring that nodes have enough energy
typical node will consist of four major components, a sensing to function in the network bymaintaining appropriate energy
unit, processing unit, communications unit, and a power levels and transferring energy froman energy resourced node
unit. Of the different components, the communications unit, to an energy hungry node in the same network is referred to
which involves data transfer (involving both transmission as energy balancing. The use of energy balancing approach
and reception), expends a significantly higher proportion of to extend the life time of the networks may involve the
the energy available [13]. This is represented in Figure 11. use of any of the following schemes or a combination of
Typical energy conservation techniques simply seek to them: energy conservation, energy harvesting, or wireless
prolong the lifespan of the network by reducing the energy energy transfer. Energy conservation is the sparing use of
used and do not typically require the introduction of new energy in sensor nodes to allow sensor networks to be able
sources of energy. To increase the energy available to the to function as required [3]. It usually involves minimizing
sensor nodes, energy harvesting techniques have been pro- the communication cost in nodes [3, 15] since the radio is
posed [14, 15]. A key limitation of this technique is that known to be the greatest consumer of the available energy
energy sources may not always be available and hence there [12, 23, 24]. It may also be achieved by developing energy effi-
is the need to store the harvested energy using rechargeable cient routing protocols, clustering approaches, sleep/wake-
batteries or low-powered supercapacitors as in Figure 2 up optimization (duty cycling), and in some cases mobility
although in some cases the energy is utilized directly by the [15, 25, 26].
nodes as shown in Figure 3. Energy management schemes are the techniques
Another recent technique employed for prolonging the designed for the efficient use of energy in a network [23]
lifetime of sensor nodes and the network is transferring and in some instances for efficient use of harvested energy
ADC
PROCESSOR
Wireless Communications and Mobile Computing 3
Energy Management Schemes 
Energy Conservation Energy Transfer/ 
Charging Energy Harvesting 
Wireless Energy Transfer 
Radio Optimization Technologies, Tools & Sources of Energy 
Data Reduction Techniques Harvesting 
Data Compression Charging Techniques Energy Storage 
Data Prediction Joint Information & Energy Neutral Operation 
Energy Transfer 
Energy Balancing 
Figure 4: Energy management schemes in WSN.
[15]. Although some of the proposed energy management the challenges related to energy management schemes and
techniques assume that data acquisition through sensing provide future research directions in developing wireless
consumes less energy than data transmission [3, 23], this sensor networks that consider the trio (i.e., harvesting,
may not be so for all applications [27, 28] especially in transferring, and conserving) energy management scheme.
the case of energy hungry sensors, e.g., gas sensors. Most Finally, Section 7 concludes the paper.
of these techniques are used to prolong the lifetime of
sensor nodes by either reducing energy consumption or 2. Energy Harvesting
replenishing the consumed energy in battery powered nodes
or low-powered capacitors. In this paper, we attempt to Energy harvesting approaches scavenge for energy from
categorize the proposed energy management techniques the external environment such as wind, vibrations, solar,
into energy conservation mechanisms, energy harvesting, acoustic, and thermal. The techniques used in energy har-
and energy transfer/wireless charging mechanisms. In some vesting convert energy from the environment into electrical
applications, [29, 30] sensing may consume significant energy that can be used in wireless sensing nodes/devices.
percentage of the energy available. The broad categorizations In wireless sensor networks, energy harvesting can be used
of energy management used in this paper are energy to overcome the challenge of energy depletion that causes
conservation mechanisms, energy harvesting, and energy shorter lifetime of the nodes in the network and in other
transfer/wireless charging mechanisms as presented in cases of the black hole problem [31]. To realize the promised
Figure 4. A holistic approach to achieving energy balancing benefits of energy harvesting, concerted effort is required
in a network not only must be limited to energy harvesting on the part of researchers to address some outstanding
and transfer but also should include energy conservation. issues. Energy harvesting does not guarantee immortal nodes
In this paper we present the state of the art in energy and continuous operation due to the uncontrollable energy
management schemes (i.e., energy harvesting, energy con- sources, making them unpredictable and difficult to model.
servation, and energy transfer) and present the techniques The constant unavailability of energy harvesting sources is
used for harvesting, transferring, and conserving energy in discussed in [15, 32, 33]; hence a buffer is proposed to store
WSNs. We discuss management schemes related to radio energy for later use, using a battery-less sensor node and
optimization, data reduction, aggregation, compression and low-powered capacitors to act as buffers [5, 34], as shown
prediction, andwireless transfer technologies and techniques. in Figures 2 and 3. An example is solar energy which is not
We present the concept of energy balancing in wireless sensor available for harvesting at night due to the absence of the
network when energy harvesting, transfer, and conservation sun [35]. Table 1 gives specifications of some commercially
are used efficiently. We discuss limitations in existing sim- available solar energy harvesting units for use in sensor
ulators and emulators that are designed for modeling WSN nodes. In energy harvesting, nodes in the network may be
applications. Finally, we discuss the challenges and future attached with special devices for scavenging energy from
research directions for energy management schemes. the ambient environment for conversion into electric energy.
The rest of the paper is organized as follows. In Section 2, In the case of solar energy, the size of the panel is directly
we describe the different energy harvesting sources available proportional to the amount of energy converted through
and approaches. In Section 3, we describe the different wire- the photovoltaic technique [34]. This poses a challenge
less energy transfer techniques and technologies and present when the energy harvesting device becomes larger than the
the various simulation and emulation tools for modeling sensor node. Special energy harvesting devices may therefore
recent WSNs applications. Section 4 provides current tech- be provided in the network to scavenge energy and then
niques for energy conservation. In Section 5 current energy wirelessly transfer them to nodes. Powercast technology [36]
balancing schemes are presented. In Section 6, we present harvests energy from intentional, anticipated, and known
4 Wireless Communications and Mobile Computing
Table 1: Specifications of solar energy harvesting sensor nodes [37].
Node Solar Panel Power Solar Panel Size Energy Availability Storage Type Battery Type Battery Capacity(mW) (inxin) (mWh/day) (mAh) Sensor Node Used MPPT Usage
Heliomote 190 3.75 ∗ 2.5 1140 Battery Ni-MH 1800 Mica2 No
HydroWatch 276 2.3 ∗ 2.3 139 Battery Ni-MH 2500 TelosB Yes
Fleck1 - 4.56 ∗ 3.35 2100 Battery Ni-MH 2500 NA NO
Everlast 450 2.25 ∗ 3.75 2700 Supercap (100F) NA NA NA Yes
SolarBiscuit 150 2 ∗ 2 900 Supercap (1F) NA NA NA NO
Sunflower 4 PIN Photodiodes20mW NA 100 Supercap (0.2F) NA NA NA NO
AmbiMax 400 Supercap (two 22F)3.75 ∗ 2.5 1200 & Battery Li-poly 200 TelosB NO
Prometheus 130 780 Supercap (two 22F)3.23 ∗ 1.45 & Battery Li-poly 200 TelosB NO
Wireless Communications and Mobile Computing 5
sources using the Powerharvester Receivers. Powerharvester including use in devices attached to the body and implantable
Receivers are designed for 50 standard antennas on the 902 devices such as pacemakers for the heart. It is possible to
928MHz frequency band. envisage their use in other monitoring applications where
a temperature difference exists. A thermal energy harvester
2.1. Sources of Energy Harvesting. The source from which capable of achieving an output of 100 𝜇W was reported in
energy is harvested in a sensor network is a valuable resource [42].
since it determines the amount of energy available to the
network and the rate of conversion from the source to 2.5. Radio Frequencies Energy Harvesting. Given the large
electrical energy. Energy harvested may be classified under number of radio transmitters available in any urban environ-
ambient sources, which are sources available in the surround- ment, harvesting energy from this source is very appealing.
ing environment and human sources [37]. Ambient sources of Those devices capable of using harvested RF energy will
energy discussed include solar, vibration, thermal, and radio have very limited power requirements. In addition, they must
frequency. be in close proximity to the energy source or have a very
large antenna for collecting the energy. The basic principle of
2.2. Solar Energy Harvesting. Solar energy is an affordable operation is for the antennas to receive RF energy from the
and clean source of energy given its abundance in the envi- atmosphere and convert them to electrical signals as shown
ronment.Theharvesting of solar energy throughphotovoltaic in Figure 5. The matching circuit is made up of capacitor
effect is seen as the likely choice for sensor nodes with energy and inductor components and is used tomaximize RF energy
harvesting [15, 35, 38]. Even with its abundance there are in the circuit. The voltage multiplier is made up of diodes
times of the day when solar will not be available; hence and capacitors and the resulting energy is stored in either
there is a need for energy storage that balances the energy supercapacitors or rechargeable batteries. The conversion of
stored with the consumption rate of the sensor node. In RF signals to DC energy is dependent on the source of
[32], an energy neutral operation is employed when solar the power, antenna gains, and distance between source and
energy was the only source of energy and the sensor node receiver nodes and the energy conversion rate [15], given that
has no battery. Solar energy is obtained when a solar cell the power density of a receiving antenna is
receives sunlight with appropriate energy. The amount of
energy derived from a typical solar system is dependent 𝐸2
on the amount of illumination and the surface area of the 𝑃 = (2)𝑍
solar cell with power conversion efficiencies of 15% to 25% 𝑜
on crystalline silicon PV cells [34]. The other known PV where E is the electric field and Zo is the radiation resistance
cells are themonocrystalline, polycrystalline, and thin-filmed of free space. Assuming Zo = 377 ohms and an E value of
based [34]. Table 1 is a summary of some commercial solar 0.5V/m we obtain a power density value of 0.13𝜇𝑊/cm2.
harvesting tools from [37] and their specifications making Electric field values larger than 1 V/m are extremely rare.
them useful in WSNs. Progress in the use of RF sources will require advancement
in power requirements of wireless sensor nodes.
2.3. Vibration Energy Harvesting. Vibrational energy may be Some of the RF technologies that exist but are not
obtained through activities that produce sufficient vibrations optimized for WSN use include Bluetooth, Wifi technol-
like subways, industrial machinery, and vehicles. Amount ogy (IEEE 802.11a/b/h/g), and Ultra-Wideband (UWB IEEE
of energy harvested is approximated in 100-W range using 802.15.3). UWB has greater ratio of velocity with lower power
mechanical-to-electrical energy generators (MEEG) that use consumption as compared to Wifi and Bluetooth but is
piezoelectric (ferroelectrics) and magnetostrictive materials, limited to short range communications. Others that are being
and electrostatic or electromagnetic mechanisms to harvest developed forWSN use includeWavenis by Coronis Systems,
energy [39, 40].The harvested energy is directly proportional Wibree by Nokia, and Zigbee which is widely used by most
to the size of the MEEG used. In sensor networks where the WSN systems. RF power harvesting shown in Figure 5,
smaller size of the node is a requirement, vibration may not convert RF energy emitted by RF sources such as TV signals
be the best choice. and wireless radio networks.
2.4. Thermal Energy Harvesting. Thermal energy is based 2.6. Energy Storage. The use of harvested energy inWSN has
on the existence of a temperature difference within an a limitation since it is not always available. There is often
environment.The amount of energy obtainable is determined a need to store the harvested energy for later use. Sensor
by the Carnot cycle as nodes equipped for energy harvesting either have attached
(𝑇 − 𝑇) Δ𝑇 storage devices to store the harvested energy for later useℎ 𝑙 = (1) as in Figure 3 or may not have storage devices but directly
𝑇ℎ 𝑇ℎ use the harvested energy in the node as in Figure 2. The
where 𝑇ℎ and 𝑇𝑙 are the maximum and minimum tempera- storage devices could be either batteries (rechargeable and
tures of the thermodynamic cycle. nonrechargeable) or supercapacitors. To replenish energy
Efficiency values up to 17% have been achieved for small levels inWSN, rechargeable batteries and supercapacitors are
temperature gradients based on the Carnot cycle [41]. Ther- used. Batteries have limited recharge cycles [5], and hence to
mal energy harvesting has found application in many areas prolong the lifetime of nodes energy conservation techniques
6 Wireless Communications and Mobile Computing
Base 
Station 
Satellite 
TV
Radio
RF Energy
Matching Rectifier Charging Battery
Circuit Circuit
Figure 5: RF energy harvesting system.
must be employed together with energy harvesting. The use [49]. The energy densities of the NiMH are typically 60-
of supercapacitors [5, 43] is an alternative to batteries and 80Wh/kg while that of the lithium rechargeable batteries
may be repowered by energy harvesting. Supercapacitorsmay could be as high as 120-140Wh/kg. NiMH batteries are
be recharged with a recharge cycle of half a million years rated for 300-500 cycles while lithium batteries are rated for
with a 10-year functioning lifetime before the energy stored 500-1000 cycles, but their lifetime decreases with frequent
is reduced by 20% [44]. Supercapacitors have become more charge/discharge cycles. Even with the higher cycle efficiency
useful because of the high density of energy stored and their of the lithium rechargeable batteries, they are limited with
smaller size which is appropriate for WSN nodes. shorter lifetime.The electrolyte decays causing an increase in
the internal resistance. This causes the stored energy being
2.7. Batteries. The use of batteries in wireless sensor nodes is unable to be discharged.
to act as sources of energy, but with their limited capacity,
energy management has become an important research 2.9. Capacitors. Supercapacitors are alternatives to using
area. Replacing batteries when their capacity is depleted is rechargeable batteries. Traditional electrolytic capacitors due
inconvenient inmost applications ofWSN. Energy harvesting to their low energy density are usually not encouraged in
gives opportunity for nodes to receive energy either from the WSN. Current research proposes the use of supercapacitors.
ambient environment or from intentional sources [15]. This Supercapacitors are 10-100 times higher in energy density
energy can be stored in batteries and hence the batteries must than traditional electrolytic capacitors. Supercapacitors usu-
have the ability to be recharged. The amount of harvested ally have an energy density of 1-10Wh/kg high enough for
energy is usually less than needed to charge a battery applications in WSN. They are mostly preferred in energy
and hence must be stored to accumulate for intermittent harvesting systems due to the higher cycles, usually rated
use. Earlier power harvesting used traditional electrolytic as high as 100,000 cycles [49]. Table 1 presents some energy
capacitors as energy storage [45, 46] but with their limited harvesting nodes using rechargeable batteries and superca-
energy density, their output energy per discharge cycle is very pacitors.
limited.
2.10. Charge and Discharge Efficiency. Rechargeable and
2.8. Rechargeable Batteries. Rechargeable batteries are pre- supercapacitors may not have efficiency 100%. The Coulom-
ferred in WSN due to their high energy density [47, 48]. bic efficiency or charge/discharge efficiency is defined as
A 40mA nickel metal hydride (NiMH) rechargeable battery
could be charged from zero state to maximum state within an 𝐷𝑖𝑠𝑐ℎ𝑎𝑟𝑔𝑒𝑒𝑛𝑒𝑟𝑔𝑦
hour under the vibration from a typical vibrating machine. 𝑛 = (3)𝑐ℎ𝑎𝑟𝑔𝑒
Rechargeable batteries include nickel cadmium (NiCad), 𝑒𝑛𝑒𝑟𝑔𝑦
NiMH, and lithium ion/polymer (lithium) rechargeable bat- The types of rechargeable batteries mostly used in WSN [47,
teries. NiCad has memory effect not suitable for shallow 50] are the cadmium, NiCd, nickel metal hydride (NiMH),
charging, but energy harvesting is usually slow charging. lithium based (Li+), and sealed lead acid (SLA). SLA is not
NiMH and lithium rechargeable batteries were discussed in often used because even with low energy densities it also
Wireless Communications and Mobile Computing 7
i iＣＨ int1
+
+
RＣＨt
ilea
Vin CＭ VＭ
Rlea
- -
(a) Charged process
i
i int2out
+ +
RＣＨt
ilea2
Vout CＭ VＭ
Rlea
- -
(b) Discharged process
Figure 6: Charge and discharge process in rechargeable batteries.
has shallowdischarge cycleswhich causes temporary capacity authors focused mainly on the solar available periods and
losses, also known as memory effect. The NiMH and Li+ designed a dynamic power management scheme that allows
batteries are mostly used, but Li+ is more efficient. They the system to be operational for longer periods of time. To
have lower rate of discharge and longer lifetime cycle. But prove the performance of their approach, trace-driven sim-
they are also more expensive and have complicated charging ulations were performed based on real-world data collected
circuits, degrading faster when subjected to deep discharge over 11-year period.This scheme for powermanagement over
cycles. NiMH batteries also degrade to 80% of their charging a long-term period achieved better performance compared
capacities after repeating 100% discharges, degrading to 500 to other similar schemes. In [53], a real-time demonstration
cycles. Supercapacitors store charges but self-discharge at of vibration energy harvester was adapted to improve the
a much faster rate than batteries to as much as 5.9% on network through energy neutrality. The authors measured
a day. They have lower energy-to-density ratios of about different parameters such as data transmission and reception
5Wh/kg with high charge and discharge efficiency of 97- since these components consume a great deal of energy
98%. The charge and discharge efficiencies of frequently when the network is operational. Network-wide operations
used supercapacitors, Li+ and NiMH capacitors, are 95,92 such as routing, clustering, and the node duty cycling allow
and 65%, respectively [49]. In Figure 6 there is a diagram wireless sensor nodes to maintain their energy levels through
that shows the process of charge and discharge in a typical energy neutral operation [52, 54]. The uncertainty in the
rechargeable battery. The NiMH rechargeable battery has amount of harvested energy results in a more challenging
the least rate of decrease and has the largest leakage loss in protocol design and energy prediction models for such
caparison to Li+ and supercapacitors. networks. In most EH-WSN, designers and developers seek
to maximize the overall network performance while meeting
2.11. Energy Neutral Operation (ENO). In energy harvesting ENO. In order to overcome these challenges, it is relevant
wireless sensor network (EH-WSN), energy neutral operation to compensate the energy harvesting systems by providing
(ENO) aims at achieving the desired network performance energy chargers and transfer power from these charges to
that can be supported by the energy harvested from the power the sensor nodes.
required energy sources (i.e., solar, vibration, and RF) and the
network-wide operations (i.e., routing, clustering, and duty 3. Wireless Energy Transfer
cycling) over longer periods of time [32].The implementation
of WSN in environmental monitoring applications requiring Wireless energy transfer also known as wireless power trans-
uninterrupted operations has become common in recent fer is the ability to transfer electrical energy from a source
years. In such systems, power is constantly supplied to the storage to some destination storage without any plugs or
sensor nodes for efficient performance. The implementation wires [55]. In 1900, Nikola Tesla experimented the wireless
of ENO generally improves the network lifetime indefinitely transfer of power from device to another without contact
[51, 52]. Several researchers have proposed different schemes with large electric fields.These large electric fields diminished
to achieve energy neutrality in the network [51–53]. the energy transfer efficiency and coupled with the size
In [51], energy neutral operation was achieved by harvest- of large antennas required to make these transfers feasible
ing solar energy to improve the systems performance. The [8] Tesla’s invention was abandoned. Due to the pervasive
8 Wireless Communications and Mobile Computing
use of portable devices, wireless power transfer or wireless The addition of a parallel capacitor to the secondary
charging (these terms being used interchangeably in this coil to form a resonant circuit at the operating frequency
paper) has reemerged with much acceptance, already having increases the voltage received. The Wireless Power Consor-
commercial use in applications, for example, the electric tium in 2010 approved the first wireless charging standard
toothbrush and mobile phone wireless charging like in Apple (Qi) for low-power inductive charging. In an application
IPhone, Samsung Qi, etc. Wireless power transfer has been where robot swarms were powered, resonance was applied
achieved in applications such as RFID and medical implants on the receiving coil but not on the transmitting coil, to
using nearfield coupling. In 2007, Witricity was reintroduced minimize performance variations from the interactions of
by [56] who reported of powering a 60W bulb from 2 the robots [61]. It is from inductive coupling that other
meters with 40% efficiency using strongly coupled magnetic wireless power transfer methods like resonant magnetic
resonance [57]. Application areas include the electric vehi- coupling were derived where resonance is applied to both the
cle charging applications [58] medical sensors, implantable transmitting and receiving coils, and power transfer is done
devices and consumer electronics, and power transfer in with little radiated losses. Inductive coupling operates at a
concrete [59]. frequency of 13,56 and 135MHz. The transmission range is
In [36], the transfer of RF energy (between 850 and less than 1 meter and works best when the transmitter and
950MHz, central frequency of 915MHz) broadcasts radio receiver are in close contact (0 cm giving the highest power
waves in the 915MHz ISM band and a receiver tuning into transmission) and have accurate alignment in the charging
the same frequency harvest RF power. The work [14] reports direction. These limitations make inductive coupling not
that 45mW of energy is harvested within 10 cm of the RF desirable in WSN.
transmitter with 1% efficiency. Earlier research in wireless
power transfer considered transfer over single hops until [22] 3.1.2. Magnetic Resonant Coupling. First presented by [56],
demonstrated the possibility of transferring energy over mul- magnetic resonant coupling works on the principle of mag-
tihops. Their method gives room for possibility of neighbor- netic resonant coils where coils on the same resonance fre-
ing nodes to charge energy deprived nodes that may be out- quency are strongly coupled through nonradiative magnetic
side the charging range of energy charging devices like energy resonance. Energy is transferred from a source coil to a
transmitters [22, 60]. Initial power transfer approaches had receiver coil on the same resonance frequency with little
limitations in usage due to requirements such as close contact, losses to external off-resonance objects. The coils could be
continuous line of sight, and accurate alignment in charging made small enough to fit into portable devices such as sensor
direction. The work [56] experimentally demonstrated the nodes without decreasing efficiency. Experimental results
transfer of power from magnetic inductive coil to another from charging a 60-W light bulb at 2m in [56] reported
magnetic inductive coil that are in resonance. Resonance 40% power transfer efficiency. Challenges in magnetic res-
is achieved by the interplay between distributed inductance onance coupling include orientation and interference, with
from a transmitting coil and the distributed capacitance the maximum charging distance of 2m achieved only when
from the receiving coil. Strongly coupled magnetic resonance the transmitting and receiving coils are aligned coaxially.
between the coils enables the transfer of power between the A 45-degree rotation of the coaxial alignment reduces the
coils, and this is not affected by obstructive interfaces; it is coupling factor and when charging multiple devices mutual
nearly omnidirectional and not limited by line of sight. The coupling between the receiving coil and other objects may
work [56] suggested that the receiving coil could be smaller cause interference. Cannon [62] demonstrated power transfer
for portable devices without decreasing the efficiency of from a single resonant transmitting coil to multiple resonantreceivers provided they meet these two conditions: (1) coils
transfer. on the receiver must remain in the uniform magnetic field
3.1. Wireless Energy Transfer Technologies. The broad catego- generated by the transmitting coil; (2) mutual inductances
rization of wireless power transfer technologies is inductive between the receiving coils must have negligible effect on
coupling, electromagnetic radiation, and magnetic resonant the resonant interaction. This means receiving coils mustbe far enough from each other that their interactions with
coupling. the transmitting coil are decoupled. Designing a network
3.1.1. Inductive Coupling. Inductive coupling is the near field of mobile nodes that use magnetic resonant coupling is
wireless transmission of electrical energy from a primary therefore a challenge due to the second limitation. Giventhe limited distances allowed in resonance coupling and the
coil to a secondary coil. It is generated when an alternating coupling interference of nodes, new research challenges are
current in a primary coil from a source generates a varying opened in power transfer in WSN.
magnetic field that induces a terminal voltage of a secondary
coil at a receiver. In inductive coupling, the size of coil is 3.1.3. Electromagnetic Radiation. Electromagnetic radiation
directly proportional to the amount of energy generated. or EM radiation emits energy from the transmitting antenna
Its charging efficiency is reduced over short distances. Its of a source to the receiving antenna through EM waves.
simplicity and ease of use have led to several commercial The electromagnetic spectrum can contain regions of ambi-
applications including electric toothbrushes, charging pads ent energy levels of low and high regions and the effi-
for mobile phones or laptop and medical implants and RFID ciency of conversion depends on the part of the spectrum.
tags. Classifications of EM radiations are omnidirectional and
Wireless Communications and Mobile Computing 9
Table 2: Wireless power transfer technologies [8].
Wireless Power Transfer Technologies Strengths Weakness Examples
Short Charging distance,
Simple, high power transfer requiring accurate Electric toothbrush,Inductive Coupling efficiency in centimeters alignment in charging charging pad for cell
direction. phones and laptops.
Rapid drop of power Charging WSN for
Omnidirectional Tiny receiver size transfer efficiency over environmental monitoringdistances, ultra-low-power (temperature, moisture,
Electromagnetic reception light)
Effective power Requiring LOS and
Unidirectional transmission over long complicated tracking Sharp unmanned plane
distances mechanisms.
High efficiency over several
meters under Charging mobile devices,
Magnetic Resonant omnidirection. Not High Efficiency only within electric vehicles,
Coupling requiring LOS and several meters implantable devices and
insensitive to weather WSNs.
conditions
unidirectional. Omnidirectional radiation transmits broad- charging station that is assumed to have enough power to
cast EM waves in an assigned ISM band and a receiver in charge several nodes. Themobile charging vehicles may have
the same frequency harvests the radio power. Unidirectional power harvesters attached that scavenge energy from the
radiation on the other hand transmits from one source to environment and hence ensure continuous power supply to
a receiving antenna in an assigned band. Omnidirectional the battery station [9, 71, 72]. They could also have attached
EM waves dissipate over distances and in a paper by [8] the rechargeable batteries such that, after a cycle of charging
power transfer efficiency was 1.5% with a receiver at 30 cm. nodes in the network, the mobile charging vehicles return to
EM radiations with omnidirectional antennas can be used in a stationed power source to be replenished with its energy
low-power sensor nodeswith low sensing activities to prevent [55, 69]. The works [19, 20] studied the optimization of
hazards to humans. To achieve high power transmission in the vacation time of the wireless charging vehicles (WCV)
unidirectional antennas, microwave beams transmitted on over the cycle time. The use of multiple mobile chargers has
microwave frequency of 2.45 and 5.8GHz is used. Laser- also been proposed in [69] where a scheduling algorithm
beamed systems can be used for unidirectional power transfer is used to charge sensor nodes in a large network. Single
under the visible or near infrared frequency spectrum. mobile chargers may not carry enough energy to recharge
Unidirectional radiation is not suitable for wireless sensor energy node in the network if it is not equipped with energy
networks because they require line of sight and has compli- harvesting.
cated tracking mechanisms. Omnidirectional radiations are The deployment of multiple wireless chargers has been
used in applications where either the location of nodes is studied [63, 69, 70] and different methods are proposed.
unknown a priori or nodes are mobile. Powercast technology Of such methods is triangular deployment [8] of deploying
is a commercially developed device that uses the EM waves multiple mobile chargers such that a charger is placed at each
to transfer radio frequency (RF) power from a source to vertex of the triangle. The side length of the triangle yields
receiver(s). the minimum number of nodes for covering a plane. The
A summary of the wireless energy transfer technologies method accounts for the fading of recharge signals in space
is presented in Table 2. unlike in binary disks. The infrastructure used even though
fixed could have mobile nodes, and it is argued that having
3.2. Energy Transfer Tools and Techniques. Energy transfer mobile nodes as opposed to stationary nodes could have the
may be achieved using stationed energy sources that transfer advantage of having fewer chargers. The work [73] proposed
energy to nodes in the network [16] or by the use of mobile a hierarchical charging structure of multiple chargers in a
chargers [8, 16, 63]. Mobile chargers have been widely used network. Two groups of mobile chargers are formed: the
in the literature. Tools for energy transfer include mobile lower mobile chargers that charge the sensor nodes in the
chargers, charging vehicles or robots, energy transmitters, network and the higher Special Chargers to charge themobile
and the sensor nodes themselves. chargers.
Some approaches of wireless charging include the use of
3.2.1. Mobile Chargers. The use of mobile chargers has been specialized nodes called energy transmitters [74] to harvest
used by several authors when the energy of the nodes energy from the environment and then transfer to ordinary
runs low [8, 69, 70] using either human manned chargers nodes in the network. Electromagnetic induction, inductive
or robots. Mobile charging vehicles have also been used coupling, and RF energy transfer techniques have been
[8, 55, 70]. A mobile charging vehicle carries a battery discussed and proposed in literature [14]. With all these
10 Wireless Communications and Mobile Computing
approaches being discussed, there is still a need to use energy station spends at home recharging itself (also known as the
as a scares resource in the network to prolong the lifetime. vacation time) to the time spent in a cycle while charging
Near field coupling is applied in RFID tags and medical nodes. The assumption is that the mobile charger is charged
implant. The efficiency of near field coupling techniques enough that is may not be depleted in a cycle. But this may
decays with distance at a rate of 1/𝑟6 [75]. not always be so, especially in instances where the rate of
consumption of somenodes due the location-specific channel
3.2.2. Energy Transmitters. Energy transmitters used in re- behavior of some nodes that may change. In [32], distributed
search assume that the nodes have some attached antennas energy harvesting routing is proposed where battery-less
for receiving and transmitting energy from some device to nodes using energy directly harvested from the ambient
sensor node. Powercast technology [36] that introduced the environment can predict the amount of harvested energy
TX91501 Powercast Transmitter uses the 915-MHz ISM band and based on the rate of consumption of the node, the real-
to transmit radio waves for power and data. The TX91501 time residual energy is used to create a distributed protocol.
transmitter can broadcast power and data over 12 meters This protocol is limited and may not be practical for most
to Powercast receivers. The Powercast receivers convert RF energy harvesting sources whose continuous availability in
energy into DC for devices that are batteryless or wirelessly the environment may not be predicted and amount harvested
trickle charge batteries. In [16] they realized the high broad- may not be modeled.
cast range and power of the energy transmitters of as much
as 3 Watts introduces interference in data communication. 3.2.4. Charging Techniques. Techniques for charging sensor
A model for concurrent energy and data created three nodes are dependent on the source of energy being harvested
regions in a network: (i) the wireless charging region within and transferred and the antennas used. RF energy charging
which data can be transferred directly from a transmitter to is different from the conventional constant voltage charging.
neighboring nodes; (ii) communication region where nodes This is because the RFpower received for recharging a capaci-
can communicatewith the ETbut cannot be charged; and (iii) tor is constant for an RF source that transmits constant power
interference range where nodes receive interference from the to a fixed distance [65]. Some of the charging techniques
ETs energy transmission and therefore data communication for RF energy are discussed in [65] as multiple antenna
is affected during energy transfer of the ETs. transmissions, distributed beamforming of multiple anten-
nas, and cooperative relay and protocol based optimizations.
3.2.3. Monitoring Techniques. Monitoring approaches pro- This technique is described using the illustration in Figure 7.
vide means to check the energy consumption levels in sensor In [22] three charging techniques are proposed based
nodes and to report the amount of energy remaining in on Witricity and investigate the possibility of transferring
the node. This method of monitoring energy levels gives energy efficiently over multiple hops using long-time reso-
the energy source storage nodes the ability to know which nant electromagnetic states with localized slowly evanescent
nodes to charge and the schedule for charging. Somemethods field patterns. The techniques are as follows:
and techniques employed in research for monitoring energy (i) The store and forward technique assumes that nodes
levels are as discussed below. In [72], three charging schemes are equipped with rechargeable batteries. The main
are proposed. In two of the proposed schemes, nodes mon- power source which is assumed to be stationary
itor their energy levels and inform mobile chargers when charges neighboring nodes till their batteries are fully
their energy levels go below predefined threshold. A mobile charged or the source reaches an energy threshold of
charger patrols the shortest path computed and charges nodes 50%. The energy may then be transferred to nodes
within the threshold. In the third scheme, a function creates in the neighbors next hop and stored in batteries
a charging scheme based on the nodes residual energy and and then transferred to the next hop. It is assumed
its distance to a charger for chargers that charge more than 1 that nodes are attached with antennas for both trans-
sensor node. mitting and receiving energy and size and signal
In [71], a three-tier architecture of stationary sensors, interference is not a problem.
a mobile charger, and an energy station is proposed. The
energy station computes a charging sequence for the network (ii) The direct flow technique works on the principle
when sensor nodes periodically send information about their that a single node can couple with multiple nodes
energy level state to the energy station. Qi-Ferry, a mobile simultaneously. Nodes couple with the previous and
charger used in [76], describes the tour of the QiF as it goes the next nodes in their path from the source to the
through the network to charge nodes below some distance destination nodes, receiving energy without storing
threshold. The aim of QiF is to maximize the number of in batteries, directly transmitting to the next node.
sensors charges in a tourwhile reducing the amount of energy Charge and discharge losses associated with energy
spent by the charger in a tour. Qi-Ferry monitors its energy transfer are not incurred except at the last node.
level and iteratively removes tour stop from the tour until all Coupling coefficient of a pair of nodes does not affect
nodes are sufficiently charged. that of the next pair of nodes since the coupling
In [55] an optimal path is formulated for a mobile coefficient depends on the radius of the coils and not
charger to periodically charge all nodes in the network, and the distance between nodes.
this optimal path is the shortest Hamiltonian cycle. The (iii) The hybrid technique uses a combination of the
method is to maximize the ratio of the time the mobile store and forward technique and the virtual circuit
Wireless Communications and Mobile Computing 11
Sensor
Node
Data Energy
Transfer Flow
Sensor
Sensor
Node Node
Base Sensor Energy beamforming Node Base StationStation
using antenna array (BS)Transmission range D
Single hop energy transfer
2-hop energy transfer
Sensor BS1
Node
Relay Node Discontinuous Distributed
2-hop BeamformingRF Source transmission Relay Node BS2
Continuous Distributed Sensor
one-hop beamforming Node Transmission Range D+d
transmission
RF energy transfer range extension using two hop 
Cooperative energy transfer with distributed beamforming
Relaying
Figure 7: Beamforming techniques for RF energy harvesting [65].
technique. It uses a two-step approach. To transfer rate-energy tradeoff region is achieved. A new transmission
energy to the Nth node, where Mth node comes strategy that satisfies the condition of signal-to-leakage-and-
before the Nth node in terms of distance from the energy-ratio maximum beamforming is also proposed. A
energy transmitter, the direct flow is used to transfer general k-userMIMO interference channel is explored in [84]
m-hops to the Mth node where it is stored; then where three scenarios are investigated: (i) multiple energy
through another direct flow technique the energy harvesting receiver and a single ID receiver; (ii) multiple
stored in the Mth node is transferred to the Nth node. IDs and single energy harvesting receiver; (iii) multiple IDs
The advantage is to be able to transfer to several and multiple energy harvesting receivers, where IDs are
hops while overcoming the challenges of the charge devices for information decoding and energy harvesters are
and discharge losses. The limitation of the technique for receiving RF energy from the ambient environment.
that could transfer energy to as much as 20 hops An energy beamforming scheme requires partial CSIT and
is that real life test of the technique has not been reduces the feedback overhead in a two-user MIMO and k-
made to ascertain the feasibility. Real nodes having user MIMO IFC using Geodesic information/energy beam-
both transmitter and receiver antennas are yet to be forming strategies. A summary of beamforming schemes
produced and tested. is presented in Figure 5. The work [65] discusses the RF
harvesting efficiency prevalent in low RF-to-DC conversion
3.3. Joint Wireless Information and Energy Transfer. RF efficiency and receiver sensitivity, with new communications
signals carry both energy and information and with the techniques enhancing the usability of RF energy harvesting.
widespread use and application in sensor networks and
IoT, research into JWIET has attracted significant attention. 3.4. Simulation and Emulation Tools
Optimal transmission strategies and performance limitation
of JWIET have been studied under perfect full channel 3.4.1. Castalia Simulator. Castalia [85] is an open source
state information at the transmitter (CSIT) [77], considering simulator built on top of OMNeT++, and it is optimized
the downlink of a cellular system with single base station for testing distributed algorithms and protocols and features
and multiple stations, cooperative relay system in [78], and an accurate channel/radio model, radio behavior, and other
broadcasting system in [79]. Transmission relies on the CSIT aspects of communication. It has parameters for clock drift,
but acquisition of the full CSIT incurred large overheads sensor bias and node energy consumption, CPU energy
[80]. Partial CSIT has been considered in [81, 82] with robust consumption, memory usage, CPU time, and some imple-
beamforming schemes. In [81],MISO downlink broadcasting mentation for MAC and routing protocols. It provides a
channel with three nodes and a single MISO uplink channel first-order analysis of algorithms and protocols before their
in [82] are considered. In [83], a two-user MIMO interfer- implementation of node platforms.
ence channel in which a receiver either decodes incoming
messages or harvests RF energy to operate forever is studied. 3.4.2. EKHO. EKHO [66] is an emulation tool that records
A transmission strategy that achieves maximum energy and emulates energy harvesting conditions from diverse
beamforming and minimum leakage beamforming for a sources in the ambient environment such as solar, thermal,
12 Wireless Communications and Mobile Computing
Surface Manager Record Emulate
Serial/USB
I-V Curve
Controller MCU (xMEGA)
Front end
Voltage Current Replay 
Sense Sense Current DC Power
Load
Energy Storage MCU Sensors Comm…
Figure 8: EKHO, a simulator for energy harvesting [66].
RF, and kinetic. The energy harvesting environmental condi- energy efficiency. The Architecture Resource Layer (ARL)
tions are recorded and stored in a digital format which could provides the service layer (SL) that provides APIs for com-
be replayed through analog front-ends serving as energy posing different applications. The Resource Behavioral Layer
source. EKHO is presented in Figure 8. defines hardware resources for target architectures and the
Resource Annotation Layer specifies energy-performance
3.4.3. OMNeT++. OMNeT++ [86] is a discrete event simu- details. At the energy level, the Energy Source Layer collects
lator based on C++ for modeling communications networks, energy sources for sensor nodes. The Energy Source Layer
multiprocessors, and distributed and parallel systems. It was provides energy source models such as batteries and super-
developed as an open source tool that could be used for capacitors and may include models for energy harvesting.
educational, academic, and research oriented applications, to But the energy harvesting models are not complete. PASES
bridge the gap between open source research oriented sim- is presented in Figure 9.
ulators like the NS-2 and expensive commercial simulators
like the OPNET. It is available on Linux, MAC OS/X, and 3.4.5. COOJA. TheContiki OS Java or COOJA simulator [87]
Windows. OMNeT provides basic machinery for users to is a Contiki sensor node operating system and usually inte-
write simulations and consists of modules that communicate grated with MSPSim to form the COOJA/MSPSim. COOJA
by message passing. It has two major simulation model allows simultaneous cross-level simulation at the application
frameworks: the mobility framework and the INET frame- (network level), operating system, and machine code level.
work. OMNeT++, unlike NS-2 and NS-3 which are network COOJA combines the elevated level behavior of a node to
simulators, is a simulation platform upon which researchers the low-level sensor node hardware in a single simulation.
could build their own simulation frameworks. It does not COOJA supports adding and using different radio mediums.
have a framework for energy modeling. It allows for the flexible additions and replacements of its
parts including the radio medium, the hardware node, and
3.4.4. PASES. Power Aware Simulator for Embedded Sys- plug-ins for input/output. With all the cross-level support
tems (PASES) [67] is a SystemC based framework that is provided in COOJA, it does not have an energy model
a combination of an event-driven simulation engine and a and the energy parameters of nodes may not be properly
hardware, applications, and network models composer. It is analyzed during simulations. COOJA has a Visualizer.java
a simulation and design space exploration framework that is class that could be extended to provide GUI and has support
used for power consumption analysis of WSNs application, for memory and radio model simulations but has relatively
communication, and platform layers. It gives performance low efficiency with increasing number of nodes hence not
and energy analysis ofWSNhardware platforms and provides scalable.
a gap between pure network oriented WSN tools and tools
for architecture specific simulation environment. It supports 3.4.6. Network Simulator 2 (NS-2). NS-2 or Network Sim-
Platform Based Design methodology and provides power ulator 2 is a discrete event simulator based on the Object
analysis for different platforms by defining abstraction layers Oriented Programming (OOP) and consists of two lan-
for the application, communication, hardware, power supply, guages: C++ and Object Oriented Tool Command Language
and sensing modules of the network and node. PASES (OTcL) bound together by TcLCL. Codes written in OTcL
provides these abstractions: the software layer provides users will be visualized using NAM and XGRAPH with optional
with the application layer (AL) to define application function- python bindings. NS2 has support for protocols such as
ality. 802.11.802.16 and 802.15.4 but is limited with support for
The communication layer (CL) could be tweaked to sensing. Parameters such as energy model, packet formats,
meet network requirements such as throughput, latency, and and MAC protocols are different from those used in real
DAC
ADC2
ADC1
Wireless Communications and Mobile Computing 13
System Requirements: Network Requirements: 
performance, lifetime, .... latency, throughput, ....
App Design 
(Python/C++) 
Application Layer Communication Layer 
AODV, TREE: 
MAC & Routing Protocols IEEE 802.15.4 
TMAC, ....
Simulation Results Choice of services 
Lifetime 
Performance, 
...... Service Layer 
(HAL, API, Middleware) Resource Behavioural Layer 
Node #N Runtimer, PutCPU2Sleep, CPU, Timer, ADC, SendRadioPkg, Sensors, RF, ...
Node #2 GetADCSample, Choice of energy 
.... performance for HWNode #1 
Resource Annotation Layer
Network model: Choice of battery parameters:
8 Nodes, Capacity, voltage, ....
Location, Sensor Node instance Energy Source Layer
Signal propagation (Battery, Energy Harvesting)
model 
····
Figure 9: Design space exploration methodology of PASES [67].
sensor network nodes. NS-2 has parameters for residual the amount of energy that will be available in the future based
energy but does not give models for keeping track of energy on information from the basic energy source and energy
consumed by the different components and does not have a harvester. An extended diagram of NS-3 with modules for
model for energy harvesting. energy harvesting is presented in Figure 10.
All the above tools provide some support for energy
3.4.7. Network Simulator 3 (NS-3). NS-3 [88] is not consid- modeling and even some simulators like the PAWiS, WSNet,
ered an extension of NS-2 but is an entirely new simulator OPNET, and Qualnet not mentioned above provide energy
written in C++ with optional python bindings. Energy model modeling but not completely. Support for energy modeling
inNS-3 consists of twomajor components: energy source and is still an open research especially when multihop energy
the device energy model. The energy source is an abstract transfer is considered in WSN.
base class that provides interface for updating/recording total
energy consumption on a node, keeping track of remaining 4. Energy Conservation
energy, decreasing energy, and when the energy is completely
depleted. The device energy model monitors the state of Energy conservation methods are concerned with reducing
the device to calculate its energy consumption. It provides energy consumption of the nodes. To conserve energy, the
an interface for updating the residual energy in the energy major components in a sensor node that consume energy
source and gives notification from the energy source when must be controlled. The lifetime of a sensor node, which the
energy is completely depleted and maintains a record of the lifetime of the network is dependent on, is an indication of
total energy consumed by the device. NS-3 provides energy how much energy is consumed and the amount of energy
model for Wifi Radio with states IDLE, CCA BUSY, TX, RX, available for use [11].
and SWITCHING. Developers may extend on the models in Definition of a network frequently used in literature is of
NS-3 to model different scenarios that may not be present in n-of-n such that
current releases. 𝑇𝑛 = min𝑇
NS-3 allows for the definition of new energy sources that 𝑛 V (4)
incorporate the contributions of an energy harvester, with where𝑇V is the lifetime of node V [11].The lifetime of the node,
the addition of an energy harvester component with existing which is a function of energy consumed and energy available
energy source as well as the possibility of evaluating the for use in the network, depends on the activities of various
interaction between energy sources and the different energy components, being the sensing, processing, radio, and power
harvesting models. The work [68] provided an extension of supply units, with typical energy consumption of the various
the current energy models in NS-3 introducing the concept units of the node presented in Figure 11.
of energy harvesting. Two energy harvesting models are as The sensing component consists of the sensors with
follows: the basic energy harvester, providing time-varying, Analog-to-Digital (ADC) converters for collecting data from
uniformly distributed amount of energy and the energy the environment that are then fed into the processing unit.
harvester that recharges the energy source. A model is for The processing component manages the node by perform-
a supercapacitor energy source and a device energy model ing internal computations and aggregation of data with
is for energy consumed by a sensor node. A model for an other nodes in the network and has a storage unit/memory
energy predictor was introduced that is supposed to predict included working as a temporary buffer. The transceiver
Architecture
Resource SW Level
Level
14 Wireless Communications and Mobile Computing
Supercapacitor 
RV Battery Model 
Amount of Energy Lithium Ion Energy Source 
to Remove
Device Energy model Basic Energy Source 
Simple Device Energy Energy Source 
Model No Energy 
Remaining 
Wifi Radio Energy Model 
inin
g 
Sensor Energy Model a
ergy
 rem
nt en Amount of energy harvested re
Cur
Predicted Energy 
Amount 
Energy 
Predictor Energy Harvester 
Basic Energy
Predictor Basic Energy Harvester 
Real Data Energy Harvester
Figure 10: NS3 extension with energy harvesting model and basic energy model [68].
Energy Consumption in a typical Wireless Sensor Node Energy conservation methods provide means of reducing
4.44% 4.44% energy consumption by the different components of the sen-
6.67% sor node as shown in Figure 1. Of the different components,
data communication expends the maximum energy available
24.44% [13], where communication involves both transmission and
reception.
The sensing component consists of the sensors with
Analog-to-Digital (ADC) converters for collecting data from
33.33% the environment that are then fed into the processing unit.
The processing component manages the node by perform-
ing internal computations and aggregation of data with
other nodes in the network and has a storage unit/memory
26.67% included working as a temporary buffer. The transceiver,
also known as the radio unit, connects the node to the
Sensing Rx network. The power unit consists of the battery or low-
CPU IDLE powered capacitors and serves as the source of energy. They
Tx SLEEP may also be supported with power scavenging units for
Figure 11: Energy consumption of the components in a sensor node. energy harvesting. The control of the energy consumption
of the various components of a sensor node has led to the
generation of some methods in energy conservation which
also known as the radio unit connects the node to the may be classified as radio optimization techniques, data
network. The power unit consists of the battery or low- reduction, and efficient routing techniques. In general, energy
powered capacitors and serves as the source of energy. They conservation methods focus on networking and sensing.
may also be supported with power scavenging units for Networking comprises the management of the sensor nodes
energy harvesting. The control of the energy consumption and the design of the network protocolswhile sensing is based
of the various components of a sensor node has led to the on the techniques to reduce the frequency of sensing.
generation of some methods in energy conservation which
may be classified as radio optimization techniques, data 4.1. Radio Optimization Technique. The radio module is re-
reduction, and efficient routing techniques. In general, energy sponsible for wireless communication and is the component
conservation methods focus on networking and sensing. that consumes significant amounts of energy in the network.
Networking comprises the management of the sensor nodes To optimize the radio, techniques used include coopera-
and the design of the network protocolswhile sensing is based tive communication schemes, sleep/wake-up schemes, duty
on the techniques to reduce the frequency of sensing. cycling, and radio optimization parameters such as radio
Wireless Communications and Mobile Computing 15
coding and modulation techniques, power transmission, and achieved by relay nodes receiving data from sources and
antenna direction. The radio transceiver is one component then transmitting to destinations using some cooperation
that consumes much energy since it is used in data commu- protocols (amplify-and-forward, decode-and-forward, and
nication. Energy conservation methods focus more on data compress-and-forward). In node cooperative systems, nodes
transmission since more energy is expended from the node cooperate by either communication terminal using combined
during data transmission than data processing/computation. processing or coordinating the strategies for communication
Energy consumed during sensing may be considered in at the terminals. Challenges in the implementation of cooper-
energy conservation since the energy consumed may be ative communication systems include, but are not limited to,
comparable to or even greater than communication [27, cooperation assignment and hand-off, network interference,
28, 89] in some applications and cannot be ignored. Radio transmitting and receiving requirements of wireless systems,
optimization techniques provide means of mitigating the and the loss of rate to the cooperating mobile system [93].
energy consumption of sensor nodes due to wireless commu-
nication. Radio optimization techniques considered include 4.5. Sleep/Wake-Up Schemes. Sleep/wake-up schemes adapt
SISO, MIMO, cooperative communication schemes, sleep the node to the activities of the network to conserve energy
wake-up schemes, and Transmission Power Control. by putting the radio to sleep, to minimize idle listening (idle
sensing of the channel). Duty cycle is defined as the ratio
4.2. Single-Input Single-Output (SISO). Single-Input Single- of time nodes that are active during their lifetime and the
Output (SISO) refers to the direct transmission of data from sum of the times when the node is on and asleep [94], which
single nodes to a base station usually through a single hop means nodes alternate between sleep and wake-up times.The
transmission. Challenges of SISO include data congestion, radio transceiver of nodes is made to sleep when there is
collisions, and loss of energy when the distance between node no communication and wakes up when data transmission
and base station is big. is required. The alternating of sleep and wake-up periods is
referred to as duty cycling. The downside of this technique is
4.3. Multiple Input Multiple Output (MIMO). Multiple Input that data generated during the vacation period (when node is
Multiple Output (MIMO) systems assume that multiple sleeping) may be lost. Another technique that is data driven
antennas on nodes transmit data to multiple receivers. The senses the channel until some data is generated; then it wakes
application of MIMO spreads the power to transmit among up for transmission. Unnecessary data may be transmitted to
different antennas on nodes in the network to achieve power the sink increasing energy consumption and could also be too
gains. This increases the bandwidth for high data rates and energy consuming if the data sensing is not negligible.
bit-error-rate performance requirements [74, 90]. MIMO has To optimize the sleep period, event-driven systems adapt
challenges in WSN due to the limited physical size of typical selective and incremental wake-up scheme, where low-
sensor nodes that cannot support multiple antennas and the powered sensors continuously monitor the system, until
energy consumed by the circuit energy of the transmitter some event trigger is received; then nodes are triggered for
and receiver in the system. When the number of antennas high-quality detection and quality sensing. Another method
increase, the circuit energy consumed by multiple antennas puts all nodes to sleep but they wake up when there is a
increases [74]. To mitigate the limitations of MIMO, coop- demand by another node to communicate. This means nodes
erative MIMO technologies are constructed that minimize are active only for a minimum time during communication.
the energy consumed in transmission especially in long range No sensing of the network is required and it is appropriate
transmission where the benefits of MIMO outweigh SISO for applications where sensing consumes much energy and
[90, 91]. SISO has efficient energy consumption for short periods of data communication are known a priori. Thirdly,
range transmissions but still requires approaches tominimize there are methods where all nodes sleep and wake up at the
circuit energy consumption. The reader could read papers same time according to a wake-up schedule. These meth-
[74, 92] for further benefits of MIMO. ods are appropriate for data gathering applications where
aggregation may be required but more collisions will be
4.4. Cooperative Communication Schemes. Cooperative com- introduced in such networks as all nodes wake up at the
munication schemes provide means of communication by same time. Asynchronous methods allow nodes to wake-up
allowing the terminals/antennas in a multiuser environment independently with overlapping wake-up periods with the
to collaborate in communicating in a sensor network, using neighbors. Such networks require active periods of sensing or
the broadcast nature of wireless networks. Single antennas nodeswill have towake up frequentlywhen sender sends long
in a multiple user environment collaborate to share their preamble or receiver remains active for longer periods. All
antennas to form a virtual multiple antenna transmission, will require huge energy cost to the network and hence this is
thereby gaining the benefits of MIMO systems while over- not an efficient energy conservation technique, but good for
coming their challenges, such as size, cost, hardware, and QoS purposes [3].
deployment limitations [76, 93]. Wireless nodes in cooper-
ative communication systems act as transmitters and also 4.6. Transmission Power Control. The aim of Transmission
cooperative agents for other users. Two ways of cooperation Power Control (TPC) approaches is to dynamically adjust
are introduced in [76] called the relay cooperation and node the transmission power of the radio to maintain an effec-
cooperation. Relay cooperation is when extra relay nodes tive communication link between pairs of nodes while not
help to transmit data from source to destination. This is transmitting at full power capacity [95, 96]. Factors such
16 Wireless Communications and Mobile Computing
as distance and link quality affect the transmission power A survey of data aggregation algorithms is presented in
within a transmitter-receiver pair. A survey of TPCs by [95] [101] and analyzes different solutions against performance
investigated existing approaches of protocol development metrics such as data latency and accuracy. In data aggregation
that were based on single-hop communication in WSN. algorithms, there is usually latency and accuracy based on
An Adaptive Transmission Power Control (ATPC) [96] was the application area. Energy efficiency, aggregation freshness,
proposed that builds a model for communication where and collision avoidance are some performance metrics used
neighboring nodes create a correlation between transmission in data aggregation.
power and link quality. A feedback-based TPC algorithm is
employed to dynamically maintain individual link quality, 4.9. Data Compression. These are techniques that reduce the
creating a pairwise adjustment for ATPC that saves energy size of sensed data before transmission. This reduces the
with online control and is robust to environmental changes. amount of energy consumed in processing and transmitting
A TPC method for SCADA systems is used for industrial data in individual nodes in the network, reducing the size
control of their remote stations and a central site [97]. of bandwidth used. A basic assumption in compression is
Using a fuzzy based algorithm, a minimum number of that the amount of energy consumed compressing a bit of
transmission paths are maintained between the sink and data into b, such that 𝑎 < 𝑏, must be smaller than the
source nodes while maintaining minimum multihops. The amount consumed in transmitting a−b string of data [102].
effect of different Transmission Power Control protocols The work [103] presented a survey of mechanisms for data
on the lifetime of WSN is studied in [98] when power compression. The assumption used in data compression is
levels and strategies for transmission power assignment are that multiples of energy consumed per 480 addition instruc-
discretized. The bandwidth of TPCs and the granularity tions are consumed for every bit of data transmitted by radio.
of the power control of the link-level affect the energy If more than 1 bit of data is taken from sensed data by data
consumed. compression, total power consumed by transmitting that data
will be significantly reduced. Data compression techniques
4.7. Data Reduction. Data reduction techniques in WSN in WSN take into consideration the size of the compression
reduce the amount of data that is transmitted to the des- algorithm and the processing speed (that of a typical WSN
tination, usually the sink, thereby reducing the number node is 128MB and 4MHz, respectively). Examples of these
of transmissions. These techniques reduce the bandwidth compression techniques are coding by ordering, pipelined
needed to send data as it traverses the network from source in-network compression, low-complexity video compression,
to destination (usually the sink). Some techniques used are and distributed compression [102].
data aggregation, compression, and prediction. Others are
network coding and efficient routing. 4.10. Data Prediction. Prediction is a term given to the
process of inferring missing values in a dataset based on
4.8. Aggregation. Aggregation techniques fuse data as it statistical or empirical probability or the estimation of future
traverses the network from one node to the other to the sink. values on some historical data. A prediction method is a
Its main aim is to aggregate data in an efficient manner to function with two inputs, the set of observed values and
increase the network lifetime [99]. Since near nodes share the set of parameters. A model created for prediction is
similar data by spatial correlation, energy is wasted when deterministic and obtained from the observed values, but
the same data value is routed from multiple sources to the one could have several prediction models from the same
sink. Transmitting 1 KB of data over 100m expends energy prediction method or algorithm [104].
as much as executing 300 million instructions on a typical Prediction methods require additional information about
processor with 100MIPS [100]. In-network data aggregation the observed data which may be known to the user before
can be broadly categorized as Address-Centric (AC) and deployment which can be applied to the statistical data.
Data Centric (DC). It reduces medium access contention and The additional information may support assumptions made
the number of transmitted packets and minimizes packet in predictions that determine the feasibility of the model.
transmission delays. Aggregation in the network can be done This feature of prediction models makes them more reliable
via data aggregation tree (DAT for flat networks) and by compared to machine learning techniques that use fewer
clustering for hierarchical networks. Some key points in data assumptions of the data in exchange for the time to adjust
aggregation are as follows: parameters and adapt to the observed data set. This does
not give the user the opportunity to see the prediction
(i) Nodes sense data values on the entire network and accuracy before testing with real data. The work [104]
route to neighbor nodes. groups prediction schemes under two main headings: single
(ii) Sensor nodes can receive different versions of the prediction schemes and dual prediction schemes. Single
same message from different nodes in the network. prediction schemes are made at one point in the network
(iii) Data is combined from diverse sources and routes to which could either be closer to the sensor nodes or close
mitigate redundancy. to the data collection point. In this, sensor nodes may sense
data but based on the reliability of predictions of the sensed
(iv) Intermediate nodesmust access the content of packets data predict changes to the amount of data measured and
to be aggregated. transmitted. The advantage is that each device may decide
(v) Nodes must wait for a predefined waiting time (WT). to adapt itself based on the predictions or not without a
Wireless Communications and Mobile Computing 17
need to synchronizewith neighbors of their decisionswithout will not be the same throughout the network. Rotating cluster
incurring any overhead cost of communications. This could heads changes the topology of the network at each round of
eventually reduce the quality of the information derived from rotation and imposes change over overheads [114, 115].
the cluster heads sincemost datamaynot be transmitted from All cluster heads in the network must be notified of the
the sensor nodes.With the autonomy of cluster heads coupled change while cluster heads change their routing tables and
with the spatiotemporal correlation of sensor nodes placed scheduling strategy. Some methods include the addition of
near each other, probabilistic models could be generated with high energy specialized nodes in the network to balance the
good distributions and confidence levels that may be used to load in various locations of the sink node to balance energy
predict measurements thereby reducing transmissions. consumption. In [116], high energy nodes called gateways are
Applications of this models are used in adaptive sampling proposed which form equal sizes of clusters in the network
[105, 106], clustering [107], and data compression [108, 109]. based on the cost of communication and the load on the
Dual prediction schemes on the other handmake predictions gateways. These gateways act as cluster heads and perform
of the cluster heads and in sensor nodes. When sensor energy consuming tasks like data fusion and organization of
nodes measure values outside the threshold of the prediction nodes for special tasks. The method solves the problem of
models, the value from the sensor nodes is transmitted to extra overhead incurred by frequent reclustering on nodes
the cluster heads which then sends to the sensor nodes the since this task is performed by the gateways. The addition of
correct value. Frequent transmissions and therefore energy specialized nodes comes at an extra cost and the optimized
consumption are hereby reduced. The aim of dual prediction number added in a network must be considered. Other
schemes is to mitigate the number of transmissions without methods balance the energy consumption in the network
compromising on the quality of measurements made by by forming clusters of unequal sizes [7, 117, 118]. In these
the systems and hence a tradeoff between the number of approaches, the size of clusters increases as one approaches
transmissions for new prediction model distribution and the the base station. The assumption is that, for nodes further
reliability of the channel is always made [109, 110]. away from the base station, multihoping data through relay
cluster is more energy efficient than directly since the amount
5. Energy Balancing of energy required to transmit data from one node to another
is directly proportional to the distance between the two
Energy balancing techniques that distribute the amount of nodes. Cluster heads aggregate data from their clusters and
energy in the network such that nodes have equal amount of relay data from other cluster heads to the base station. This
energy and have prolonged lifetime have been discussed in means cluster heads closer to the base station will be depleted
this paper.They comprise data reduction schemes that reduce of their energy faster than nodes on the peripheral.
the amount of data that is delivered to the sink node and This paper includes energy harvesting schemes aug-
balancing schemes that optimize the distribution of energy mentedwith energy transfer technologies to distribute energy
available to the node and energy consumption of the nodes available in the network fairly such that nodes are not
in the network. depleted of their energy below some threshold when they
Balancing schemes proposed in recent research discussed are no more operational. Energy conservation techniques are
means of distributing and managing the energy in a sensor also included to ensure efficient use of energy by the sensor
node [23]. Clustering schemes have been used to balance node with minimal consumption.
energy in the network, and they were processes where nodes
are grouped together with a coordinator, usually known as 6. Challenges and Future Research Directions
the cluster head, that perform specialized functions such as
data fusion and aggregation, and communicate this data from In energy transfer in wireless sensor networks, some
its clusters to the base station. Most published clustering researchers have attempted to solve the distance related
approaches form groups of nodes and allow these nodes to energy transfer issues [8, 34, 55, 63]. Despite the attempts
select a cluster head based on some criteria. The cluster head to resolve these issues, there remains a great deal of work
selection can be randomized [111] or based on degree of in this area. In this section, we present some challenges in
connectivity [1]. Some approaches include the residual energy energy harvesting and energy transfer and propose likely
of the node as a criterion for cluster head selection [7, 112]. future works.
The possibility of unbalanced energy consumption in the
network was due to the different consumption rates of energy 6.1. Challenges
of nodes and their distance from the base station, causing
some clusters to be of high energy while others are of low Cost of Experimentation Using Testbeds. Research in WSN
energy, in a situation known as the black hole problem [31]. requires comprehensive evaluations process that could be
To solve this problem, the unequal clustering approaches [113] verified and reproduced. Most studies done over the years
have been proposed. A round-robin method causes cluster have evaluated theoretical analysis and simulations lacking
heads to be rotated among the nodes in the network of experimental evaluations. Over the years research into the
homogeneous nodes (beginning the network formation with transfer of energy fromnode to node or froman energy trans-
the same energy level) and have the same capabilities. The mitter to nodes by either single hop or multihop has been
assumption is that due to different transmission and recep- proposed as a solution to making sensor networks immortal.
tion rates of data of the individual nodes energy depletion Most of such research is based on the modeling of the
18 Wireless Communications and Mobile Computing
networks and the charging scenarios. Creating real testbed other energy sources like supercapacitors) and the energy
experiments for energy transfer is still ongoing research with harvesting model.The energy harvesting model increases the
a little breakthrough. The work [56] performed simulation energy stored in the energy source and could be modeled
and temporary transfer of energy over magnetically coupled as some other energy harvesting source [68] like in solar
resonance coils of 1m diameter or less with 60% efficiency. energy harvesters in [120]. Other simulators like Castalia,
This shows a direct transfer without storing or retransmission EKHO, andCOOJAdo not havemodels for energymodeling.
of the power. Powercast Technology [36] has the Powercast Castalia does not provide battery or energy modeling and
energy transmitter that provides an EIRP of 3W for 5W therefore does not support lifetime estimation simulations.
DC input but does not generate continuous RF output. The It also does not have postprocessing tools for GUI support
work [22] proposed a solution for multihop energy transfer [86]. PASES [67] is a design space framework that was created
using theory and simulations to investigate the phenomenon with power awareness for the different components of the
of slowly evanescent field patterns that can transfer energy sensor node. It has the energy level that introduces the Energy
efficiently. Their results proved the transfer of energy over 20 Source Layer that analyzes the power consumption of the
hops but lack testbed tests.Thework [119] demonstrated mul- hardware components and models for energy sources like
tihop RF energy transfer within two hops using the MICA2 batteries and supercapacitors and energy harvesters. It has
mote operating on the supercapacitors on the Powercast P1110 model for the device energy model but does not give a user
Evaluation Board. The setup was made of A HAMEG RF the flexibility to customize protocols at theMACand network
synthesizer HM8135, an intermediate node which is made layer; files are in XML and Python and do not support other
up of the P1110 Evaluation Board, and a modified MICA2 low-level languages like C and C++ which is used in most
mote powered from the 50mF supercapacitor on the board simulators and test beds. Interfacing PASESwith testbed tools
and has a 6.1 dBi antenna for transmitting energy in the form like what is done in COOJA is not possible and PASES does
of data packets to the end node (P1110 Evaluation Board). not also support increasing number of sensor nodes being
This setup is not automated and requires reconfigurations added at runtime.
each time the nodes position or topology changes. It is also
not scalable and is limited to two hops with modified sensor PredictionModels for Energy Harvesting.The unpredictability
nodes. For efficient energy transfer, cross-layer support for of energy due to the continuous supply of energy harvesting
energy transfer comprising the MAC, link, and application sources to predictive models for energy harvesting that
layers is critical for the implementation of wireless energy depend on the residual of energy in the network based on
transfer in WSN. There is lack of hardware designed to the availability of the energy source is difficult due to the
support energy transfer and the lack of optimal energy-aware unpredictability of energy sources. Routing protocol design
routing protocols that consider the concurrent transfer of is a challenge due to the fluctuation of the available energy
energy and data in a network. in the network which affects which nodes will be awake at
For charging nodes in an entire network, specialized every point in time to receive broadcast packets. This makes
devices such asmobile chargers or robots have been designed, broadcasting not suitable for WSN with energy harvesting
with shortest path algorithms and optimal paths developed [5].The use of energy-aware duty cycling algorithms becomes
for easy charging, but these specific nodes increase the overall a challenge if the energy on the nodes is dependent on the
cost of energy transfer on test beds. The introduction of harvested energy. This could create erratic sleep/wake-up
specialized nodes for energy harvesting and transfer called cycles since the residual energy may not be known a priori
energy transmitters also increases the cost of implementation. [121].
Sensor nodes due to their usage and places of deployment are
expected to be smaller in size; this becomes a challenge when Inductive Coupling. Inductive coupling has been revised as a
antennas for energy transmission and reception must both technique for wireless energy transfer to handle its limitation
be attached to common nodes for receiving and transmitting issues such as alignment and distance that are critical to
energy. their deployment and implementation. A strongly coupled
inductive resonance coupling technique is introduced by [56]
Designing Energy Transfer Models in Current Simulators. and has become a viable means of energy transfer. The chal-
Simulation tools of wireless sensor networks currently lack lenge is the health implications of inductive resonance in the
features that support useful energy models for energy har- human environment due to constant exposure to radiations
vesting from renewable and sustainable energy sources [34]. and the discomfort when used in humans [122]. Inductive
There is a need to either develop energy models in existing coupling works within few centimeters, and therefore the
simulation tools for energy harvesting and monitoring or distance of operation is limited. Scalability of transmission
develop energy modeling simulators for wireless sensor using inductive coupling is still a challenge since nodes must
networks. NS-2 energy model comprises the radio energy be tuned to avoid interference due to mutual coupling effect
model parameters and allows a user to set the initial energy [123]. Since inductive coupling requires alignment of nodes,
on the mote but does not have models for energy harvesting it is a challenge to design nodes with mobility.
or the ability to transfer energy from node to node. NS-
3 has an energy model which has models for the device 6.2. Future Research Directions. Current research on model-
energy model, the fundamental energy source (which is ing energy transfer is showing positive directions for single
usually the Li-ion battery but can allow formodificationswith hop and multihop energy transfer. There is the need to
Wireless Communications and Mobile Computing 19
develop or improve existing simulation tools to support 7. Conclusions
energy transfer.
The role wireless sensor networks play in monitoring human
Single Energy Transmitters. The possibility of single energy activities in the last decade cannot be underestimated. Over
transmitters transferring energy to multiple receivers simul- the years, the introduction of energy management schemes
taneously with multihop energy transfer with minimum that seek to prolong the lifetime of the sensor node and
charge and discharge losses is still open for research. Current the overall network has been proposed, but the amount of
research proposes using multiple transmitters to simultane- energy required by the sensors to be operational all the time
ously charge nodes in large networks. But due to problem of remains a challenge. In this paper, we have provided the trio
signal interference with multiple chargers, there is a limit to energy management scheme that when fully implemented
the number that could be used in a network.The possibility of will keep the network alive forever. We first discussed the
using a single energy transmitter that continuously receives broad categorization of energy harvesting technologies and
energy through energy transfer could be a huge solution if techniques and followed the discussion with the current
multihop energy transfer is explored. energy transfer techniques and finally the approaches for
conserving energy. Although there is an extensive work on
Extending Recent Simulation and Emulation Tools. Current each of these management schemes, there are still several
simulation and emulation tools on the market (both com- other challenges that need to be addressed by the research
mercial and open source) lack the full capability to test and community for effective implementation of the trio schemes.
evaluate new energy harvesting and transfer applications
and protocols in WSN. For example, the current simulators Conflicts of Interest
and emulators used for modeling WSN applications such as
Network Simulator 3 (NS-3) [88], Castalia Simulator [85], The authors declare there are no conflicts of interest regarding
EKHO [66], OMNeT++ [86], COOJA [87], and PASES [67] this paper publication.
do not reflect the network behavior accurately. Most of
these simulators do not include models for energy trans- References
fer which makes performance evaluation difficult. Network
Simulator three (NS-3) was recently extended to include [1] S. Soro and W. B. Heinzelman, “Cluster head election tech-
two energy harvesting models [68]. The first model (i.e., niques for coverage preservation in wireless sensor networks,”
the basic energy harvester) uses a generic random vari- Ad Hoc Networks, vol. 7, no. 5, pp. 955–972, 2009.
able to provide energy to the harvester. The second model [2] C. Wang, J. Li, Y. Yang, and F. Ye, “Combining solar energy
recharges the energy sourcewith datasets based on real values harvesting with wireless charging for hybrid wireless sensor
obtained from solar panel. Development of simulation and networks,” IEEE Transactions on Mobile Computing, vol. 17, no.
emulation environments that support all aspects of energy 3, pp. 560–576, 2017.
harvesting and transfer is still an open research and will [3] G. Anastasi, M. Conti, M. Di Francesco, and A. Passarella,“Energy conservation in wireless sensor networks: a survey,”Ad
be a valuable tool to evaluate the proposed energy transfer Hoc Networks, vol. 7, no. 3, pp. 537–568, 2009.
techniques. [4] I. F. Akyildiz, W. Su, Y. Sankarasubramaniam, and E. Cayirci,
“Wireless sensor networks: a survey,” Computer Networks, vol.
Experimental Demonstration of Multihop Energy Transfer. In 38, no. 4, pp. 393–422, 2002.
WSN, the available energy harvested from the energy source [5] W. K. G. Seah, Z. A. Eu, and H.-P. Tan, “Wireless sensor net-
may have practical implementation or simulation ofmultihop works powered by ambient energy harvesting (WSN-HEAP)—
energy transfer that reduces the charge and discharge losses survey and challenges,” in Proceedings of the 1st International
with better transfer efficiencies is still an open research. For Conference on Wireless Communication, Vehicular Technology,
charging nodes in an entire network, specialized devices Information Theory and Aerospace & Electronic Systems Tech-
such as mobile chargers or robots have been designed, where nology (Wireless VITAE ’09), pp. 1–5, Aalborg, Denmark, May
shortest path algorithms and optimal paths are developed 2009.
for easy charging, but these specialized nodes increase the [6] L. J. Chien, M. Drieberg, P. Sebastian, and L. H. Hiung, “A
overall cost of energy transfer on test beds. The introduction simple solar energy harvester for wireless sensor networks,” in
of specialized nodes for energy harvesting and transfer called Proceedings of the 6th International Conference on Intelligent and
energy transmitters also increases the cost of implementation. Advanced Systems (ICIAS ’16), pp. 1–6, August 2016.
Sensor nodes due to their usage and places of deployment are [7] S. Soro and W. B. Heinzelman, “Prolonging the lifetime of
expected to be smaller in size; this becomes a challenge when wireless sensor networks via unequal clustering,” in Proceedings
antennas for energy transmission and reception must both of the 19th IEEE International Parallel andDistributed ProcessingSymposium (IPDPS ’05), pp. 236–243, Washington, DC, USA,
be attached to ordinary nodes for receiving and transmitting April 2005.
energy. The need to design special nodes that could have [8] L. Xie, Y. Shi, Y. T. Hou, and A. Lou, “Wireless power transfer
contained both transmitter and receiver antennas on small and applications to sensor networks,” IEEE Wireless Communi-
sized nodeswill enable the efficient implementation of energy cations Magazine, vol. 20, no. 4, pp. 140–145, 2013.
transfer. There is also the need to develop optimal energy [9] T. Rault, A. Bouabdallah, and Y. Challal, “Energy efficiency
routing protocols to support multihop energy transfer that in wireless sensor networks: a top-down survey,” Computer
have the MAC and link layer support. Networks, vol. 67, pp. 104–122, 2014.
20 Wireless Communications and Mobile Computing
[10] Y. Chen and Q. Zhao, “On the lifetime of wireless sensor [26] M. di Francesco, S. K. Das, and G. Anastasi, “Data collection in
networks,” IEEE Communications Letters, vol. 9, no. 11, pp. 976– wireless sensor networks with mobile elements: a survey,”ACM
978, 2005. Transactions on Sensor Networks, vol. 8, no. 1, article 7, 2011.
[11] I. Dietrich and F. Dressler, “On the lifetime of wireless sensor [27] C.Alippi, G. Anastasi,M. Di Francesco, andM. Roveri, “Energy
networks,” ACM Transactions on Sensor Networks, vol. 5, no. 1, management in wireless sensor networks with energy-hungry
article 5, 2009. sensors,” IEEE Instrumentation & Measurement Magazine, vol.
[12] J. Ren, Y. Zhang, K. Zhang, A. Liu, J. Chen, and X. S. 12, no. 2, pp. 16–23, 2009.
Shen, “Lifetime and Energy Hole Evolution Analysis in Data- [28] M. A. Razzaque and S. Dobson, “Energy-efficient sensing in
Gathering Wireless Sensor Networks,” IEEE Transactions on wireless sensor networks using compressed sensing,” Sensors,
Industrial Informatics, vol. 12, no. 2, pp. 788–800, 2016. vol. 14, no. 2, pp. 2822–2859, 2014.
[13] I. F. Akyildiz, W. Su, Y. Sankarasubramaniam, and E. Cayirci, “A [29] V. Raghunathan, S. Ganeriwal, and M. Srivastava, “Emerging
survey on sensor networks,” IEEE Communications Magazine, techniques for long lived wireless sensor networks,” IEEE
vol. 40, no. 8, pp. 102–105, 2002. Communications Magazine, vol. 44, no. 4, pp. 108–114, 2006.
[14] T. Soyata, L. Copeland, and W. Heinzelman, “RF energy [30] Y. C. Eldar and G. Kutyniok, Compressed Sensing: Theory and
harvesting for embedded systems: a survey of tradeoffs and Applications, Cambridge University Press, 2012.
methodology,” IEEE Circuits and Systems Magazine, vol. 16, no.
1, pp. 22–57, 2016. [31] E. Ever, R. Luchmun, L. Mostarda, A. Navarra, and P. Shah,
[15] F. K. Shaikh and S. Zeadally, “Energy harvesting in wireless “UHEED: an unequal clustering algorithm for wireless sensor
sensor networks: a comprehensive review,” Renewable & Sus- networks,” in Proceedings of the 1st International Conference
tainable Energy Reviews, vol. 55, pp. 1041–1054, 2016. on Sensor Networks (SENSORNETS ’12), pp. 185–193, February2012.
[16] M. Y. Naderi, K. R. Chowdhury, S. Basagni, W. Heinzelman, S.
De, and S. Jana, “Experimental study of concurrent data and [32] A. Kansal, J. Hsu, S. Zahedi, and M. B. Srivastava, “Power
wireless energy transfer for sensor networks,” in Proceedings of management in energy harvesting sensor networks,” ACM
the IEEEGlobal Communications Conference (GLOBECOM ’14), Transactions on Embedded Computing Systems, vol. 6, no. 4,
pp. 2543–2549, December 2014. article 32, 2007.
[17] R. J. M. Vullers, R. V. Schaijk, H. J. Visser, J. Penders, and [33] E. D. Dunlop, L. Wald, and M. Suri, Solar Energy Resource
C. Hoof, “Energy harvesting for autonomous wireless sensor Management for Electricity Generation from Local Level to
networks,” IEEE Journal of Solid-State Circuits, vol. 2, no. 2, pp. Global Scale, Nova Science Publishers Inc., 2006.
29–38, 2010. [34] F. Akhtar and M. H. Rehmani, “Energy replenishment using
[18] R. Du, C. Fischione, and M. Xiao, “Joint node deployment renewable and traditional energy resources for sustainable
and wireless energy transfer scheduling for immortal sensor wireless sensor networks: a review,” Renewable & Sustainable
networks,” in Proceedings of the 15th International Symposium Energy Reviews, vol. 45, pp. 769–784, 2015.
on Modeling and Optimization in Mobile, Ad Hoc, and Wireless [35] P. T. V. Bhuvaneswari, R. Balakumar, V. Vaidehi, and P.
Networks (WiOpt ’17), pp. 1–8, Paris, France, May 2017. Balamuralidhar, “Solar energy harvesting for wireless sensor
[19] L. Xie, Y. Shi, Y. T. Hou, and H. D. Sherali, “Making sensor networks,” in Proceedings of the 1st International Conference
networks immortal: an energy-renewal approach with wireless on Computational Intelligence, Communication Systems and
power transfer,” IEEE/ACMTransactions onNetworking, vol. 20, Networks (CICSYN ’09), pp. 57–61, July 2009.
no. 6, pp. 1748–1761, 2012. [36] P. Technologies, “Documentation,” Powercast, Wireless Power
[20] L. Xie, Y. Shi, Y. T. Hou,W. Lou, H. D. Sherali, and S. F. Midkiff, for a Wireless World, 2018, http://www.powercastco.com/docu-
“On renewable sensor networks with wireless energy transfer: mentation/.
the multi-node case,” in Proceedings of the 9th Annual IEEE [37] K. S. Adu-Manu, N. Adam, C. Tapparello, H. Ayatollahi, and
Communications Society Conference on Sensor,Mesh andAdHoc W. Heinzelman, “Energy-harvesting wireless sensor networks
Communications and Networks (SECON ’12), pp. 10–18, June (EH-WSNs): a review,” ACM Transactions on Sensor Networks
2012. (TOSN), vol. 14, no. 2, p. 10, 2018.
[21] C. Zhu, K. Liu, C. Yu, R. Ma, and H. Cheng, “Simulation [38] T. J. Kazmierski and S. Beeby, Energy Harvesting Systems,
and experimental analysis on wireless energy transfer based on Springer, 2014.
magnetic resonances,” in Proceedings of the Vehicle Power and
Propulsion Conference (VPPC ’08), pp. 1–4, 2008. [39] C. Ó. Mathúna, T. O’Donnell, R. V. Martinez-Catala, J. Rohan,
[22] M. K. Watfa, H. AlHassanieh, and S. Selman, “Multi-hop wire- and B. O’Flynn, “Energy scavenging for long-term deployable
less energy transfer in WSNs,” IEEE Communications Letters, wireless sensor networks,” Talanta, vol. 75, no. 3, pp. 613–623,
vol. 15, no. 12, pp. 1275–1277, 2011. 2008.
[23] A. A. Babayo, M. H. Anisi, and I. Ali, “A Review on energy [40] S. Roundy, P. K. Wright, and J. M. Rabaey, “Energy scavenging
management schemes in energy harvesting wireless sensor for wireless sensor networks,” Norwell, 2003.
networks,” Renewable & Sustainable Energy Reviews, vol. 76, pp. [41] D. M. Rowe, “Conversion efficiency and figure-of-merit,” in
1176–1184, 2017. CRC Handbook of Thermoelectrics, p. 31, CRC Press, 1995.
[24] R. Doost-Mohammady and K. R. Chowdhury, “Transforming [42] I. Stark, “Invitedtalk: Thermal energy harvesting with thermo
healthcare and medical telemetry through cognitive radio life,” in Proceedings of the International Workshop Wearable and
networks,” IEEEWireless CommunicationsMagazine, vol. 19, no. Implantable Body Sensor Networks (BSN ’06), pp. 19–22, 2006.
4, pp. 67–73, 2012. [43] A. S. Aricò, P. Bruce, B. Scrosati, J.-M. Tarascon, and W. van
[25] A. Khelil, F. K. Shaikh, P. Szczytowski, B. Ayari, and N. Suri, Schalkwijk, “Nanostructured materials for advanced energy
“Map-based design for autonomic wireless sensor networks,” in conversion and storage devices,” Nature Materials, vol. 4, pp.
Autonomic Communication, pp. 309–326, Springer, 2009. 366–377, 2005.
Wireless Communications and Mobile Computing 21
[44] F. I. Simjee and P. H. Chou, “Efficient charging of superca- Transactions on Antennas and Propagation, vol. 61, no. 3, pp.
pacitors for extended lifetime of wireless sensor nodes,” IEEE 1378–1384, 2013.
Transactions on Power Electronics, vol. 23, no. 3, pp. 1526–1536, [60] F. Engmann, J.-D. Abdulai, and J. Q. Azasoo, “Enhancing
2008. the reliability of wsn through wireless energy transfer,” in
[45] T. H. Ng and W. H. Liao, “Sensitivity analysis and energy Proceedings of the International Conference on Computational
harvesting for a self-powered piezoelectric sensor,” Journal of Science and Its Applications, pp. 610–618, Springer, 2016.
Intelligent Material Systems and Structures, vol. 16, no. 10, pp. [61] J. Li, K. Li, andW. Zhu, “Improving sensing coverage of wireless
785–797, 2005. sensor networks by employing mobile robots,” in Proceedings of
[46] N. S. Shenck and J. A. Paradiso, “Energy scavenging with shoe- the IEEE International Conference on Robotics and Biomimetics
mounted piezoelectrics,” IEEE Micro, vol. 21, no. 3, pp. 30–42, (ROBIO ’07), pp. 899–903, December 2007.
2001. [62] B. L. Cannon and D. D. Stancil, “Magnetic resonant coupling as
[47] G. K. Ottman, H. F. Hofmann, A. C. Bhatt, and G. A. Lesieutre, a potential means for wireless power transfer to multiple small
“Adaptive piezoelectric energy harvesting circuit for wireless receivers,” IEEE Transactions on Power Electronics, vol. 24, no. 7,
remote power supply,” IEEE Transactions on Power Electronics, pp. 1819–1825, 2009.
vol. 17, no. 5, pp. 669–676, 2002. [63] M. Erol-Kantarci and H. T. Mouftah, “Suresense: sustainable
[48] H. A. Sodano, D. J. Inman, and G. Park, “Comparison of wireless rechargeable sensor networks for the smart grid,” IEEE
piezoelectric energy harvesting devices for recharging batter- Wireless Communications Magazine, vol. 19, no. 3, pp. 30–36,
ies,” Journal of Intelligent Material Systems and Structures, vol. 2012.
16, no. 10, pp. 799–807, 2005. [64] K. S. Adu-Manu, C. Tapparello, W. Heinzelman, F. A. Katsriku,
[49] M. J. Guan and W. H. Liao, “Characteristics of energy storage and J.-D. Abdulai, “Water quality monitoring using wireless
devices in piezoelectric energy harvesting systems,” Journal of sensor networks:Current trends and future researchdirections,”
IntelligentMaterial Systems and Structures, vol. 19, no. 6, pp. 671– ACM Transactions on Sensor Networks, vol. 13, no. 1, 2017.
680, 2008. [65] D. Mishra, S. De, S. Jana, S. Basagni, K. Chowdhury, and W.
[50] V. Raghunathan, A. Kansal, J. Hsu, J. Friedman, and M. Heinzelman, “Smart RF energy harvesting communications:
Srivastava, “Design considerations for solar energy harvesting Challenges and opportunities,” IEEE Communications Maga-
wireless embedded systems,” in Proceedings of the 4th Interna- zine, vol. 53, no. 4, pp. 70–78, 2015.
tional Symposium on Information Processing in Sensor Networks [66] J. Hester, T. Scott, and J. Sorber, “Ekho: realistic and repeatable
(IPSN ’05), pp. 457–462, April 2005. experimentation for tiny energy-harvesting sensors,” in Pro-
[51] B. Buchli, F. Sutton, J. Beutel, and L. Thiele, “Dynamic power ceedings of the 12th ACM Conference on Embedded Networked
management for long-term energy neutral operation of solar Sensor Systems (SenSys ’14), pp. 1–15, New York, NY, USA,
energy harvesting systems,” in Proceedings of the 12th ACM November 2014.
Conference, pp. 31–45, November 2014. [67] I. Minakov and R. Passerone, “PASES: an energy-aware design
[52] S. Peng, T. Wang, and C. P. Low, “Energy neutral clustering for space exploration framework for wireless sensor networks,”
energy harvesting wireless sensors networks,”AdHoc Networks, Journal of Systems Architecture, vol. 59, no. 8, pp. 626–642, 2013.
vol. 28, pp. 1–16, 2015. [68] C. Tapparello, H. Ayatollahi, and W. Heinzelman, “Energy
[53] S. Baghaee, S. Chamanian, H. Ulusan, O. Zorlu, E. Uysal- harvesting framework for network simulator 3 (ns-3),” in
Biyikoglu, and H. Kulah, “Demonstration of energy-neutral Proceedings of the 2nd InternationalWorkshop on EnergyNeutral
operation on aWSN testbed using vibration energy harvesting,” Sensing Systems (ENSsys ’14), pp. 37–42, Memphis, TN, USA,
in Proceedings of the 20th European Wireless Conference (EW November 2014.
’14), pp. 47–52, May 2014. [69] H. P. Dai, X. B. Wu, L. J. Xu, G. Chen, and S. Lin, “Using mini-
[54] C. M. Vigorito, D. Ganesan, and A. G. Barto, “Adaptive control mummobile chargers to keep large-scale wireless rechargeable
of duty cycling in energy-harvesting wireless sensor networks,” sensor networks running forever,” in Proceedings of the IEEE
in Proceedings of the 4th Annual IEEE Communications Society 22nd International Conference on Computer Communication
Conference on Sensor, Mesh and Ad Hoc Communications and and Networks (ICCCN ’13), pp. 1–7, August 2013.
Networks (SECON ’07), pp. 21–30, IEEE, San Diego, Calif, USA, [70] W. Xu, W. Liang, X. Lin, and G. Mao, “Efficient scheduling of
June 2007. multiple mobile chargers for wireless sensor networks,” IEEE
[55] Y. Shi, L. Xie, Y. T. Hou, andH.D. Sherali, “On renewable sensor Transactions on Vehicular Technology, vol. 65, no. 9, pp. 7670–
networks with wireless energy transfer,” in Proceedings of the 7683, 2016.
IEEE INFOCOM ’11, pp. 1350–1358, Shanghai, China, April 2011. [71] Y. Peng, Z. Li, W. Zhang, and D. Qiao, “Prolonging sensor
[56] A. Kurs, A. Karalis, R. Moffatt, J. D. Joannopoulos, P. Fisher, network lifetime through wireless charging,” in Proceedings of
and M. Soljacic, “Wireless power transfer via strongly coupled the 31st IEEE Real-Time Systems Symposium (RTSS ’10), pp. 129–
magnetic resonances,” Science, vol. 317, no. 5834, pp. 83–86, 139, IEEE, December 2010.
2007. [72] W. Yao, M. Li, andM.-Y.Wu, “Inductive charging with multiple
[57] X.Wei, Z.Wang, andH.Dai, “A critical review ofwireless power charger nodes in wireless sensor networks,” in Proceedings of the
transfer via strongly coupled magnetic resonances,” Energies, Asia-Pacific Web Conference, pp. 262–270, Springer, 2006.
vol. 7, no. 7, pp. 4316–4341, 2014. [73] A. Madhja, S. Nikoletseas, and T. P. Raptis, “Hierarchical,
[58] F. Musavi and W. Eberle, “Overview of wireless power transfer collaborative wireless energy transfer in sensor networks with
technologies for electric vehicle battery charging,” IET Power multiple Mobile Chargers,” Computer Networks, vol. 97, pp. 98–
Electronics, vol. 7, no. 1, pp. 60–66, 2014. 112, 2016.
[59] O. Jonah and S. V. Georgakopoulos, “Wireless power transfer [74] H. Ayatollahi, C. Tapparello, andW.Heinzelman, “Transmitter-
in concrete via strongly coupled magnetic resonance,” IEEE receiver energy efficiency: a trade-off in MIMO wireless sensor
22 Wireless Communications and Mobile Computing
networks,” in Proceedings of the IEEE Wireless Communications [91] S. Hussain, A. Azim, and J. H. Park, “Energy efficient virtual
and Networking Conference (WCNC ’15), pp. 1476–1481, March MIMO communication for wireless sensor networks,” Telecom-
2015. munication Systems, vol. 42, no. 1-2, pp. 139–149, 2009.
[75] A. B. Constantine, “Microstrip Antennas,” in Antenna Theory: [92] L. Lu, G. Y. Li, A. L. Swindlehurst, A. Ashikhmin, and R. Zhang,
Analysis and Design, John Wiley & Sons, 3rd edition, 2005. “An overview of massive MIMO: benefits and challenges,” IEEE
[76] Q. Li, R. Q. Hu, Y. Qian, and G. Wu, “Cooperative communica- Journal of Selected Topics in Signal Processing, vol. 8, no. 5, pp.
tions for wireless networks: techniques and applications in LTE- 742–758, 2014.
advanced systems,” IEEE Wireless Communications Magazine, [93] A. Nosratinia, T. E. Hunter, and A. Hedayat, “Cooperative
vol. 19, no. 2, pp. 22–29, 2012. communication in wireless networks,” IEEE Communications
[77] K. Huang and E. Larsson, “Simultaneous information and Magazine, vol. 42, no. 10, pp. 74–80, 2004.
power transfer for broadband wireless systems,” IEEE Transac- [94] S. Basagni, M. Y. Naderi, C. Petrioli, and D. Spenza, “Wireless
tions on Signal Processing, vol. 61, no. 23, pp. 5972–5986, 2013. sensor networks with energy harvesting,” Mobile Ad Hoc Net-
working: Cutting Edge Directions, pp. 701–736, 2013.
[78] A. A. Nasir, X. Zhou, S. Durrani, and R. A. Kennedy, “Relaying
protocols for wireless energy harvesting and information pro- [95] I. Khemapech, A. Miller, and I. Duncan, “A survey of transmis-
cessing,” IEEETransactions onWirelessCommunications, vol. 12, sion power control in wireless sensor networks,” in Proceedings
no. 7, pp. 3622–3636, 2013. of the PGNet, pp. 15–20, 2007.
[96] S. Lin, F. Miao, J. Zhang et al., “ATPC: Adaptive transmission
[79] R. Zhang and C. K. Ho, “MIMO broadcasting for simultaneous power control for wireless sensor networks,” ACM Transactions
wireless information and power transfer,” IEEE Transactions on on Sensor Networks, vol. 12, no. 1, 2016.
Wireless Communications, vol. 12, no. 5, pp. 1989–2001, 2013. [97] K. V. Kumar and E. Baburaj, “Energy efficient transmission
[80] J. Park and B. Clerckx, “Joint wireless information and energy power control in sensor nodes of WSN SCADA systems
transfer with reduced feedback in MIMO interference chan- using cognitive fuzzy systems,” International Journal of Applied
nels,” IEEE Journal on Selected Areas in Communications, vol. Engineering Research, vol. 11, no. 2, pp. 1478–1484, 2016.
33, no. 8, pp. 1563–1577, 2015. [98] H. Cotuk, K. Bicakci, B. Tavli, and E. Uzun, “The impact of
[81] Z. Xiang and M. Tao, “Robust beamforming for wireless transmission power control strategies on lifetime of wireless
information and power transmission,” IEEE Wireless Commu- sensor networks,” IEEE Transactions on Computers, vol. 63, no.
nications Letters, vol. 1, no. 4, pp. 372–375, 2012. 11, pp. 2866–2879, 2014.
[82] X. Chen, C. Yuen, and Z. Zhang, “Wireless energy and infor- [99] A. Tripathi, S. Gupta, and B. Chourasiya, “Survey on data aggre-
mation transfer tradeoff for limited-feedback multiantenna gation techniques for wireless sensor networks,” International
systems with energy beamforming,” IEEE Transactions on Journal of Advanced Research in Computer and Communication
Vehicular Technology, vol. 63, no. 1, pp. 407–412, 2014. Engineering, vol. 3, no. 7, pp. 7366–7371, 2014.
[83] J. Park and B. Clerckx, “Joint wireless information and energy [100] L. C. Zhong, R. Shah, C. Guo, and J. Rabaey, “An ultra-low
transfer in a two-user MIMO interference channel,” IEEE power and distributed access protocol for broadband wireless
Transactions on Wireless Communications, vol. 12, no. 8, pp. sensor networks,” IEEEBroadbandWireless Summit, vol. 3, 2001.
4210–4221, 2013. [101] M. Bagaa, Y. Challal, A. Ksentini, A. Derhab, and N. Badache,
[84] J. Park and B. Clerckx, “Joint wireless information and energy “Data aggregation scheduling algorithms in wireless sensor
transfer in aK-userMIMO interference channel,” IEEETransac- networks: solutions and challenges,” IEEE Communications
tions onWireless Communications, vol. 13, no. 10, pp. 5781–5796, Surveys & Tutorials, vol. 16, no. 3, pp. 1339–1368, 2014.
2014. [102] N. Kimura and S. Latifi, “A survey on data compression in
[85] Castalia, “Castalia wireless sensor network simulator,” 2016. wireless sensor networks,” in Proceedings of the International
Conference on Information Technology: Coding and Computing
[86] F. Chen, I. Dietrich, R. German, and F. Dressler, “An energy (ITCC ’05), vol. 2, pp. 8–13, April 2005.
model for simulation studies of wireless sensor networks using
OMNeT++,” Praxis der Informationsverarbeitung und Kommu- [103] T. Srisooksai, K. Keamarungsi, P. Lamsrichan, and K. Araki,
nikation, vol. 32, no. 2, pp. 133–138, 2009. “Practical data compression in wireless sensor networks: asurvey,” Journal of Network and Computer Applications, vol. 35,
[87] H. Sundani, H. Li, V. Devabhaktuni, M. Alam, and P. Bhat- no. 1, pp. 37–59, 2012.
tacharya, “Wireless sensor network simulators a survey and [104] G. M. Dias, B. Bellalta, and S. Oechsner, “A survey about
comparisons,” International Journal of Computer Networks, vol. prediction-based data reduction in wireless sensor networks,”
2, no. 5, pp. 249–265, 2011. ACM Computing Surveys, vol. 49, no. 3, 2016.
[88] H. Wu, S. Nabar, and R. Poovendran, “An energy framework [105] C. Alippi, G. Anastasi, M. Di Francesco, and M. Roveri, “An
for the network simulator 3 (ns-3),” in Proceedings of the adaptive sampling algorithm for effective energy management
4th International ICST Conference on Simulation Tools and in wireless sensor networks with energy-hungry sensors,” IEEE
Techniques, Barcelona, Spain, March 2011. Transactions on Instrumentation and Measurement, vol. 59, no.
[89] M. K. Stojcev, M. R. Kosanovic, and L. R. Golubovic, “Power 2, pp. 335–344, 2010.
management and energy harvesting techniques for wireless [106] B. Srbinovski, M. Magno, B. O’Flynn, V. Pakrashi, and E.
sensor nodes,” in Proceedings of the 9th International Conference Popovici, “Energy aware adaptive sampling algorithm for
on Telecommunications in Modern Satellite, Cable, and Broad- energy harvesting wireless sensor networks,” in Proceedings of
casting Services (TELSIKS ’09 ), pp. 65–72, October 2009. the Sensors Applications Symposium (SAS ’15), pp. 1–6, 2015.
[90] S. Cui, A. J. Goldsmith, and A. Bahai, “Energy-efficiency of [107] Y. Yin, F. Liu, X. Zhou, andQ. Li, “An efficient data compression
MIMO and cooperativeMIMO techniques in sensor networks,” model based on spatial clustering and principal component
IEEE Journal on Selected Areas in Communications, vol. 22, no. analysis in wireless sensor networks,” Sensors, vol. 15, no. 8, pp.
6, pp. 1089–1098, 2004. 19443–19465, 2015.
Wireless Communications and Mobile Computing 23
[108] X. Cao, S. Madria, and T. Hara, “Efficient Z-order encoding [123] S. Bi, C. K. Ho, and R. Zhang, “Wireless powered commu-
based multi-modal data compression in WSNs,” in Proceedings nication: opportunities and challenges,” IEEE Communications
of the 37th IEEE International Conference on Distributed Com- Magazine, vol. 53, no. 4, pp. 117–125, 2015.
puting Systems (ICDCS ’17), pp. 2185–2192, June 2017.
[109] M. Wu, L. Tan, and N. Xiong, “Data prediction, compression,
and recovery in clusteredwireless sensor networks for environ-
mental monitoring applications,” Information Sciences, vol. 329,
pp. 800–818, 2016.
[110] B. Stojkoska, D. Solev, and D. Davcev, “Data prediction in
WSN using variable step size LMS algorithm,” in Proceedings
of the 5th International Conference on Sensor Technologies and
Applications (SENSORCOMM ’11), pp. 191–196, August 2011.
[111] W. R. Heinzelman, A. Chandrakasan, and H. Balakrishnan,
“Energy-efficient communication protocol for wireless micro-
sensor networks,” in Proceedings of the 33rd Annual Hawaii
International Conference on System Siences (HICSS ’00), p. 10,
January 2000.
[112] M. Ye, C. Li, G. Chen, and J. Wu, “EECS: an energy efficient
clustering scheme in wireless sensor networks,” in Proceedings
of the 24th IEEE International Performance, Computing, and
Communications Conference (IPCCC ’05), pp. 535–540, IEEE,
April 2005.
[113] G. V. Selvi and R. Manoharan, “A survey of energy efficient
unequal clustering algorithms for wireless sensor networks,”
International Journal of Computer Applications, vol. 79, no. 1,
2013.
[114] R. Sharma, G. Jain, and S. Gupta, “Enhanced Cluster-head
selection using round robin technique in WSN,” in Proceedings
of the International Conference on Communication Networks
(ICCN ’15), pp. 37–42, Gwalior, India, November 2015.
[115] A. A. Abbasi andM. Younis, “A survey on clustering algorithms
for wireless sensor networks,” Computer Communications, vol.
30, no. 14-15, pp. 2826–2841, 2007.
[116] C. Alippi, R. Camplani, C. Galperti, and M. Roveri, “A robust,
adaptive, solar-powered WSN framework for aquatic environ-
mental monitoring,” IEEE Sensors Journal, vol. 11, no. 1, pp. 45–
55, 2011.
[117] G. Chen, C. Li, M. Ye, and J. Wu, “An unequal cluster-
based routing protocol in wireless sensor networks,” Wireless
Networks, vol. 15, no. 2, pp. 193–207, 2009.
[118] H. Bagci and A. Yazici, “An energy aware fuzzy approach to
unequal clustering in wireless sensor networks,” Applied Soft
Computing, vol. 13, no. 4, pp. 1741–1749, 2013.
[119] K. Kaushik, D. Mishra, S. De et al., “Experimental demonstra-
tion of multi-hop RF energy transfer,” in Proceedings of the
IEEE 24th Annual International Symposium on Personal, Indoor,
and Mobile Radio Communications (PIMRC ’13), pp. 538–542,
September 2013.
[120] G. Benigno, O. Briante, andG. Ruggeri, “A sun energy harvester
model for the network simulator 3 (ns-3),” in Proceedings of the
12th Annual IEEE International Conference on Sensing, Commu-
nication, and Networking - Workshops (SECON Workshops ’15),
pp. 1–6, Seattle, WA, USA, June 2015.
[121] R. C. Carrano, D. Passos, L. C. S. Magalhaes, and C. V. N. Albu-
querque, “Survey and taxonomy of duty cycling mechanisms
in wireless sensor networks,” IEEE Communications Surveys &
Tutorials, vol. 16, no. 1, pp. 181–194, 2014.
[122] I. Mayordomo, T. Drager, P. Spies, J. Bernhard, and A. Pflaum,
“An overview of technical challenges and advances of inductive
wireless power transmission,” in Proceedings of the IEEE, vol.
101, no. 6, pp. 1302–1311, 2013.
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