Journal of Ambient Intelligence and Humanized Computing https://doi.org/10.1007/s12652-021-03683-y ORIGINAL RESEARCH A modified distance‑based energy‑aware (mDBEA) routing protocol in wireless sensor networks (WSNs) J.‑D. Abdulai1 · K. S. Adu‑Manu1  · F. A. Katsriku1 · F. Engmann2 Received: 14 January 2021 / Accepted: 21 December 2021 © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022 Abstract Wireless sensor networks (WSNs) are used to collect data and detect phenomena in a real-time environment. There is con- siderable interest in the deployment of WSNs in remote, inaccessible and inhospitable locations; such use of WSNs throws up many challenges. WSNs come with numerous advantages, yet a notable limitation is that the battery life dictates the lifetime of the sensor node. Two critical factors that determine battery lifetime are the frequency of sensor readings and the transmission range of the sensor nodes. Some energy-efficient routing protocols have been proposed and adopted for use to extend the lifetime of sensor nodes. These protocols aim at optimizing the routes in the network. Given that multi-hop routes are energy inefficient, improving the lifetime of WSNs in a multi-hop routing environment will require the use of route optimization techniques. A modified distance-based energy-aware (mDBEA) routing protocol is proposed which is efficient and capable of minimizing the energy consumption of the sensor nodes and hence, maximizing network lifetime. Our approach addresses the problem by calculating the Euclidian distance between successive nodes to determine the short- est distance that minimizes the energy required for transmission. The simulation results indicate that the mDBEA routing protocol reduced the amount of energy consumed in the network by choosing the minimum transmission distance between the source and its neighbour nodes that significantly prolonged the network's lifetime. Our greedy approach yielded about 95% Packet delivery ratio (PDR). Our next-hop and the direct-to-sink algorithms yielded about 82% PDR. Keywords Energy-aware · Network lifetime · Transmission range adjustment · Routing protocol · Wireless sensor networks 1 Introduction routing protocols are designed to improve the efficiency of the network. Typical among them is dynamic source routing River sensor networks (RSNs) operation is similar to Mobile (DSR) protocol, dynamic destination-sequenced distance- Ad-hoc Networks (MANETs), albeit with some key differ- vector routing (DSDV) protocol, temporally ordered routing ences. RSNs and MANETs are both distributed wireless algorithm (TORA) and ad-hoc on-demand distance vector networks in which data packets may be routed through routing (AODV) protocol. MANET routing protocols are intermediate nodes. Each network supports multi-hop rout- used to save bandwidth and conserve the remaining battery ing algorithms. RSNs and MANETs are both energy-con- power in large and dense mobile networks. AODV sends strained, and their primary concern relates to minimizing route error (RERR) messages whenever a link breaks and re- energy consumption. RSNs and MANETs are constrained by establishes a new route after the old route breaks. A broken limited bandwidth and physical security, whereas RSNs are link or route disrupts communication, rendering the network constrained by data reliability, cost, transmission range, data inefficient and affecting its lifetime (Adu-Manu et al. 2019, rate, latency, physical size, and data security. Most MANET 2020). MANET routing protocols are mainly intended for data K. S. Adu-Manu transmission and aggregation between the source and desti-* ksadu-manu@ug.edu.gh nation nodes. Examples include low energy adaptive cluster- ing hierarchy (LEACH), power-efficient gathering in sensor 1 Department of Computer Science, University of Ghana, information systems (PEGA-SIS), threshold sensitive energy Accra, Ghana efficient sensor network protocol (TEEN), greedy perimeter 2 School of Technology, Ghana Institute of Management stateless routing (GPSR) and location aided routing (LAR). and Public Administration, Accra, Ghana Vol.:(012 3456789) J.-D. Abdulai et al. These protocols route data packets by taking advantage of location information of the sensor nodes and managing energy efficiently through multi-hop communication. Rout- ing protocols in WSNs and MANETs ensure quality com- munication among nodes to increase the network lifetime. The network lifetime in WSN depends on the available bat- tery power of each sensor node in the network. Node and network lifetime maximization in WSNs are of great concern because the nodes have limited energy capacity for extended operation (Rana and Raja 2013). MANET protocols adapted for use in river sensor networks (RSNs) require some modi- fication due to the high level of mobility of sensor nodes in RSNs, leading to breaks in the communication links. The modified MANET routing protocols implemented in RSNs help improve packet delivery between the source and the destination nodes whenever the intermediate nodes are out of range of their neighbours. In freshwater sources, such as rivers, contaminants flow Fig. 1 RSN with mobile sensors and static sink nodes (Scenario 1) along the river path; hence, sensor nodes must be deployed to cover a broader area to detect pollutants along the river path or move along with the impurity to keep track of the level of pollution. In general, sensor networks for freshwater monitoring may be deployed in one of the following three (3) scenarios. These are (1) pre-installed stationary sensor nodes in the river and along its banks (i.e., stationary sensor nodes); (2) the nodes are allowed to float freely along the river path (i.e., mobile sensor nodes) and (3) a combination of both stationary and mobile sensor nodes. In all cases, fixed or mobile sink nodes may be used to collect the data from the sensor nodes to ensure timely, accurate and reliable contaminant event detection. In scenarios 2 and 3, the sink node must be activated and reachable by the mobile nodes. Routing protocols in RSNs are classified based on the net- work structure, the process of route discovery, the operation and route selection (Adu-Manu 2019). Figures 1, 2, 3 and 4 depict the deployment architecture of a typical RSN. In Fig. 1, the mobile sensors float along with the river current. They sense water quality parameters and transmit data to the stationary nodes positioned along the river bank as they move. The use of mobile sensor nodes Fig. 2 RSN with mobile sensors and mobile sink nodes (Scenario 2) will support the modelling of the spread of contaminants in the river over a period. The stationary nodes, which serve may depend on the river type (i.e., deep but slow-moving as sink nodes, perform in-node data analysis and transmit rivers, deep but swift-moving rivers, and shallow but swift- the sensed water parameters to the appropriate users. The moving rivers). In slow, deep rivers, the velocity of the river mobile nodes may also provide additional information about is relatively low. their path while moving, such as their current velocity, direc- The mobile sink and sensor nodes move along the river tion, comprehensive coverage, and high connectivity among path and their movement is dependent on the river's current. the sensor nodes with minimum energy utilization in the In swift, shallow and deep rivers, the movement is erratic. network. In Fig. 2, the mobile sensors and mobile sinks float Static WSNs for river network monitoring is designed for along with the river current, and as they move, the mobile use when it is required to know the state of the river at a sensors detect pollutants and transmit the data to mobile particular location. Static nodes deployed in specific areas in sinks floating in the river. This kind of network improves the a river monitor only that region, leaving the other areas unat- coverage/sensing area. The number of mobile sinks deployed tended and uncovered (Du et al. 2013; Khoufi et al. 2017). 1 3 A modified distance‑based energy‑aware (mDBEA) routing protocol in wireless sensor networks… spreading in the river body, as illustrated in Fig. 3. Hence, a combination of mobile nodes may be implemented to resolve this problem, as shown in Fig. 3. Figure 4 illustrates static sinks with mobile nodes to monitor the state of the river. In river sensor networks (RSNs), sink movement may be arbitrary. The mobile sink may find itself: (1) trailing in the sensor network field on its mobility path (i.e., creating the trailing sink problem), (2) moving among the mobile sensor nodes (i.e., creating the near sink problem), and (3) moving ahead of all other mobile nodes due to high velocity and variability in mobility (i.e., creating the far sink problem). The mobile sink node's velocity is slower in the trailing sink node problem and lags behind the source nodes (see Fig. 5). The near-sink node problem occurs when the mobile sink node finds itself within close range of the source nodes as they move along the river path, as illustrated in Fig. 6. The near-sink node problem also creates energy hole problems. Fig. 3 RSN with static nodes and static sinks (Scenario 3) This arises because the sensor nodes near the sink are likely to use up their energy compared to those far from the sink node. Under such conditions, attempts by source nodes to With static WSNs, it is difficult to know how the pollutant is reach a mobile sink and transmit data packets will require high transmission power utilization. The far-sink node problem occurs when a mobile sink node has a higher velocity than the other nodes in the mobile sensor network (see Fig. 7). The sink node is thus, far removed from other nodes and vice versa. In the far-sink and trailing-sink problems, there is an increase in the amount of energy expended by sensor nodes in the network in reach- ing the sink node. The topology of the network changes fre- quently depending on the temporal dynamics of the water body. Generally, the standard AODV routing protocol is designed for mobile adhoc networks (MANETs). The AODV routing protocol is also widely used in WSNs. It is an on- demand protocol, meaning that it builds routes between nodes only as required by source nodes. It maintains these routes as long as the source nodes need them. It broadcasts route requests (RREQs) throughout the network for the num- ber of nodes deployed. The continuous broadcast of RREQs Fig. 4 RSN with static sensors and mobile sinks (Scenario 3) results in constant collision and energy consumption. It is essential to pay attention to the amount of energy utilized during the transmission of RREQs to reduce the power con- sumed by the nodes. The routing protocol proposed in this Fig. 5 The trailing sink problem in WSN for RSNs 1 3 J.-D. Abdulai et al. Fig. 6 The near sink problem in WSN for RSNs Fig. 7 The far sink problem in WSN for RSNs paper was modelled based on the AODV protocol archi- in sensor networks based on transmission range adjustment. tecture, which allows a sensor node to obtain the position The network model and the problem formulation are also information of neighbours periodically by sending "Hello" described in Sect. 3. Section 4 presents the proposed energy- messages to neighbours one-hop away along already estab- aware routing protocol capable of adjusting the transmis- lished routes/paths. Each node builds a location table at the sion range during data transmission. The simulation results beginning of the network and stores the location information showing the performance of the sensor network modelled in of other nodes in the location table. The neighbour nodes this paper is described in Sect. 5. Finally, Sect. 6 provides a update their location table by using the current position conclusion to the paper. received from the nodes. The entire location table is trans- mitted to update all the tables in the entry. The use of the global information of the topology allows sharing of loca- 2 Related works tion information. The RREQ is a broadcast packet, and the coordinates (x, y) are added to the fields of the RREQ. The main task of a sensor node is to sense a phenomenon The position information acquired is used to compute of interest. The sensed data packet is transmitted to a sink the Euclidean distance to determine the shortest distance node for onward forwarding to a central repository for analy- required to minimize the energy consumed by sensor nodes sis. This process of sensing and transmitting data from sen- to transmit the data packet to the next-hop neighbour. The sor nodes to sink node leads to the depletion of the limited approach proposed requires the dynamic adjustment of the energy on the batteries of the sensor nodes. Eventually, the node's transmission range to ensure optimal energy usage batteries ran out of energy, and the sensor node dies, creat- during transmission, thus, maximizing the lifetime of the ing coverage and energy holes in the sensor network. Dif- node and the overall network lifetime. In RSNs, when con- ferent approaches have been proposed to overcome the rate tamination is sensed in a given region of interest (ROI), at which the sensor node's energy depletes to prolong the measurements are made. The data is aggregated and routed sensor network lifetime (Adu-Manu et al. 2017; Jabbar et al. towards the sink node. The event-detection node may, in 2018). One such approach is the design of algorithms tar- each case, have to adjust its' transmission range and use the geted at energy savings at the node and the network levels. optimal amount of energy for the transmission. This ensures The performance of routing protocols is measured based that the nodes' lifetime and the network lifetime are extended on the following parameters: data delivery, scalability, as much as possible. energy consumption, data aggregation, and other quality of The focus of this paper is to propose an algorithm to service parameters. In most wireless network applications, maximize the sensor node/network lifetime by controlling sensor nodes are deployed randomly and are static after the amount of energy a node expends on transmitting/receiv- deployment. In RSN, it is a challenge to predetermine the ing data packets in mobile networks with particular refer- position of the sensor nodes. Given the critical role of trans- ence to freshwater monitoring applications. Figure 8 illus- mission range on energy expended during transmission, it is trates nodes in a RSN with live and dead nodes. The rest of essential to take into account the position of the nodes. Sev- the paper is organized as follows: Sect. 2 reviews related eral approaches proposed in the literature proposed to pro- works. Section 3 focuses on lifetime maximization methods long network lifetime based on the distance between nodes 1 3 A modified distance‑based energy‑aware (mDBEA) routing protocol in wireless sensor networks… Fig. 8 RSN with dead and live sensor nodes (Chauhan and Soni 2020; Haseeb et al. 2019a, b; Sharma consume energy at the same rate, so the network as a whole et al. 2019). Some of these algorithms use a clustering model could fail after a while if the power supply runs out. to minimize the energy consumed in the network by peri- In Mothku and Rout (2019), the authors proposed a reli- odically selecting the cluster heads and optimizing the size able energy-aware routing (RER) protocol in a delay-tolerant of the clusters. Notable among clustering algorithms are mobile sensor networks environment. The work focused on fuzzy modelling based energy-aware clustering (FMBEAC) improving the data delivery ratio and data reliability whilst (Sharma et al. 2019), mobile sink-based energy-efficient reducing the energy consumed by the sensor nodes. The cluster head selection (MSBE) (Chauhan and Soni 2020), authors adopted two approaches. The first approach was to and reliable cluster-based energy-aware routing (RCBEA) use a hop-by-hop retransmission acknowledgement mech- (Haseeb et al. 2019a, b). Besides these clustering algorithms, anism. The second approach was to introduce a reliable other proposed approaches include adaptive fuzzy-based and energy cost based on distance (RECB) in RER to prolong delay-aware routing (AFBDA) (Mothku and Rout 2019), net- the network lifetime. The problem with their strategy is that work structure based energy-aware routing (NSBEA) (Gong the sensor nodes needed to know what their neighbours’ 2019), secret sharing-based energy-aware multi-hop routing energy costs were. Their method adds to the protocol's time (SSBEA) (Haseeb et al. 2019a, b), distance-based energy and spatial complexity. When forwarding packets in the net- routing (DBEAR) (Wang et al. 2010), energy-aware in delay- work, there are also higher overheads. tolerant routing protocol (EADT) (Mothku and Rout 2019), In Liu et al. (2009), the authors proposed a distributed and reliable energy-aware routing (RER) (Liu et al. 2009). and energy-aware routing protocol (EAP) to gather data in Distance-based energy-aware routing (DEAR) was pro- WSNs. In their approach, the sensor nodes are grouped into posed in (Wang et al. 2010) to ensure energy efficiency in clusters and a routing tree built among the cluster heads for WSNs and to balance energy in the network using traffic energy-saving during data communication. In EAP, each sen- models. The distances between wireless sensor nodes are sor node needs to maintain a neighbourhood table to store the utilized as the significant parameter for regulating and equal- topological information about its neighbours. Besides, EAP izing the amount of energy spent among them, maximizing introduced area coverage to reduce the number of working the network lifetime. Their research focuses on calculating nodes within each cluster to prolong the network lifetime. the best number of hops based on the distance between the In Adu-Manu et al. (2018), the authors discussed various source its neighbours. DEAR used two tables (that is, the approaches to harvesting energy to improve network life- routing table containing information about the node and the time, such as solar and wind power. Different ways of har- neighbour table containing distance, energy information, vesting solar and wind power have been used in other similar among others). The problem with this protocol is that nodes systems. These methods include using a feature selection 1 3 J.-D. Abdulai et al. filter and a hybrid forecast engine based on a neural network connectivity is the time interval for the network to perform (NN), an intelligent evolutionary algorithm, Pearson's corre- sensing and transmission functions (Adu-Manu et al. 2019). lation coefficient, mixed integer genetic algorithm (MIGA), In WSNs, power is expended during the following pro- and multi-objective particle swarm optimization (PSO) to cesses: (1) transmission, (2) reception, (3) sensing, and (4) reduce operating costs, payment costs, and electricity costs sleep mode. The transmission power (Tx) is the amount of in micro-grid systems and other sites (Aghajani and Ghadimi energy/power required to transmit data packets. It is also 2018; Akbary et al. 2019; Hamian et al. 2018; Liu et al. proportional to the communication distance between the 2017; Mirzapour et al. 2019). transmitting and receiving nodes (usually called the trans- The techniques and approaches proposed in literature over mission range). Reception power (Rx) is the amount of the years to improve energy efficiency and maximize the energy required to receive data packets in a network. Sensing network lifetime in WSNs have not efficiently solved the power (S) is the power needed for a sensor node to sense an energy problem. Hence, we propose a modified distance- event within time t seconds, and in sleep mode, the power is based energy-aware routing protocol that minimises the consumed when the sensor node is in the sleep state (where energy consumed for transmitting data packets by calculat- no activity is performed). ing the distance between two successive nodes. Given a network of n nodes, the lifetime of the network may be defined as the length of time t when the death of the ith node partitions the network such that it loses its func- 3 L ifetime maximization problem in WSN tional purpose (i.e. degrades the network to such a point that it can no longer function as a network). For example, a To measure the network lifetime, metrics such as coverage network of n nodes is partitioned into an m-subgraph with and connectivity are taken into account. Region of interest k anchor nodes that connect this subgraph to the sink, as (RoI) determines the coverage of the network. The window shown in Fig. 9. Network lifetime is defined as when the last of time the RoI is within the sensing range of a sensor node anchor node dies since the nodes of a subgraph cannot reach will impact on the network lifetime. The longer the RoI is other nodes in the network nor the sink. Anchor nodes are within sensing range, the more data points are captured and nodes through which all other nodes within a subgraph will hence more energy expended. Network lifetime will also gain access to the sink. It is the last node in the route or path be dependent on the number of successful data collection to which other nodes could reach the sink. The network still rounds and the total number of data packets that are trans- is available when let's say, anchor node 1 dies, but there is a mitted to the sink and these defines the connectivity of the degradation in the network. From Fig. 9, the network ceases network. In other words, network lifetime with coverage and to exist when all four anchor nodes die. Fig. 9 Network lifetime illustra- tion 1 3 A modified distance‑based energy‑aware (mDBEA) routing protocol in wireless sensor networks… 3.1 T ransmission range adjustment may be lost since the nodes might have to transmit at maxi- mum capacity to reach the base stations. In other cases, the In WSNs, reliably transmitting information from the source nodes may be closer to each other, thereby transmitting at node to the sink node is vital. It is, however, important that full power using fixed transmission ranges may only increase this is done with minimal energy requirements. In typical the energy wastage. Therefore, in such deployments, opti- networks, the transmission range of the nodes is fixed. Inde- mally choosing the transmission power is essential. Network pendent of how far away they are from each other and the lifetime and the packet delivery ratio may be improved by sink node, the same power level is expended in transmis- adjusting the transmission ranges of the sensor nodes (Arya sion. In a sensor network, the choice of the transmission 2013). power level between two nodes directly impacts the energy consumed by the nodes. Hence, controlling the transmis- 3.2 Network model and problem formulation sion power level and adjusting it to the appropriate distance should be considered to improve the network's lifetime (Arya We consider a river sensor network in which mobile sensor 2013; Chen et al. 2002). nodes detect water quality parameters such as pH, dissolved oxygen, conductivity and many more. Initially, the nodes are 3.1.1 Effects of transmission range adjustment assumed to have been deployed randomly in a rectangular array. The node forwards the sensed data as packets peri- Adjusting the transmission range may affect the performance odically to static sinks positioned at the various locations of the network. When a source node changes its transmission along the river bank. The packets may be sent directly or range to the highest range, it reduces the number of interme- through a next-hop neighbour to the sink. In each case, the diate nodes (or, in some cases, may not use an intermediate node transmitting the data packet adjusts its transmission node) in the path to the sink node. Such an approach will range to minimize the amount of energy consumed for the extend the collision domain and hence increase the likeli- transmission of data packets to prolong the network lifetime. hood of a collision. Choosing a shorter transmission range means using more intermediate nodes to send data packets 3.2.1 A ssumptions to the sink. In this case, the collision domain is reduced, and, as such, there will be fewer collisions (Chen et al. 2002). Before providing the details about the transmission range This technique improves the communication in the network adjustment algorithm, the following assumptions are made: and reduces the amount of energy consumed by the nodes at any given time. To this end, the authors (Sharma et al. 1. Each sensor node is aware of the position/location of 2019) proposed a maximum one-hop transmission distance all other nodes, including the sinks, at the start of the to minimize the total energy consumed within the network. simulation. 2. The source node communicates to the sink directly or 3.1.2 Effects of power consumption uses neighbour node(s) to reach the sink node. 3. The sensor nodes communicate through multi-hop to In mobile sensor network applications, the power consump- avoid transmission and propagation delay. tion of nodes depends mainly on the transmission range. 4. The mobility of the nodes also contributes to communi- Energy consumed during packet transmission and reception cation overheads in the network. depends on the transmitting range of the communicating 5. The mobile sensors are capable of adjusting their data node (Engmann et al. 2020). The amount of energy required transmission range. for a packet to be transmitted and received should be min- 6. The data transmission and reception activities are the imal to increase the lifetime of the sensor network (Eng- primary energy consumption activities. mann et al. 2018). In WSN applications where the nodes are 7. The source nodes move freely in one direction after mobile, multi-hop communication is the preferred option. being deployed (movement patterns follow that of river Packets may be forwarded from one node to the other using flow). maximum transmitting power to deliver packets successfully 8. Static or mobile sinks are used throughout the simula- to the receiving node. Although the packet delivery ratio tion. is likely to be high, transmitting at full power may cause a decrease in battery lifetime (Khemapech et al. 2007). In 3.2.2 General notation networks for river monitoring applications, using a fixed transmission range may not be ideal since nodes are likely In WSNs, the distance between the nodes affects the trans- to have moved from their original positions due to river cur- mission power. More energy will be expended when the dis- rent. In such situations, it is expected that communication tance between nodes is vast, which may likely decrease the 1 3 J.-D. Abdulai et al. nodes' lifetime. In formulating the problem, it is assumed and (2) deployment of a large number of nodes within the that the sensor network consists of a set of sensor nodes area of interest may result in an excessively dense network N = {n1, n2, n3, …, nj}, where j is the total number of nodes that is prone to congestion and collisions. These considera- deployed. In modelling the transmission range adjust- tions are critical in determining the size of the river's area of ment problem, the sensor network is represented by a interest to deploy sensor nodes to monitor freshwater quality graph G = (n, cl), where cl is the communication links with efficiently. Hence, the river’s dimension is determined by Cl = {c1, c2, c3, …, cn}. A source node may directly trans- the number of nodes and the type of network topology. This mit data packets to sinks or indirectly send them through its paper used 100 m × 750 m because we deployed up to 200 neighbours. Sk represents the set of sink nodes with Sk = {s1, nodes. When the sensor nodes in the river sensor network s2, s3, …, sn,} and it is assumed that the sink node is the end- (RSN) are deployed, and the nodes begin to move in the point of the communication link. Communication links are direction of the flow of river, they usually form a tree or established between the source nodes ni, neighbour nodes, mesh topology. Sensor nodes in RSNs with the exact river nr, and Sk sink nodes. Nodes n1 and n2 are neighbours if they dimensions can be statically deployed and create either a lin- are next-hop nodes. The communication path between two ear or a star topology based on the river monitoring scheme nodes may belong to set Cl. The transmission range R is the and the fixed distances between the nodes. The contour of maximum value set for nodes to communicate, and it is the the river may be rectangular or irregular. The randomly same for all nodes in the network. The Euclidian distance d deployed nodes obtain the positional information of their between nodes n1 and n2 is d (n1, n2). Hence, cl = {(x, y) ∈ E|d next-hop and the sink nodes at the first broadcast within the (n1, n2) ≤ R}. The position (x, y) of each sensor node in a network. two-dimensional (2 − D) space is represented by P = {p1, p2, A source node sends a broadcast message when it detects p3, …, pt}. The positional information is important since an event. The source node n1 discovers a neighbour node n2 that will be required for determining the distance between within its transmission range to forward the packets. Then the nodes. the neighbour node n2, upon finding a sink s1, forwards the In this model, rather than transmit with the maximum packets received from n1 to s1. The goal is to transmit data allowable power, nodes will communicate with a power less packets from n1 to s1 with minimum energy consumed by n1, than or equal to the power required to reach the maximum n2, and s1. The main issue of concern tackled here relates to transmission range. The nodes need positional information the neighbour node n2 changing its relative position to the of their neighbours, the sink, and the node's position infor- direction of flow and speed of the river. The sensor node mation to decide which transmission range to use for packet n1 is likely to be within the range of a neighbour node if forwarding to conserve energy. Existing location-based rout- the following conditions are met: (a) sensor node position ing protocols use GPS information or non-GPS information remains the same if and only if both sensor nodes are mov- to determine the positions of other nodes in the network ing with the same speed in their deployable environment; (Jabbar et al. 2018). The nodes are positioned randomly (b) the river speed is the same at different sections in the in the network area. For nodes to identify the positions of region of interest; (c) the transmit node, n1 receives a con- other nodes in the network, they must periodically send a stant response from its immediate neighbour when the hello hello message to obtain the positional information of other messages were sent. nodes in the network. When the nodes are not reached by this method, a broadcast message is sent. The amount of 3.2.4 Energy and distance notation energy consumed is proportional to the distance between the transmitter and receiver and the amount of energy required As defined earlier, network lifetime is ultimately directly for the data to reach its destination. related to or proportional to the total energy expended before the network dies. This energy equals the sum of energy spent 3.2.3 Transmission range adjustment algorithm transmitting from the sensor nodes to the sink node through a neighbour node over some distance. The equations pre- To investigate the impact of distance and the location of the sented in this section consists of variables defined in Table 1 sensor nodes on energy consumption, using the ns-3 simula- as follows: tor, n sensor nodes have been randomly deployed in a river The model proposed in this paper calculates the network of dimensions, A = 100 m × 750 m. The authors chose a river lifetime as the sum of energy spent to transmit data packets dimension of 100 m × 750 m for this work due to the fol- directly to the sink or through a neighbour node to the sink. lowing considerations: (1) the deployment of fewer nodes Hence, the energy consumed for this operation is described within the area of interest, which would almost certainly in Eq. 1. result in a sparse network, thereby impairing connectivity, 1 3 A modified distance‑based energy‑aware (mDBEA) routing protocol in wireless sensor networks… Table 1 Equation parameters Variables Meaning of the variable explained eti Energy expended by the transmitter eri Energy expended by the receiver nr Neighbour node Etotals-hop Total energy consumed by nodes through direct communication (single hop) Etotalm-hop Total energy consumed by all the nodes in a multi-hop communication Eoverall Total energy expended in the network d The dimensionless factor that accounts for the distance di,j Distance between node i and node j r The distance between node positions Numpktrec Number of packets received esk The energy expended at the sink to receive and process the data cosθ The angle subtended between a source node and two neighbours or two sinks ( ) n n In introducing the distance parameter in Eqs. 3 and 4, the ∑ ∑ Etotal = e + e + e + em hop ti ri ti s (1) protocol considers the Euclidian distance between the source − k i=1 i=1 node and the angle subtended by two neighbours or two Equation 1 holds for n > 2 when the number of inter- sinks taking the source node as a reference point (see Eq. 6). mediate nodes is equal to 2 (i.e., if n = 2), the total energy √ 2 2 expended is given by Eq. 2. di j = r + r − 2r r cos, (6)1 2 1 2 Etotal = eti + es (2) The distance di,j holds under three conditions:s−hop k Under condition 1, the source node (S7) transmits packets The expression in Eq. 3 is obtained from the energy to a neighbour node (S8) one-hop away and within its trans- required for a source node to transmit packets through a set mission range, as shown in Fig. 10 Region C. S7 and S8 are of neighbour nodes to the sink node as a distance function. said to be in a line-of-sight (LoS). Similarly, source node, { ( ) } S8 and the neighbour node, S10, are in LoS. Hence, there is n n ∑ ∑ (3) direct communication between S7, S8 and S8, S10. The dis-Etotal (dij) = em hop ti + eri + eti ∗ dij +es− k i i tance between the source and destination nodes is calculated =1 =1 based on their positional information using Eq. (7). Here, To compute the power consumed by the transmitter when the angle subtended between the two nodes is assumed to be a sensor node n sends a packet to sink node sk, the energy 180. Once node S7 is within the transmission range of node consumption between the sensor node and sink node over S8, communication is established, and packets are transmit- distance dij is described in Eq. 4. ted to node S8 and then from node S8 to node S10 within ( ) the network as much as the distance between the transmitter Etotal (dij) = eti ∗ ds hop ij + es− k (4) and receiver do not go beyond the transmission distance of The energy consumed by a source node to communicate nodes S7 and S8. through a set of neighbour nodes to the sink is calculated √ 2 2 based on the power consumed at the source node, di = r + r + 2r r,j (7)1 2 1 2 Etotal (dij) , the next-hop neighbours (i.e., which uses both m−hop Under condition 2, we consider a source node within the the transmission power Ptx and the received power Prx) and range of two neighbour nodes as shown in Fig. 10 Region the received power at the sink, +es . A neighbour node k B. Here, Node S3 sends a broadcast within the network and receives and transmits packets; therefore the energy used by obtains location information of neighbour nodes within its the neighbour node per packet is the sum of the transmit and transmission range (that is, nodes S4 and S5). The distances receive power. The sink (sk) uses only the receiving energy between source node S3 and its neighbours are determined to receive packets from source nodes or next-hop nodes. based on nodes S4 and S5. Communication between the Hence, for a sink to receive packets directly from a source source node and the destination nodes is established based node, it expends the received power energy in Eq. 5. on the transmission distances and angle formed by the P = e ∗ Num (5) source node. The distance between the nodes is obtained rxc ri pktrec 1 3 J.-D. Abdulai et al. Fig. 10 Calculating distance between nodes using Eq. 6. which is used to calculate the distance between sinks broadcast their position for the source nodes to obtain the source node and two neighbouring nodes. their position and store the location of the receivers; (2) Under condition 3, we consider the value of θ in Eq. 6 An active source node (i.e., a node that has detected an to lie between 0° < θ < 90° or 90° < θ < 180°. This is the event) sends a broadcast message and waits for a response. condition determined in Eq. 6 for all angles that satisfies the When a response is received, it establishes the position of following two relations 0° < θ < 90° or 90° < θ < 180°. This the neighbour node or the sink. It calculates the distance condition satisfies the scenario labelled Region A (which between itself (i.e., the sender), its immediate neighbours, is similar to condition B) in Fig. 10, where the source node and the destination node. The modified distance-based (S6) is in the transmission range of two sinks (SK2 and energy-aware (mDBEA) routing protocol proposed in this SK3). The source node (S6) calculates the distance between paper operates using the algorithms described in the sec- itself and the sinks based on their position information and tions that follow. adjusts its transmission distance to the shortest distance between the two sinks. In this case either r1 < r2 or r2 < r1 4.1 Power adjustment algorithm as illustrated in Fig. 10 Region A. In some special cases where the value of θ approaches 90°, the distance dij is cal- The proposed algorithm provides strategies to efficiently culated using Eq. 8 minimize the energy consumed by the nodes in the sen- sor network. The energy-efficient power adjustment algo- √ d r2 r2i = + rithm is implemented in a sensor network environment with ,j (8)1 2 mobile source nodes and static sinks set up rectangularly. From Algorithm 1, if the mobile source node with initial transmission distance d meters senses an event, it broad- 4 The modified distance‑based casts to find the neighbours and sink/destination nodes energy‑aware (mDBEA) routing protocol within its range. The neighbour nodes within reach of the source node send their position information to the request- The introduction of a modified distance-based routing pro- ing node. The flowchart for Algorithm 1 is depicted in tocol capable of adjusting the transmission range during Fig. 11. Depending on the position information, the sender packet transmission is presented. The modified Distance- chooses the node located a minimum distance away and based Energy-Aware (mDBEA) routing protocol proposed adjusts it transmit power to communicate with this identi- in this paper works as follows: (1) At the initial stage, the fied neighbour node. 1 3 A modified distance‑based energy‑aware (mDBEA) routing protocol in wireless sensor networks… Fig. 11 Calculating the distance between nodes using power- adjust algorithm Algorithm 1: Power-Adjust Algorithm 1 SET srcNODEtxp = Ptxinitial; 2 for each next-hop in the srcNodes transmission range, do 3 DETERMINE the position; 4 CALCULATE the distance between srcNode position and its NEIGHBOURS position; 5 SELECT minimum distance (minDIST) from each nextHOP within the range; 6 SET Ptxadjusted = assignRANGE (minDIST); 7 SEND data packet with Ptxadjusted to the selected node; 8 end 1 3 J.-D. Abdulai et al. Fig. 12 Calculating distance between nodes using source node to sink communication 4.2 Direct transmission to sink node (DTS) for packet transmission. The algorithm is designed for use when the transmitting node has enough energy in its buffer This section presents the DTS Algorithm as a technique for to perform the transmission directly without resorting to adjusting the transmission range of nodes communicating neighbour nodes. The approach, as mentioned earlier, guar- directly to the sink node. As shown in Algorithm 2, DTS antees network connectivity. While the source node is still in algorithm is applied in such a way that it automatically motion, the source nodes' position information is sent when adjusts the transmission range by calculating the Euclidian receiving a broadcast notification message from its next-hop distance between the source node and the sink using Eq. 8. neighbours. By this approach, energy is reduced to prolong The Euclidian distance is calculated based on the vector the lifetime of the sensor network. The best paths from the positions of two nodes (the source and the sink). DTS algo- source node to the base station were determined using route rithm uses the distance calculated to determine the appropri- optimization. We chose the most efficient route to extend the ate transmission range required to transmit the data packet network's lifespan. As a result, selecting the shortest route from one node to the other. The flowchart for Algorithm 2 is extends the life of sensor nodes. depicted in Fig. 12. The selection of the minimum transmis- sion range aims to minimise the amount of energy consumed 1 3 A modified distance‑based energy‑aware (mDBEA) routing protocol in wireless sensor networks… Fig. 13 Calculating distance between nodes using neighbour node to sink communication 1 3 J.-D. Abdulai et al. Algorithm 2: Source Node to Sink Communication Data: To send data packets directly to the sink node: Data: Source Nodes placed randomly at the region of interest as the node moves at time t and detects/senses a contaminant Data: SrcNode BROADCAST RREQ to send packets to sink at the initial transmission range 1 if srcNode receives RREP from the sink then 2 srcNode sends data packet to sink using the PtxInitial; 3 else 4 SET Ptx = PtxMax; 5 end 6 if sink detected then 7 CALCULATE the distance to the sink SET Ptx = Ptxadjusted; 8 TRANSMIT data packet to sink; 9 srcNode receives ACK from the sink using RREP; 10 else 11 BUFFER the PACKET and RETRY in t seconds; 12 end 4.3 N eighbor node to sink node communication itself. The broadcasted node then selects the replying node with the shortest distance and the minimum number of hops In Algorithm 3, nodes select their next-hop to the sink node to the sink. The broadcasted node after choosing the mini- by sending broadcasting to the other nodes in the network. mum distance transmits packets to the sink using this dis- When a reply is received from its immediate neighbours, tance. The flowchart for Algorithm 3 is depicted in Fig. 13. it calculates the distance between each replying node and Algorithm 3: Neighbor Node to Sink Communication Data: To select a nextHop to route to the sink node: Data: Let the source node (srcNode) broadcast RREQ 1 if srcNode receives a REPLY then 2 Calculate the DISTANCE d, between this NODE (i.e., the node (s) that sent the REPLY using the nodes position; 3 Source node SELECTS the REPLYING NODE with the shortest DISTANCE dshort and the MINIMUM Hops to the SINK NODE; 4 ADJUST the TRANSMISSION POWER, Ptx to this DISTANCE dshort value calculated 5 TRANSMIT data to this receiving NODE at this calculated transmission range value, Px(d); 6 end Data: Let the source node unicast "Hello" to keep track of the next-hop neighbour 7 if next-hop is alive then 8 Calculate the distance d; 9 ADJUST the TRANSMISSION POWER, Ptx to this DISTANCE dshort value calculated; 10 TRANSMIT data to this receiving NODE at this calculated transmission range value, Px (d); 11 else 12 Let the source node (srcNode) broadcast RREQ; 13 end 1 3 A modified distance‑based energy‑aware (mDBEA) routing protocol in wireless sensor networks… Fig. 14 Calculating distance between nodes using greedy-hop algorithm 4.4 G reedy‑hop algorithm a break in the network connectivity or the transmission link may be due to the movement of a sensor node that had estab- In the greedy-hop algorithm, as shown in Algorithm 4, each lished a connection but moved due to the speed of the node node to save energy drops the packet to the next node with at its current position/location. The break may also be due the minimum distance without considering the neighbour's to insufficient residual energy, which caused the node to energy level and distance to the sink. Figure 14 shows a die-off. flowchart showing the flow for Algorithm 4. In our approach, 1 3 J.-D. Abdulai et al. Algorithm 4: Greedy-hop Algorithm Data: srcNODE broadcast RREQ to send packets to sink to find all possible routes Receiving nodes (nextHOP) sends their positions 1 if srcNode receives RREP from the nodes within its range position, then 2 for each RREP, do 3 DETERMINE the distance d, based on the nodes position/location; 4 srcNode SELECTS the next-hop with minimum transmission distance; 5 ADJUST Ptxinitial = PtxMinDist (i.e., the calculated distance); 6 TRANSMIT dataPacket to the nextHop; 7 while PtxInitial! = PtxMax do 8 end 9 end 10 ADJUST Ptxinitial = PtxMax (i.e., the calculated distance); 11 TRANSMIT dataPacket to sink; 12 end 5 P erformance evaluation and validated using wireshark. Details of each packet were decoded and displayed for evaluation and verification. The 5.1 Simulation parameters simulation parameters are shown in Table 2. Experiments were run using network simulator 3 (NS3). The 5.2 A nalysis on mobile sensor nodes and static simulation model is designed to mimic a freshwater environ- sinks ment where frequent water quality data is collected via a network. The network environment simulated in NS3 has a The AODV protocol is a reactive protocol for wireless sen- deployment area of 100 m by 750 m. The initial energy set sor and adhoc networks that has widely been used in the for each sensor node in the simulation was 20 Joules. Each research community. AODV routing protocols are used to sensor node generates data at a rate of 7 packets per second. save bandwidth and also conserve power in large and dense For the network to ensure connectivity, the transmission mobile networks. The AODV is the de-facto routing protocol range for each mobile sensor node was set to 80 m. Seven that enables nodes to establish and maintain connectivity in (7) packets per second for packet generation rate was chosen a multi-hop mobile ad-hoc network. In AODV, mobile nodes to regulate the amount of energy consumed by the sensor respond to breaks in communication links and changes in nodes. The simulation output (pcap files) were analyzed topology of the network. AODV is designed to maintain routes that are actively engaged in communication. The message types defined in AODV are route request message Table 2 Network parameters used in simulation (RREQ), route reply message (RREP), route error message (RERR), and HELLO message. Routes are requested on- Network parameter Value demand as compared to other proactive routing protocols Number of nodes Up to 200 such as DSR and OLSR. AODV sends route error (RERR) Packet generation rate Seven packets per second messages whenever a link breaks and reestablishes a new Type of traffic Constant bit rate (CBR) route after the old route breaks. Packet size 512 byte Further to the above reasons, we also performed a vali- Initial energy 10 J dation test among the existing MANET routing protocols Mobility model RandomWalk2D in NS-3 (AODV, OLSR, DSDV). The validation test was Minimum speed 10 m/s to represent low mobility performed to evaluate the performance of the protocols with case various distances and determine the protocol that records the Maximum speed 50 m/s to represent high mobility highest packet delivery ratio (PDR) and use that as the base- case line protocol to compare our work. At a distance of 50 m, Initial transmission range (radio) 80 m AODV performs recorded 78% PDR compared to DSDV Propagation model Range propagation and OLSR which recorded 71% and 72% PDR respectively. Number of mobile sinks Variable 1 3 A modified distance‑based energy‑aware (mDBEA) routing protocol in wireless sensor networks… Fig. 15 Residual energy consumption Fig. 17 Algorithm performance To ascertain the performance of the proposed transmis- sion range adjustment algorithm concerning the energy con- sumption rate by the mobile sensor nodes vis-a-vis the num- ber of packets delivered at the sinks, a scenario of up to 200 nodes was used to compare the performance of mDBEA. The performance between the two protocols is shown in Fig. 16. It shows that the proposed algorithm outperforms AODV for the different sensor nodes used for the simulation. It may be observed that when the number of sensors was increased to 160 nodes, both algorithms peaked up, but the number of packets dropped sharply when the number of sen- sor nodes was increased beyond 160 due to collisions in the channel. Also, most packets were not retransmitted since the number of packets competing for channel access increased per unit time. Panchal and Singh (2021) presented work in which they compared the residual energy of a network to Fig. 16 Average energy consumption test the outcomes of their protocol. The results showed that residual energy becomes 50% of the network's initial energy after a certain number of rounds. Compared with the AODV, the mDBEA routing pro- In other scenarios, the performance of the transmission tocol prolongs the network lifetime of the sensor network range adjustment algorithm illustrated in Algorithms 1 to thrice that of AODV. When the residual energies of both Algorithm 3 discussed in this paper was run to compare protocols were compared, as shown in Fig. 15, it was their performance. Figure 17 shows the outcome of the deduced that the energy consumption decreases over time algorithm's performance in terms of packet delivery ratio. as the number of rounds increases. Comparing AODV and When the greedy approach was adopted, we recorded a mDBEA routing protocol, it can be seen that using AODV; PDR of about 95%, but the PDR dropped to about 55%, the energy decreases much faster than mDBEA. Similar with a transmission range of 180 meters. The next-hop and studies utilizing an energy-aware routing protocol to deter- the direct-to-sink algorithms yielded a PDR of 82% at the mine the network lifetime are discussed in Liu et al. (2009), transmission range of 20 meters. The PDR of the next-hop Mazinani et al. (2012) and Wang et al. (2010). In this sec- approach was reduced to 30% at about 180 meters. From tion, we conclude that when the AODV routing protocol Fig. 17, the observed decrease in PDR is due to the mobility is implemented with our network setup, the node's battery scheme (the random walk) used in the simulation. All three drains more quickly than the proposed algorithm, as shown algorithms at the transmission range of 60 meters yielded in Fig. 15. a PDR of 80%, as illustrated in Fig. 17. Figure 18 shows 1 3 J.-D. Abdulai et al. Fig. 18 End-to-end delay Fig. 19 Maximum speed vs throughput the end-to-end delay from packet generation to reception. In another paper, the authors presented a PDR analysis of their routing protocol and evaluated the impact of adjustment on PDR. With approximately 700 nodes in the network, they achieved a PDR of 63% (Draz et al. 2021). The performance comparison of the two routing protocols in terms of end-to-end delay is shown in Fig. 18. From the diagram, it may be seen that the traditional AODV recorded better end-to-end delay compared to mDBEA when the speed of the nodes increased over time. The end-to-end delay increased with increasing speed in both protocols, attributed to high mobility. The end-to-end delay at the speed of 12ms for the proposed approach was 0.08 seconds. In contrast, AODV was around 0.04 seconds resulting from the possible number of nodes transmitting packets within the specified period. (Draz et al. 2021) performed an end-to-end delay analysis of a similar proposed scheme (that is, a collision- Fig. 20 Packet delivery ratio at transmission range of 80 m free routing protocol) and reported the highest delay of about 0.6 seconds, slightly lower than the end-to-end delay record routing protocol recorded a PDR of 0.54 (54%). Although with our approach. the PDR dropped after several simulation rounds (about 20 The sensor nodes require position information to take rounds), the PDR increased to 0.85 (85%) at about 25 simu- the appropriate decision to adjust its transmission distance lation rounds (i.e., this happens to be the best performance). before transmitting data packets to the next-hop neighbour. PDR again dropped slightly to 0.84 (84%) at 35 simulation The neighbour's position might have changed at this time, rounds and 0.83 (83%) at 43 simulation rounds (see Fig. 19). causing an increase in delay. As shown in Fig. 19, AODV Increasing the number of simulation rounds improved the experienced low throughput compared to mDBEA. The low performance in terms of PDR. The results show high sys- performance of AODV is due to the high degree of mobil- tem overheads, which may be due to the relative speed and ity, which causes the links established between the nodes to the direction of the flow, which determines the path of the break. mDBEA overcomes the link break problem because mobile sensor node. The experimental results show that the of its ability to adjust its transmission range. movement of the sensor nodes impacts end-to-end delay (see In Figs. 19, 20, 21 and 22, the transmission range was set Fig. 20). to 80 m to evaluate the following performance metrics: end- An end-to-end delay for the initial simulation rounds to-end delay, PDR, and the number of received packets. At was low. At 25 simulation rounds, a delay of 0.05 s was the start of the simulation, the performance of the mDBEA recorded, and at 35 rounds, a delay of 0.063 s was recorded. 1 3 A modified distance‑based energy‑aware (mDBEA) routing protocol in wireless sensor networks… Fig. 21 Delay at transmission range of 80 m Fig. 23 Energy consumption for nodes 5, 10, and 15 Fig. 22 Number of packets received at transmission range of 80 m Fig. 24 Energy remaining for nodes 5, 10, and 15 It can be deduced from the low delay values that there was packet transmission errors are minimal in the new approach, less congestion in the system, which is the reason for an which improved the number of packets received at the sink. increased number of packets received in the sensor network, Hence, in the new approach, as shown in Figs. 21 and 22, as depicted in Fig. 21. The success rate may be attributed to fewer packets are lost. From Figs. 23, 24, 25 and 26, several the minimum number of hops used for packet transmission nodes between 10 and 30 were used to evaluate the proposed because of the algorithm's efficiency. Although a good num- algorithm's energy consumption pattern. Figures 19 and 20 ber of the data packets were dropped, the experiment shows show the rate of node depletion in the network. that several packets sent were also received. 175 packets Similarly, Figs. 23 and 24 show the energy consumed and were obtained when the simulation rounds were increased to the remaining energy after transmission and reception. In 25 but dropped sharply to about 60 packets per second due WSNs, river network monitoring using mobile sensor nodes to a break in the communication links and high mobility. and static sink (s) improves energy efficiency in the sensor The packets increased steadily to about 168 packets at 35 network. In such networks, data packets are forwarded to simulation rounds (see Fig. 22). the sink nodes through single-hop or multiple-hop infra- The performance of the modified mDBEA improved structure. In sensor networks, routing is used to reduce the because there was less interference in the channel. Also, amount of energy consumed by sensor nodes. Nodes around 1 3 J.-D. Abdulai et al. Energy consumption for nodes 20, 25, and 30 Fig. 27 Energy efficiency vs number of nodesFig. 25 Fig. 28 Network lifetime vs number of nodes Fig. 26 Energy remaining for nodes 20, 25, and 30 about 80% after 40 simulation rounds due to the number of iterations in Fig. 30. the sink utilize their energy quickly and may be the first node to die. These dead nodes reduce the number of data packets 5.3 Analysis on mobile sensor nodes and mobile that can reach the sink node (s) and thus the network lifetime. sinks In comparison to Xin and Liu (2017), the authors reported that the average energy consumption of a node is 0.0016 In the chosen scenario and deployment environment, Joules when nodes are placed 1000 m apart. From Figs. 27, depending on the position of the sink, its speed, and direc- 28, 29 and 30, we observed trends in energy consumption, tion may give rise to the three main problems discussed and PDR, and network lifetime. AODV consumed more energy illustrated earlier in Sect. 3.2.4. The sink node does not coor- with an increasing number of nodes than mDBEA, which dinate the movement of mobile sensor nodes because of the tends to be more energy-efficient. AODV and mDBEA drop nature of the application environment. Both mobile sensors to PDR of about 90.5% after 20 simulation rounds as shown and the sink nodes follow the river path (i.e., direction) and in Fig. 29, compared to the PDR of mDEBA which drops to the river's flow rate (i.e. speed). 1 3 A modified distance‑based energy‑aware (mDBEA) routing protocol in wireless sensor networks… Fig. 29 PDR at 20 simulation Fig. 31 Throughput at different speeds Fig. 30 PDR at 40 simulation rounds Fig. 32 Delay at different speeds Figure 31 shows the throughput values of the mobile 6 Conclusion sensor nodes and mobile sinks scenario—the variation in the speed from 5 to 10 m/s and 15 m/s aided in the evalu- Wireless sensor networks for environmental monitor- ation of mDBEA. The results showed a considerable drop ing applications require long battery life and low power in throughput in all cases after 25 simulation runs. The net- consumption to enable them to operate over a prolonged work's poor performance may be due to the movement of period. An essential requirement for such networks is that the sink node, which may be suffering from the sink mobil- the energy consumption of the nodes should be kept minimal ity problems discussed earlier in this paper: the end-to-end to increase the lifetime of the wireless sensor network and delay and the nodes extra energy. Figure 31 shows that at improve the performance of the sensor nodes and the wire- 5 m/s, the throughput was better compared to 10 m/s and less network. Ad-hoc on-demand distance vector (AODV) 15 m/s. The energy consumed by the mobile sensor nodes routing protocol, designed for routing in mobile ad-hoc and the mobile sinks tends to be higher for higher speeds networks (MANETs), has been shown to provide efficient compared to nodes with lower speeds. In Fig. 32, the energy routing in wireless sensor networks (WSNs). 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