A Study into Lifetime Maximization of Wireless Sensor Networks for Water Quality Monitoring
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Date
2020-09
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Publisher
University Of Ghana
Abstract
Freshwater sources represent only about 2.5% of the world’s water bodies, hence, maintaining
and monitoring its quality is vital. Several deaths reported across the world is as a result of
contaminated and polluted water. Among the various methods proposed for monitoring water
quality, the use of wireless sensor networks appear to have gained currency. Advances in
Wireless Sensor Network (WSN) technology and the emergence of the Internet of Things
(IoT) is engendering significant improvements in the operation and delivery of services in
many developing countries, including Ghana. One application area of WSNs is in Water
Quality Monitoring (WQM). Measuring and monitoring water quality parameters to obtain
quality data has been of great concern to governments, researchers, stakeholders,
policymakers, and the community at large. WQM systems seek to ensure high data precision,
data accuracy, timely reporting, easy accessibility of data and completeness. The conventional
monitoring systems are inadequate when used to detect contaminants/pollutants in real-time
and cannot meet all the requirements of WQM systems. Collecting water samples for lab
analysis is expensive, time-consuming, and a repetitive process. This approach lacks
sufficient data for trend analysis for the development of appropriate models. The
traditional/conventional approach to monitoring water quality does hence not achieve the set
objectives for real-time WQM. There is, therefore, the need to adopt an approach which is
efficient and cost-effective and overcomes the problems associated with the conventional
methods. In recent years, wireless sensors capable of detecting the presence of pollutants and
heavy metals in water bodies have been developed and commercialised. Wireless Sensor
Networks for environmental monitoring applications require long battery life and low power
consumption to enable them to operate over a prolonged period. An essential requirement for
such networks is that the energy consumption of the nodes should be kept minimal to increase
the lifetime of the wireless sensor network and to improve the performance of sensor nodes
and the wireless network. The dissertation in addressing the energy consumption and lifetime maximisation problems proposes a novel transmission range adjustment algorithm based on
the position of the sensor nodes. The dissertation is organised broadly in three phases. In phase
1, a systematic review of current trends in water quality monitoring using wireless sensor
networks and future developments are discussed. This top-down study surveys the different
water quality monitoring approaches ranging from traditional manual approaches to more
advanced technological approaches. Also, this specific contribution highlights recent
advances in the design of sensor devices, data acquisition procedures, communication and
network architectures, and power management schemes to maintain a long-lived operational
WQM system. In phase 2, an improved Ad-hoc On-Demand Distance Vector (AODV)
protocol that takes into consideration the distance from neighbouring nodes and adjusts the
transmission power accordingly is presented. The Euclidian distances between the nodes are
calculated, and data packets are transmitted using the absolute value of the Euclidian distances
as the transmission range between the nodes to minimise energy consumption and maximise
the lifetime of the node and the network. Network Simulator 3 (NS3) is used to simulate the
Packet Delivery Ratio (PDR), the energy consumption of the nodes, average delay, and other
parameters for performance evaluation of the WSN using the improved AODV. To verify and
validate the operation of the algorithm, a simulation was performed, and results from the
evaluation were presented. Finally, in phase 3, an automatic monitoring system using real
testbeds in real-time is presented. The sensor nodes can collect data with high integrity and
accuracy at different sampling locations and with the desired temporal granularity. The study
was conducted at the Weija intake in the Greater Accra Region of Ghana. The Weija dam
intake serves as a significant water source to the Weija treatment plant which supplies treated
water to the people of Greater Accra and parts of Central regions of Ghana. Smart water
sensors and Smart water ion sensor devices from Libelium were deployed at the intake to
measure calcium ion (Ca2+), conductivity, pH, dissolved oxygen, silver (Ag+), fluoride ion (F), nitrate ion (NO3-), oxidation-reduction potential (ORP), and temperature. The results do
indicate significant fluctuations over time in of all the parameters that were monitored. These
changes may be attributed to pollution from upstream, which are time varying.
Description
PhD. Computer Science
Keywords
Wireless Sensor Networks, Water Quality Monitoring