Development of an Artificial Fish Swarm Algorithm Based Energy Efficient Target Tracking Scheme in Wireless Sensor Networks.
ABSTRACT
Optimal deployment of sensor nodes in other to ensure optimum network coverage is one of the challenging problems faced by Wireless Sensor Network (WSN) researchers due to the complexity and exhaustive nature of WSN. The target tracking problem is concerned with maximizing the lifetime of the network while continuously monitoring a set of targets.
This dissertation presents an optimal deployment of WSN and target tracking scheme using the intelligent swarming behaviors of the Artificial Fish Swarm Algorithm (AFSA). The preying, swarming, and chasing behaviors of AFSA were initially replicated using MATLAB R2013b simulation environment.
The position of network nodes was randomly deployed in a network coverage area of 60 square meters with a total of 60 sensor nodes of 4m radius and communication range of 15m using the replicated AFSA algorithm. Thereafter, the replicated AFSA was used to detect events based on the target discovery probability model.
A series of simulations were performed, and results showed that the proposed technique can attain maximum network coverage of 77.87% when the number of iteration was 25 after which it kept an almost constant value for the rest of the simulation process.
The relationship between network coverage and the number of mobile nodes also showed that network coverage increased with an increase in mobile nodes.
The approach indicated maximum network coverage of 80.07% when the mobile node was 50. Thereafter, it tended towards stability when the number of network nodes was above 50.
Effects of various attenuation factors on the proposed model were evaluated and simulation results show that the proposed method successfully attains maximum network coverage of 70.58%, 70.99%, 72.69%, and 77.15% when the attenuation factors are 0.75, 0.8,0.85, and 0.90 respectively.
Target tracking simulation scenarios were presented and results showed that the computation energy required to successfully track 30, 45, and 60 targets were 21.63%, 28.003%, and 36.99% less than the energy (time taking) required to track the 15 targets respectively.
INTRODUCTION
Background
Wireless Sensor Networks (WSNs) are increasingly being used for collecting data, such as physical and environmental properties, from a geographical region of interest due to advancements in electronics and wireless communication technology.
WSNs are composed of a large number of tiny, low-power, low-cost sensor nodes which have the ability to sense physical phenomena, process data, and communicate with one another (Alikhani, 2010).
A sensor node is a tiny device that includes four basic components: a sensing subsystem for data acquisition from the physical surrounding environment, a processing subsystem for local data processing and storage, a wireless communication subsystem for data transmission, and a power supply subsystem, which are batteries.
A large number of these wireless sensor nodes are deployed across a geographical region to form a WSN. These WSNs create smart environments by providing access to information regarding the environment through collecting, processing, analyzing, and disseminating data whenever required (Alikhani, 2010).
In order to use WSNs in inaccessible terrains or disaster relief operations, random deployment of the sensor nodes is required.
As a result, the position of these nodes will not be predetermined and thus the nodes must have the ability to collaborate with each other to form self-organized networks in order to perform tasks including but not limited to determining their location (Akyildizet al., 2002).
WSNs have numerous applications, which include environmental monitoring, specifically for planetary exploration, geophysical monitoring, habitat monitoring, oceanography, wildlife tracking, and target tracking.
REFERENCES
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