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Error Minimization in Localization of Wireless Sensor Networks using Fish Swarm Optimization Algorithm - 2017

Error Minimization in Localization of Wireless Sensor Networks using Fish Swarm Optimization Algorithm

Research Area:  Wireless Sensor Networks


Node localization in wireless sensor networks (WSNs) is one of the most important primary requisite that needs to be resolved efficiently as it plays a significant role in many applications namely environmental monitoring, routing and target tracking which is location dependent. Localization is defined as finding the physical co-ordinates of a group of sensor nodes. Localization is classified as an unconstrained optimization problem. Localization protocols are broadly classified as range-based and range-free protocols. The range based protocols employ distance or angle estimation techniques, hardware. The range-free techniques depend on the contents of received messages to support coarse grained accuracy. In this paper, a range-free localization method known as Mobile Anchor Positioning - Mobile Anchor & Neighbor (MAP-M&N) is used to calculate the location of sensor nodes. Mobile Anchor equipped with Global Positioning System (GPS), broadcasts its coordinates to the sensor nodes as it moves through the network. As the sensor nodes collect enough beacons, they are able to calculate their locations. MAP-M&N with Fish Swarm Optimization Algorithm (MAP-M&N with FSO) is the proposed meta-heuristic approach to calculate the location of sensor nodes with minimal error. Root Mean Square Error (RMSE) is used as the performance metric to compare between the two approaches namely, MAP-M&N and MAP-M&N with FSO. Simulation results reveal that MAP-M&N with FSO algorithm is effective to bring down the localization error to a bigger level when compared to using only MAP-M&N algorithm.


Author(s) Name:  S. Sivakumar and Venkatesan

Journal name:  International Journal of Computer Applications

Conferrence name:  

Publisher name:  IJCA

DOI:  10.5120/ijca2017913000

Volume Information:  Volume 159 – No 7, February 2017