Abstract
Localization is one of the most important factors highly desirable for the performance of Wireless Sensor Network (WSN). Localization can be stated as the estimation of the location of the sensor nodes in sensor network. In the applications of WSN, the data gathered at sink node will be meaningless without localization information of the nodes. Due to size and complexity factors of the localization problem, it can be formulated as an optimization problem and thus can be approached with optimization algorithms. In this paper, the nature inspired algorithms are used and analyzed for an optimal estimation of the location of sensor nodes. The performance of the nature inspired algorithms viz. Flower pollination algorithm (FPA), Firefly algorithm (FA), Grey Wolf Optimization (GWO) and Particle Swarm Optimization (PSO) for localization in WSN is analyzed in terms of localization accuracy, number of localized nodes and computing time. The comparative analysis has shown that FPA is more proficient in determining the coordinates of nodes by minimizing the localization error as compared to FA, PSO and GWO.
Highlights
Wireless Sensor Network (WSN) consists of independent similar or diverse types of nodes that monitor the environment
Localization can be defined as determining the coordinates of unknown nodes called as target nodes using the position of known nodes called as anchor nodes or beacons [5] based on the measurements such as Time of arrival (TOA), Time difference of arrival (TDOA), Angle of arrival measurement (AOA), etc
The performance of each localization algorithm is analysed considering other values of the parameters involved in the considered algorithms in terms of localized node ( ), localization error ( )
Summary
Wireless Sensor Network (WSN) consists of independent similar or diverse types of nodes that monitor the environment. The estimation of the position of sensor nodes is one of the important issues of the WSN and is known as localization problem [4]. The analytical methods of optimization like linear programming takes more computation time for solving optimization problems and increase the complexity as the size of problem increases [8] This propelled to use nature inspired optimization algorithms for WSN as these are robust and effective [9]. All the aforementioned factors are considered for the comparison and analysis These range based localization algorithms are compared with each other to determine the proficient algorithm which performs better to solve localization issue. Number of anchor nodes and number of iterations affecting the localization error are considered and analysed for each localization algorithm and compared graphically.
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