An effective Bat algorithm for node localization in distributed wireless sensor network

Abstract

Wireless sensor networks (WSNs) have recently been extensively investigated due to their numerous applications in processes that have to be spread over a large area. One of the important issues of WSN is node localization; node localization capability is highly desirable for the performance evaluation in monitoring applications. Localization is defined as estimating the locations of sensors with initially unknown location information as most sensors do not know their locations due to the cost and size of sensors. The main objective of localization is to find the nodes' positions in a short time with a low energy cost; for this reason, recent approaches relying on swarm intelligence techniques are utilized, and node localization is considered an optimization problem in a multidimensional space. Recently, the meta-heuristic Bat algorithm was proposed as a solution for the node localization problem. This paper proposes an effective Bat algorithm for the node localization problem, the effectiveness of which is based on the adaptation of velocity of the Bats by hybridization, with Doppler effect for improving the performance, aptly termed Dopeffbat. Hence, Dopeffbat computes (through evolution) the nodes' positions iteratively through the Euclidian distance as fitness. Deploying this algorithm on a large WSN with hundreds of sensors demonstrates decent performance in terms of node localization. Moreover, the Dopeffbat parameters are simulated and interpreted in different scenarios of simulation; in addition, a comparative study was performed to further demonstrate the performance of the proposed algorithm, and the simulation results prove that Dopeffbat has a high convergence rate and greater precision compared with the original Bat algorithm and particle swarm optimization (PSO) algorithms.

Survey of Localization for Internet of Things Nodes: Approaches, Challenges and Open Issues

“…It improves the localization precision considerably. An enhanced ultrasound-based time of arrival technique [21] permits calculating the position in three dimensions and provides the data orientation. Cricket, a ToA-based localization approach [22], uses ultrasound transmitters with known locations.…”

WSN Coverage Optimization Based on Two-Stage PSO

“…In addition, biologically heuristic intelligent optimization algorithms have also received extensive attention in this field. Mihoubi [11] proposed an algorithm that uses hybridization to improve the speed of bats and combines the Doppler effect to promote positioning performance. Bin Yu [8] proposed a periodic hybridization method to enhance the global search capability of the PSO algorithm and obtained higher convergence speed and area coverage rate.…”

Obstacle‐resistant hybrid localisation algorithm

“…In addition, this method is a range‐free localisation method and also uses ( M ) ANs as reference nodes to compute the node's position. Mihoubi et al [17] introduced Dopeffbat method, which is a range‐based localisation method for sensor networks and uses ( M ) ANs and Bat algorithms to estimate the UNs positions. In this method, the UNs calculate their positions iteratively and select the Euclidean distance of the UNs and ANs as the fitness function.…”