Abstract
Nodes localization in a wireless sensor network (WSN) aims for calculating the coordinates of unknown nodes with the assist of known nodes. The performance of a WSN can be greatly affected by the localization accuracy. In this paper, a node localization scheme is proposed based on a recent bioinspired algorithm called Salp Swarm Algorithm (SSA). The proposed algorithm is compared to well-known optimization algorithms, namely, particle swarm optimization (PSO), Butterfly optimization algorithm (BOA), firefly algorithm (FA), and grey wolf optimizer (GWO) under different WSN deployments. The simulation results show that the proposed localization algorithm is better than the other algorithms in terms of mean localization error, computing time, and the number of localized nodes.
Highlights
Wireless sensor networks (WSNs) are networks that consist of a few autonomous sensor nodes that are distributed in a specific area for cooperatively sensing their environment
E main contribution of this paper is using the Salp Swarm Algorithm (SSA) for the first time ever to localize the nodes of WSNs. e performance of the proposed SSA-based localization algorithm is analyzed and compared with particle swarm optimization (PSO), butterfly optimization algorithm (BOA), firefly algorithm (FA), and grey wolf optimizer (GWO) algorithms. e results have shown that the SSAbased localization algorithm is better than the previously mentioned localization algorithms in terms of computing time, number of localized nodes, and localization accuracy
To the best of our knowledge, the Salp Swarm Algorithm (SSA) algorithm was never used for the localization problem in WSNs so far. erefore, the main objective of this paper is to employ the SSA algorithm for handling the localization problem in WSNs and is to evaluate its performance against several well-known swarm intelligence algorithms. e basic ideas behind the SSA and other swarm algorithms are given
Summary
Wireless sensor networks (WSNs) are networks that consist of a few autonomous sensor nodes that are distributed in a specific area for cooperatively sensing their environment. Using the known locations of anchor nodes, localization algorithms can be employed to estimate the positions of the unknown nodes [2]. Anglebased or point-to-point distance estimation among the sensor nodes is used with rang-based localization algorithms. In these algorithms, the positions of sensor nodes are calculated by the assist of anchor’s trilateration [3]. Unlike range-based localization algorithms, range-free localization algorithms do not need range information to estimate the positions of the unknown nodes. E main contribution of this paper is using the Salp Swarm Algorithm (SSA) for the first time ever to localize the nodes of WSNs. e performance of the proposed SSA-based localization algorithm is analyzed and compared with particle swarm optimization (PSO), butterfly optimization algorithm (BOA), firefly algorithm (FA), and grey wolf optimizer (GWO) algorithms.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
