Abstract
Aiming at the difficulty of deploying wireless sensor networks (WSNs) on three-dimensional (3D) surfaces, based on the grey wolf optimizer (GWO), an enhanced version of the grey wolf optimizer is proposed for deploying WSNs on 3D surfaces, namely the enhanced grey wolf optimizer (EGWO), which is characterized by enhanced exploitation and exploration ability of the algorithm. The novelty of EGWO is that the grey wolf population is divided into two parts, one part is responsible for the outer-layer encircle and the other is responsible for the inner-layer encircle, and the introduction of Tent mapping. The purpose of this is to enhance the exploitation and exploration ability of the algorithm respectively, so as to improve the convergence and optimization precision of the algorithm. In addition, in terms of WSN deployment in 3D surfaces, this paper improves the means of determining the perceived blind zone. Meanwhile, a novel method to calculate the WSNs coverage area of simple and complex 3D surfaces is presented by combining the grid and integral of the 3D surfaces. The EGWO is favorably compared with the GWO and three existing variants of the grey wolf optimizer when testing on 12 well-known benchmark functions. The simulation experiment results show that compared with the existing algorithms, EGWO can provide a very competitive search result in terms of optimization precision and convergence performance. Finally, this paper applies EGWO to the 3D surface deployment of WSN. Simulations show that compared with the other three deployment algorithms, EGWO can improve the network coverage of WSN, which can save network deployment costs. In addition, the probability of network connectivity deployed by EGWO is higher, that is, EGWO can provide a better deployment solution.
Highlights
With the development of 5G communication technology and the Internet of Things, wireless sensor networks (WSNs) have been widely used in the fields of medical health, smart home, underwater monitoring, and military operations [1]–[4]
5) In order to verify the effectiveness of enhanced grey wolf optimizer (EGWO) deploying sensor networks on 3D surfaces, our work will be compared with two existing 3D deployment methods, i.e., ABC-DSS in [28] and differential evolution algorithm (DEA) in [29]
WORK The main work of this paper is to propose an EGWO based on the grey wolf optimizer (GWO), which is used to deploy a WSN on a 3D surface
Summary
With the development of 5G communication technology and the Internet of Things, wireless sensor networks (WSNs) have been widely used in the fields of medical health, smart home, underwater monitoring, and military operations [1]–[4]. In order to obtain good optimization precision, some scholars have proposed a hybrid grey wolf optimizer algorithm [47], [48] that combines other optimization algorithms with the grey wolf optimizer algorithm, but new algorithms are often computationally complex Operations such as chaotic mapping, crossover, and mutation [49], [50] play a role in GWO variants. In terms of deploying wireless sensor networks on 3D surfaces, swarm intelligence optimization algorithms have demonstrated the advantages of simplicity, ease of use, and no need of special modeling [27], compared with other deployment strategies. 5) In order to verify the effectiveness of EGWO deploying sensor networks on 3D surfaces, our work will be compared with two existing 3D deployment methods, i.e., ABC-DSS (artificial bee colony algorithm with dynamic search strategy) in [28] and DEA (differential evolution algorithm) in [29].
Sign-in/Register to view highlights & summary 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.
