Abstract
Internet of Things (IOT) has found broad applications and has drawn more and more attention from researchers. At the same time, IOT also presents many challenges, one of which is node localization, i.e. how to determine the geographical position of each sensor node. Algorithms have been proposed to solve the problem. A popular algorithm is Particle Swarm Optimization (PSO) because it is simple to implement and needs relatively less computation. However, PSO is easily trapped into local optima and gives premature results. In order to improve the PSO algorithm, this paper proposes the EHPSO algorithm based on Novel Particle Swarm Optimization (NPSO) and Hybrid Particle Swarm Optimization (HPSO). The EHPSO algorithm applies the principle of best neighbor of each particle to the HPSO algorithm. Simulation results indicate that EHPSO outperforms HPSO and NPSO in evaluating accurate node positions and improves convergence by avoiding being trapped into local optima.
Highlights
Consisting of two phases is introduced in [1]
An IoT-related Hybrid Particle Swarm Optimization (HPSO) is put forth based on Novel Particle Swarm Optimization (NPSO) proposed in [13]
Based on the aforementioned descriptions, the pseudo-code of the EHPSO algorithm are provided as follows: (1) Initialization: A series of parameters should be initialized in advance, including a population of M particles with random position !! within the transmission range, the maximum iteration, random best position of the i!th particle !!!"#$% and the best position of the whole swarm !!"#$!!!
Summary
The principals accomplishing IoT node localization utilize the a priori information of anchor nodes to determine the approximate or accurate coordinates of the target nodes. !!!; the coordinate of the target node to be determined is !!! Between the ith anchor node and target node can be calculated as. The coordinates of the target nodes can be calculated as follows:. Is the value of !!, the fitness function of PSO can be described as follows:. If children’s fitness function value is lower than parent’s, child particles will replace parent particles and a new swarm is achieved. Whereas if children’s fitness function value is larger than parent’s, that is to say, ! !!!!"#!!"!, HPSO is no longer an efficient approach to solve the local optimum in that circumstance. The following method is provided to solve the problem
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 callMore From: International Journal of Online and Biomedical Engineering (iJOE)
Disclaimer: 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.
