scholarly journals Quasi-Affine Transformation Evolutionary Algorithm With Communication Schemes for Application of RSSI in Wireless Sensor Networks

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 8583-8594 ◽  
Author(s):  
Zhi-Gang Du ◽  
Jeng-Shyang Pan ◽  
Shu-Chuan Chu ◽  
Han-Jiang Luo ◽  
Pei Hu
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Evolutionary Algorithm ◽  
Affine Transformation ◽  
Wireless Sensor

scholarly journals A novel pigeon-inspired optimization with QUasi-Affine TRansformation evolutionary algorithm for DV-Hop in wireless sensor networks

2020 ◽  
Vol 16 (6) ◽  
pp. 155014772093274 ◽  
Author(s):  
Xiao-Xue Sun ◽  
Jeng-Shyang Pan ◽  
Shu-Chuan Chu ◽  
Pei Hu ◽  
Ai-Qing Tian
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Evolutionary Algorithm ◽  
Optimization Algorithm ◽  
Affine Transformation ◽  
Optimal Solution ◽  
Wireless Sensor ◽  
Local Optimum ◽  
Search Range ◽  
Homing Behavior

In modern times, swarm intelligence has played an increasingly important role in finding an optimal solution within a search range. This study comes up with a novel solution algorithm named QUasi-Affine TRansformation-Pigeon-Inspired Optimization Algorithm, which uses an evolutionary matrix in QUasi-Affine TRansformation Evolutionary Algorithm for the Pigeon-Inspired Optimization Algorithm that was designed using the homing behavior of pigeon. We abstract the pigeons into particles of no quality and improve the learning strategy of the particles. Having different update strategies, the particles get more scientific movement and space exploration on account of adopting the matrix of the QUasi-Affine TRansformation Evolutionary algorithm. It increases the versatility of the Pigeon-Inspired Optimization algorithm and makes the Pigeon-Inspired Optimization less simple. This new algorithm effectively improves the shortcoming that is liable to fall into local optimum. Under a number of benchmark functions, our algorithm exhibits good optimization performance. In wireless sensor networks, there are still some problems that need to be optimized, for example, the error of node positioning can be further reduced. Hence, we attempt to apply the proposed optimization algorithm in terms of positioning, that is, integrating the QUasi-Affine TRansformation-Pigeon-Inspired Optimization algorithm into the Distance Vector–Hop algorithm. Simultaneously, the algorithm verifies its optimization ability by node location. According to the experimental results, they demonstrate that it is more outstanding than the Pigeon-Inspired Optimization algorithm, the QUasi-Affine TRansformation Evolutionary algorithm, and particle swarm optimization algorithm. Furthermore, this algorithm shows up minor errors and embodies a much more accurate location.

scholarly journals An Adaptation Multi-Group Quasi-Affine Transformation Evolutionary Algorithm for Global Optimization and Its Application in Node Localization in Wireless Sensor Networks

Sensors ◽  
2019 ◽  
Vol 19 (19) ◽  
pp. 4112 ◽  
Author(s):  
Nengxian Liu ◽  
Jeng-Shyang Pan ◽  
Jin Wang ◽  
Trong-The Nguyen
Keyword(s):  
Wireless Sensor Networks ◽  
Global Optimization ◽  
Sensor Networks ◽  
Evolutionary Algorithm ◽  
Affine Transformation ◽  
Optimization Problems ◽  
Population Diversity ◽  
Wireless Sensor ◽  
Node Localization ◽  
Mutation Strategy

Developing metaheuristic algorithms has been paid more recent attention from researchers and scholars to address the optimization problems in many fields of studies. This paper proposes a novel adaptation of the multi-group quasi-affine transformation evolutionary algorithm for global optimization. Enhanced population diversity for adaptation multi-group quasi-affine transformation evolutionary algorithm is implemented by randomly dividing its population into three groups. Each group adopts a mutation strategy differently for improving the efficiency of the algorithm. The scale factor F of mutations is updated adaptively during the search process with the different policies along with proper parameter to make a better trade-off between exploration and exploitation capability. In the experimental section, the CEC2013 test suite and the node localization in wireless sensor networks were used to verify the performance of the proposed algorithm. The experimental results are compared results with three quasi-affine transformation evolutionary algorithm variants, two different evolution variants, and two particle swarm optimization variants show that the proposed adaptation multi-group quasi-affine transformation evolutionary algorithm outperforms the competition algorithms. Moreover, analyzed results of the applied adaptation multi-group quasi-affine transformation evolutionary for node localization in wireless sensor networks showed that the proposed method produces higher localization accuracy than the other competing algorithms.

scholarly journals Bioinspired Evolutionary Algorithm Based for Improving Network Coverage in Wireless Sensor Networks

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Mohammadjavad Abbasi ◽  
Muhammad Shafie Bin Abd Latiff ◽  
Hassan Chizari
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Evolutionary Algorithm ◽  
Sensor Node ◽  
Base Station ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Target Area ◽  
Network Coverage ◽  
Detection Model

Wireless sensor networks (WSNs) include sensor nodes in which each node is able to monitor the physical area and send collected information to the base station for further analysis. The important key of WSNs is detection and coverage of target area which is provided by random deployment. This paper reviews and addresses various area detection and coverage problems in sensor network. This paper organizes many scenarios for applying sensor node movement for improving network coverage based on bioinspired evolutionary algorithm and explains the concern and objective of controlling sensor node coverage. We discuss area coverage and target detection model by evolutionary algorithm.

scholarly journals A Self-organizing Wireless Sensor Networks Based on Quantum Ant Colony Evolutionary Algorithm

2017 ◽  
Vol 13 (07) ◽  
pp. 69 ◽  
Author(s):  
Lin-lin Wang ◽  
Chengliang Wang
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Evolutionary Algorithm ◽  
Ant Colony Algorithm ◽  
Ant Colony ◽  
Wireless Sensor ◽  
Target Coverage ◽  
Dynamic Adjustment ◽  
Coverage Problem ◽  
Self Organizing

<p><span style="font-size: medium;"><span style="font-family: 宋体;">Aiming at the coverage problem of self-organizing wireless sensor networks, a target coverage method for wireless sensor networks based on Quantum Ant Colony Evolutionary Algorithm (QACEA) is put forward. This method introduces quantum state vector into the coding of ant colony algorithm, and realizes the dynamic adjustment of ant colony through quantum rotation port. The simulation results show that the quantum ant colony evolutionary algorithm proposed in this paper can effectively improve the target coverage of wireless sensor networks, and has obvious advantages compared with the other two methods in detecting the number of targets and the convergence speed. Based on the above findings, it is concluded that the algorithm proposed plays an essential role in the improvement of target coverage and it can be widely used in the similar fields, which has great and significant practical value.</span></span></p>

Sign in / Sign up

Export Citation Format

Share Document