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A Hybrid Method for Mobile Agent Moving Trajectory Scheduling using ACO and PSO in WSNs

1
College of Information Engineering, Yangzhou University, Yangzhou 225000, China
2
Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation, School of Computer & Communication Engineering, Changsha University of Science & Technology, Changsha 410000, China
3
School of Information Science and Engineering, Fujian University of Technology, Fuzhou 350000, China
4
School of Computing Science and Engineering, Vellore Institute of Technology (VIT), Vellore 632014, India
5
Department of Computer Engineering, Chonnam National University, Yeosu 596597, Korea
*
Author to whom correspondence should be addressed.
Sensors 2019, 19(3), 575; https://doi.org/10.3390/s19030575
Received: 1 January 2019 / Revised: 26 January 2019 / Accepted: 28 January 2019 / Published: 30 January 2019
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Abstract

Wireless Sensor Networks (WSNs) are usually troubled with constrained energy and complicated network topology which can be mitigated by introducing a mobile agent node. Due to the numerous nodes present especially in large scale networks, it is time-consuming for the collector to traverse all nodes, and significant latency exists within the network. Therefore, the moving path of the collector should be well scheduled to achieve a shorter length for efficient data gathering. Much attention has been paid to mobile agent moving trajectory panning, but the result has limitations in terms of energy consumption and network latency. In this paper, we adopt a hybrid method called HM-ACOPSO which combines ant colony optimization (ACO) and particle swarm optimization (PSO) to schedule an efficient moving path for the mobile agent. In HM-ACOPSO, the sensor field is divided into clusters, and the mobile agent traverses the cluster heads (CHs) in a sequence ordered by ACO. The anchor node of each CHs is selected in the range of communication by the mobile agent using PSO based on the traverse sequence. The communication range adjusts dynamically, and the anchor nodes merge in a duplicated covering area for further performance improvement. Numerous simulation results prove that the presented method outperforms some similar works in terms of energy consumption and data gathering efficiency. View Full-Text
Keywords: wireless sensor network; mobile agent; ant colony optimization; particle swarm optimization; moving trajectory wireless sensor network; mobile agent; ant colony optimization; particle swarm optimization; moving trajectory
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Gao, Y.; Wang, J.; Wu, W.; Sangaiah, A.K.; Lim, S.-J. A Hybrid Method for Mobile Agent Moving Trajectory Scheduling using ACO and PSO in WSNs. Sensors 2019, 19, 575.

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