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Travel Route Planning with Optimal Coverage in Difficult Wireless Sensor Network Environment

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
Liberal Arts & Convergence Studies, Honam University, Gwangju 622623624, Korea
*
Author to whom correspondence should be addressed.
Sensors 2019, 19(8), 1838; https://doi.org/10.3390/s19081838
Received: 21 March 2019 / Revised: 12 April 2019 / Accepted: 16 April 2019 / Published: 17 April 2019
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Abstract

In recent years, wireless sensor networks (WSNs) have been widely applied to sense the physical environment, especially some difficult environment due to their ad-hoc nature with self-organization and local collaboration characteristics. Meanwhile, the rapid development of intelligent vehicles makes it possible to adopt mobile devices to collect information in WSNs. Although network performance can be greatly improved by those mobile devices, it is difficult to plan a reasonable travel route for efficient data gathering. In this paper, we present a travel route planning schema with a mobile collector (TRP-MC) to find a short route that covers as many sensors as possible. In order to conserve energy, sensors prefer to utilize single hop communication for data uploading within their communication range. Sojourn points (SPs) are firstly defined for a mobile collector to gather information, and then their number is determined according to the maximal coverage rate. Next, the particle swarm optimization (PSO) algorithm is used to search the optimal positions for those SPs with maximal coverage rate and minimal overlapped coverage rate. Finally, we schedule the shortest loop for those SPs by using ant colony optimization (ACO) algorithm. Plenty of simulations are performed and the results show that our presented schema owns a better performance compared to Low Energy Adaptive Clustering Hierarchy (LEACH), Multi-hop Weighted Revenue (MWR) algorithm and Single-hop Data-gathering Procedure (SHDGP). View Full-Text
Keywords: wireless sensor networks; mobile devices; travel route planning; particle swarm optimization; ant colony optimization wireless sensor networks; mobile devices; travel route planning; particle swarm optimization; ant colony optimization
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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MDPI and ACS Style

Gao, Y.; Wang, J.; Wu, W.; Sangaiah, A.K.; Lim, S.-J. Travel Route Planning with Optimal Coverage in Difficult Wireless Sensor Network Environment. Sensors 2019, 19, 1838.

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