Travel Route Planning with Optimal Coverage in Difficult Wireless Sensor Network Environment
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...
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MDPI AG
2019-04-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/19/8/1838 |
| _version_ | 1798034589645537280 |
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| author | Yu Gao Jin Wang Wenbing Wu Arun Kumar Sangaiah Se-Jung Lim |
| author_facet | Yu Gao Jin Wang Wenbing Wu Arun Kumar Sangaiah Se-Jung Lim |
| author_sort | Yu Gao |
| collection | DOAJ |
| description | 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). |
| first_indexed | 2024-04-11T20:46:24Z |
| format | Article |
| id | doaj.art-7de88efa35764938aac247ce5fbb8bd4 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-11T20:46:24Z |
| publishDate | 2019-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-7de88efa35764938aac247ce5fbb8bd42022-12-22T04:04:02ZengMDPI AGSensors1424-82202019-04-01198183810.3390/s19081838s19081838Travel Route Planning with Optimal Coverage in Difficult Wireless Sensor Network EnvironmentYu Gao0Jin Wang1Wenbing Wu2Arun Kumar Sangaiah3Se-Jung Lim4College of Information Engineering, Yangzhou University, Yangzhou 225000, ChinaCollege of Information Engineering, Yangzhou University, Yangzhou 225000, ChinaHunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation, School of Computer & Communication Engineering, Changsha University of Science & Technology, Changsha 410000, ChinaSchool of Computing Science and Engineering, Vellore Institute of Technology (VIT), Vellore 632014, IndiaLiberal Arts & Convergence Studies, Honam University, Gwangju 622623624, KoreaIn 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).https://www.mdpi.com/1424-8220/19/8/1838wireless sensor networksmobile devicestravel route planningparticle swarm optimizationant colony optimization |
| spellingShingle | Yu Gao Jin Wang Wenbing Wu Arun Kumar Sangaiah Se-Jung Lim Travel Route Planning with Optimal Coverage in Difficult Wireless Sensor Network Environment Sensors wireless sensor networks mobile devices travel route planning particle swarm optimization ant colony optimization |
| title | Travel Route Planning with Optimal Coverage in Difficult Wireless Sensor Network Environment |
| title_full | Travel Route Planning with Optimal Coverage in Difficult Wireless Sensor Network Environment |
| title_fullStr | Travel Route Planning with Optimal Coverage in Difficult Wireless Sensor Network Environment |
| title_full_unstemmed | Travel Route Planning with Optimal Coverage in Difficult Wireless Sensor Network Environment |
| title_short | Travel Route Planning with Optimal Coverage in Difficult Wireless Sensor Network Environment |
| title_sort | travel route planning with optimal coverage in difficult wireless sensor network environment |
| topic | wireless sensor networks mobile devices travel route planning particle swarm optimization ant colony optimization |
| url | https://www.mdpi.com/1424-8220/19/8/1838 |
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