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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).
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 2019, 19, 1838 .
AMA StyleYu Gao, Jin Wang, Wenbing Wu, Arun Kumar Sangaiah, Se-Jung Lim. Travel Route Planning with Optimal Coverage in Difficult Wireless Sensor Network Environment. Sensors. 2019; 19 (8):1838.
Chicago/Turabian StyleYu Gao; Jin Wang; Wenbing Wu; Arun Kumar Sangaiah; Se-Jung Lim. 2019. "Travel Route Planning with Optimal Coverage in Difficult Wireless Sensor Network Environment." Sensors 19, no. 8: 1838.
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.
Yu Gao; Jin Wang; Wenbing Wu; Arun Kumar Sangaiah; Se-Jung Lim. A Hybrid Method for Mobile Agent Moving Trajectory Scheduling using ACO and PSO in WSNs. Sensors 2019, 19, 575 .
AMA StyleYu Gao, Jin Wang, Wenbing Wu, Arun Kumar Sangaiah, Se-Jung Lim. A Hybrid Method for Mobile Agent Moving Trajectory Scheduling using ACO and PSO in WSNs. Sensors. 2019; 19 (3):575.
Chicago/Turabian StyleYu Gao; Jin Wang; Wenbing Wu; Arun Kumar Sangaiah; Se-Jung Lim. 2019. "A Hybrid Method for Mobile Agent Moving Trajectory Scheduling using ACO and PSO in WSNs." Sensors 19, no. 3: 575.