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Issue title: Artificial Intelligent Techniques and its Applications
Guest editors: Mahalingam Sundhararajan, Xiao-Zhi Gao and Hamed Vahdat Nejad
Article type: Research Article
Authors: Wang, Honglina; b; c; * | JunShen, a; b | Wang, Xinleid
Affiliations: [a] School of Computer Science and Engineering of Southeast University, Jiangsu Nanjing, China | [b] Key Laboratory of Computer Network and Information Integration of Southeast University, Jiangsu Nanjing, China | [c] School of Electronic and Information Engineering of Nanjing University of Information Science and Technology, Jiangsu Nanjing, China | [d] Binjiang College, Nanjing University of Information Science and Technology, Jiangsu Nanjing, China
Correspondence: [*] Corresponding author. Honglin Wang. E-mail: xn942686011a@163.com.
Abstract: Wireless Sensor Networks (WSNs) are widely-used. The nodes in traditional WSNs are usually fixed, which means that the nodes around the base stations will bear more communication load. This uneven load will cause quick exhaustion of network energy and the phenomenon of “Energy Hole”. In recent years, researchers have been trying to solve this problem by introducing moving nodes that are considered as the sink nodes (Sink) to collect the data in WSNs. A moving node can be moving tramcars, moving cars on bridge or autonomous underwater vehicles, etc. However, in reality, the moving nodes have certain constrains on both space and capacity. In this paper, the network problems such as the node energy, the sink movement distance upper limit and the communication loss, are formally modeled as linear programming models. Thus, the optimal sink moving path can be determined by solving these models. Experiments show that the models can be applied to small and medium size WSNs with acceptable running time. However, the running time becomes extremely long when the network size is scaled up.
Keywords: WSNs, mixed linear programming, moving nodes, optimal path
DOI: 10.3233/JIFS-169377
Journal: Journal of Intelligent & Fuzzy Systems, vol. 34, no. 2, pp. 839-848, 2018
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