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Issue title: Emerging services and technologies in wireless networks
Guest editors: Jin Li, Fatos Xhafa and Jian Weng
Article type: Research Article
Authors: Zhang, Jinga; * | Yang, Tingb | Zhao, Chenglic
Affiliations: [a] Engineering Teaching Practice Training Center, Tianjin Polytechnic University, Tianjin, China. E-mail: drjzhang@126.com | [b] School of Electrical Engineering and Automation, Tianjin University, Tianjin, China | [c] Tianjin Navigation Instruments Research Institute, Tianjin, China
Correspondence: [*] Corresponding author: Jing Zhang, Engineering Teaching Practice Training Center, Tianjin Polytechnic University, Tianjin 300387, China. E-mail: drjzhang@126.com.
Abstract: Wireless sensor networks have received increasing attention in recent years and have proven their capability in dealing with problems occurring in wide areas with difficult access. In other types of sensor networks, the nodes can modify their position to better adapt to changes in the monitored phenomenon. Thus, routing is one of the important issues to be considered for a WSN. In this paper, a distributed algorithm is proposed to ensure that the mean square deviation of the number of member nodes within each cluster is as small as possible, thus effectively balancing the number of member nodes among each cluster. The cluster-based algorithms has proven to be better than multi-hop routing. In this paper, a novel, energy-efficient, cluster-based routing algorithm for a WSN is proposed. To evaluate the efficiency of the proposed algorithm, we studied its performance in a sensor network against other applied algorithms. The simulation results indicate that the network load balancing of the Node Local Density Load Balancing (NLDLB) algorithm is better than that of other join cluster models, and that the Load Balancing and Data Aggregation Tree Routing (LBDATR) algorithm can greatly reduce the energy consumption of a node, thus efficiently extending the lifecycle of the network and enhancing its robustness.
Keywords: Wireless sensor networks, clustering, local density, load balancing, data aggregation tree, routing algorithm
DOI: 10.3233/JHS-150515
Journal: Journal of High Speed Networks, vol. 21, no. 2, pp. 121-129, 2015
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