文献类型：期刊文献

中文题名：Autoregressive moving average model as a multi-agent routing protocol for wireless sensor networks

英文题名：Autoregressive moving average model as a multi-agent routing protocol for wireless sensor networks

作者：Huang, Ru[1]; Huang, Hao[2]; Chen, Zhi-Hua[1]; He, Xing-Yong[1]

机构：[1] School of Information Science and Engineering, East China University of Science and Technology, Shanghai 200237, China; [2] Department of Information Science and Engineering, Xinjiang University, Urumqi 830046, China

年份：2011

卷号：20

期号：3

起止页码：421

中文期刊名：Journal of Beijing Institute of Technology

外文期刊名：Journal of Beijing Institute of Technology (English Edition)

收录：EI(收录号：20114314457803);Scopus

基金：Supported by the National Natural Science Foundation of China(60802005,60965002,50803016);Science Foundation forthe Excellent Youth Scholars at East China University of Science and Technology(YH0157127);Undergraduate Innovational Experimentation Program in ECUST(X1033)

语种：英文

中文关键词：wireless sensor networks （WSN）; autoregressive moving average ARMA）; multiagent; routing ; robustness

外文关键词：Power management (telecommunication) - Energy efficiency - Multi agent systems - Routing protocols - Ant colony optimization - Gaussian noise (electronic)

摘要：A prediction-aided routing algorithm based on ant colony optimization mode （PRACO） to achieve energy-aware data-gathering routing structure in wireless sensor networks （WSN） is presented. We adopt autoregressive moving average model （ARMA） to predict dynamic tendency in data traffic and deduce the construction of load factor, which can help to reveal the future energy status of sensor in WSN. By checking the load factor in heuristic factor and guided by novel pheromone updating rule, multi-agent, i. e. , artificial ants, can adaptively foresee the local energy state of networks and the corresponding actions could be taken to enhance the energy efficiency in routing construction. Compared with some classic energy-saving routing schemes, the simulation results show that the proposed routing building scheme can ① effectively reinforce the robustness of routing structure by mining the temporal associability and introducing multi-agent optimization to balance the total energy cost for data transmission, ② minimize the total communication consumption, and ③prolong the lifetime of networks.
A prediction-aided routing algorithm based on ant colony optimization mode (PRACO) to achieve energy-aware data-gathering routing structure in wireless sensor networks (WSN) is presented. We adopt autoregressive moving average model (ARMA) to predict dynamic tendency in data traffic and deduce the construction of load factor, which can help to reveal the future energy status of sensor in WSN. By checking the load factor in heuristic factor and guided by novel pheromone updating rule, multi-agent, i.e., artificial ants, can adaptively foresee the local energy state of networks and the corresponding actions could be taken to enhance the energy efficiency in routing construction. Compared with some classic energy-saving routing schemes, the simulation results show that the proposed routing building scheme can (1) effectively reinforce the robustness of routing structure by mining the temporal associability and introducing multi-agent optimization to balance the total energy cost for data transmission, (2) minimize the total communication consumption, and (3) prolong the lifetime of networks. ? Copyright.