Autoregressive moving average model as a multi-agent routing protocol for wireless sensor networks  

作　　者：黄如 黄浩 陈志华 何兴勇 

机构地区：[1]School of Information Science and Engineering,East China University of Science and Technology [2]Department of Information Science and Engineering,Xinjiang University

出　　处：《Journal of Beijing Institute of Technology》2011年第3期421-426,共6页北京理工大学学报（英文版）

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)

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 ③prolong the lifetime of networks.

关 键 词：wireless sensor networks （WSN） autoregressive moving average ARMA） MULTIAGENT ROUTING ROBUSTNESS 

分 类 号：TN929.5[电子电信—通信与信息系统] TP212.9[电子电信—信息与通信工程]