Research On Perceived MAC Protocol In Wireless Sensor Network

Wireless sensor networks(WSN)is a self-organized network,which is composed by numerous sensors deployed densely and randomly in a geographical scenario,and is used to sense,collect,transmit and process the information collaboratively.It has become an active field of research branch due to its characteristics of miniaturization,flexibility and low energy comsumption.Medium access control(MAC)protocol,which is the key to ensure the normal communication,control the allocation of channel resources.In recent years,many intelligent algorithms have been applied to WSNs to improve network performance.Reinforcement learning,as a branch of intelligent learning,is widely used in wireless sensor networks because of its simplicity and advantage of not requiring environment nodel.In this thesis,the key characteristics of medium access in wireless sensor networks are analyzed and studied.The problem of medium access is transformed into a dynamic discrete Markov decision process by combining the reinforcement learning theory with wireless sensor network.In order to improve the performance of the traditional MAC protocols,Intelligent Slot Selection Algorithm based on TD learning and Fuzzy balancer(TDFISS)and Boltzman’s Q Learning Intelligent Slot Selection Algorithm(BQISS)are proposed in the text.The innovation of TDFISS and BQISS lies in using reinforcement learning method to solve the problem of medium resource utilization.The eligibility trace,fuzzy balancer and the boltzman distribution are introduced to improve the algorithm performance based on traditional TD learning method and Q learning method.Optimize reinforcement learning method with consideration of the characteristics of wireless sensor networks.The adaptability and medium utilization of network has been improved significantly because of self-learning and self-scheduling sensor nodes.The convergence of the TDFISS and BQISS algorithms are theoretically analyzed and its feasibility in wireless sensor networks is proved.These two methods are implemented and verified in the real WSN platforms.The results show that the TDFISS and BQISS have better convergence speed than the ALOHA-Q protocol and better reliability and throughput compared with the S-MAC protocol in its relatively stable stage.The advantage of BQISS are obvious in small-scale networks.In addition,TDFISS algorithm is more suitable for large-scale networks.