Research On Self - Privacy Behavior Detection Of MAC Layer In Wireless Sensor Networks

With the development of WSN network, selfish users in wireless sensor networks take advantages of other users by change network protocols, contrary to random competition mechanism. This paper studies the detection mechanism IEEE 802.15.4 MAC layer in wireless sensor networks selfish behavior.In this paper, the main research work is as follows:First this paper researched selfish behavior is how to effect the performance of the entire network. Adopt the method of modified IEEE 802.15.4 protocol get selfish behavior simulation results of the impact of network performance.Then aimed at the IEEE 802.15.4 wireless sensor network node selfish behavior, this paper presents an Improved Density-based Clustering Detection Methods (ID-BCDM). NS2 simulation results show that the nodes selfish behavior of the IEEE 802.15.4 Wireless Sensor Networks can effectively and quickly detect by improved algorithm. The algorithm for each node throughput, delay, packet loss rate as analysis data. In order the detection of selfish nodes more effective, the NPCS-CDM algorithm of the initial clustering center is improved.NS2 simulation results show that the improved algorithm can detect whether there is a selfish behavior in the network, also can be a very good distinguish between normal and selfish nodes.Moreover, this paper propose a selfish behavior detection (Improved Artificial Immune Algorithm, IAIA) system for the detection of selfish behavior. The system consists of preprocessing module, congenital detection module, detection module and a processing modules. the access delay is calculated by Pre-processing module, and then the delay is analyzed and compared by detection module for abnormal behaviors. The processing module is processed all unusual circumstances. In this paper, the acquired detection module of immature detector generation algorithm, mature detector generation algorithm is improved, the influence of tolerance time of immature detector on the detection performance, the influence of the number of mature detector on the detection,the number of consecutive matches in n affect the performance of detection are analyzed in the simulation, Processing module is responsible for handling detector detect abnormalities, and the number of abnormal threshold effect on the performance of detection are analyzed in the simulation. Simulation results show that:after appropriately selecting the parameters of the detection mechanism, it can significantly improve the detection rate and reduce the rate of false positives rate, so as to achieve the purpose of improving the overall network...