A Study Of Energy Model Based On Semi-Markov Chain In Wireless Sensor Network

With the development of System on Chip (SOC), Micro-Electro-Mechanism System (MEMS) and wireless communication technology, Wireless Sensor Network (WSN) is widely applied to different kinds of situation. WSN is composed of a large amount of self-organized nodes and these nodes coordinate to monitor and sense environment data. The data then congregate to the sink node using type of multi-hop communication.The most notable characteristic of WSN which different from other networks is its limited energy. Due to a huge number of nodes existing in the network and most of them are always distributed in the harsh situation, so it is impossible to replace the battery of node. When nodes'energy is extinct, it is possible that the whole network will be paralyzed. Therefore, energy consumption problem of WSN is a hot spot continuously and how to utilize the node energy efficiently is always the key issue to be solved.To be a kind of approach to measure the energy consumption of WSN, the main purpose of energy model is to analyze and solve the energy utilization through model; then build the relative energy topology map to monitor the energy consumption of network. The purpose of this thesis is to build an energy model with great precision and study on energy consumption issue of WSN. The main research results are as follows:⑴Analyze the importance of building energy model and summarize the resent energy model. Then analyze the disadvantage of these models.⑵Analyze the energy consumption of WSN from node aspect and network aspect. Summarize that the main energy consumption of node hardware comes from wireless communication module and the main energy consumption of node software comes from MAC layer and Network layer of protocol; the energy consumption of network aspect is resulted from data collision rate which caused by hidden terminal. From analyzing causes of energy consumption, we can lay the foundation of building a model.⑶Separate the node's operation mode into four statuses and use the semi-markov chain as a mathematic model; Then build the node's matrix of transition probability. As the time tends to infinity, we can get node's probability of stability in four statuses. Based on these, analyze the characteristics of data flow of WSN and build data flow model; then combine node's probability of stability with data flow model to set up a new energy model.⑷Construct the simulation environment and simulate the new energy model. From the result we can get that the remaining energy which is calculated by semi-markov chain energy model is different from actual remaining energy with 0.175J (initial energy value of node is 10J). The value is accord with application demands. Based on the above analysis, we simulate model rebuild thresholdξand find that with diminish ofξ, the precision of energy model increases but the extra energy consumption of network also increases; whenξ<10%, congestion happens in the network which causes the sharply rising of energy consumption of network.⑸Graph the remaining energy topology gray map of network based on semi-markov chain energy model; And analyze the character and cause of the remaining energy distribution.