Critical Node Estimation Based Onmultiple Attribute Decision Making Foropportunistic Sensor Networks

Opportunistic Sensor Network is self-organization sensor network,which does not need a complete path between source node and destination node. Its communication is achieved by the meeting chance of mobile nodes. And it is often used in wildlife tracking, forest environment monitoring and intelligent transportation,etc.. The failure of the critical nodes may lead to the network abnormal, or even crashed. If critical nodes can be known or predicted, the network can be optimized according to the relevant information of the critical nodes. Furthermore, maintenance time and cost of network can be dramatically reduced by checking the critical nodes at the first time when the network is crashed.The project comes from the National Natural Science Foundation of China, and studies prediction method of the critical nodes for Opportunistic Sensor Networks.This thesis introduces the state of the art of the critical nodes at home and abroad. It analyzes the advantages and disadvantages and the application range of the existing critical node decision method, and points out that these methods are not suitable for the Opportunistic Sensor Networks. According to the characteristics of high latency and dynamic change of network topology, the thesis analyzes the message transfer process of Multi-region Opportunistic Sensor Networks with hierarchical model. The stage contribution is defined to reflect the contribution of the nodes in the process of message transmission, and the region contribution is defined to reflect the contribution of nodes for regions. And on this basis, in terms of the comprehensive contribution of nodes, the prediction method of critical nodes is put forward, which is based on Multiple Attribute Decision Making-- Technique for Order Preference by Similarity to Ideal Solution(TOPSIS).Opportunistic Networking Environment(ONE) is employed to simulate four topical multi-region opportunistic sensor network scenarios. TOPSIS and improved TOPSIS algorithms are employed to predict critical nodes. The experiment results show that the prediction method with improved TOPSIS algorithms achieves more accuracy. Furthermore, in order to validate the proposed method, test bed is established. The experiment results show that the prediction method with improved TOPSIS algorithms achieves more accuracy as well.