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Research on Critical Nodes Detection Algorithm Based on Node Stability Prediction in Ad Hoc Network

Author: WuBin
Tutor: ShaYi
School: Northeastern University
Course: Communication and Information System
Keywords: Ad Hoc Networks Critical Nodes Node Stability Grey Model Prediction
CLC: TN929.5
Type: Master's thesis
Year: 2011
Downloads: 72
Quote: 0
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Abstract


The critical nodes, whose removal will disconnect the network into two or more separate components in the Ad Hoc network, will play an important role in wireless Ad Hoc networks. The critical node is the heaviest load in the network, due to large volume of business, resulting in excessive energy consumption of the nodes will make the critical node failure, which led to the network is divided. Therefore, fast and accurate detection of critical nodes in the network, is important to the network connectivity and service quality.To better adapt to the dynamic characteristics of Ad Hoc network topology structure, NS-PMRC (Node Stablility algorithm of Midpoint Range Circle) is proposed. Grey model is a model which needs to transform the original data to find the rule, then the unknown data can be predicted. The geographical position the next moment of the node can be predicted according to the established grey model and the node location information provided by GPS system, then the distance between nodes can be computed and the node stability the next moment can be predicted. By comparing the stability between nodes to determine the conditions of existence of key nodes,being aware of a future disconnection in the network can help to ensure the network run well. Network services could benefit from a reliable partition detection mechanism.Based on the detailed instructions to NS-PMRC proposed, using NS2, PMRC and NS-PMRC are simulated and compared respectively from three different view, such as changing the network size, changing the node maximum speed and changing the density of the nodes. Simulation results show that, compared to PMRC,NS-PMRC improved the accuracy of detection, then the network performance is improved significantly.

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