VCDC: An Efficient Trust Value Collection Mechanism for Trust Management in MANET
管理學院 - 資訊管理系
Department of Information Management
|Thesis Publication Year:||2011|
|Graduation Academic Year:||99|
|Keywords (in Chinese):||分群 、資料蒐集 、信任值管理 、隨意網路|
|Keywords (in other languages):||Clustering, Information collection, Trust management framework, Mobile ad hoc networks|
|Reference times:||Clicks: 102 Downloads: 0|
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As the world modernizes nowadays, change of the society, and the popular use of mobile internet device by almost every human being. Consequently, there are more and more user nodes take part in mobile ad hoc networks. On the other hand, the security problems of mobile ad hoc networks have been suffering from a severe challenge. However, security methods used to deal with traditional problems can not directly be used on MANETs environment. It is obviously that people need a robust system to guard networks against wicked attacks. Then, how to build trust among nodes is the key to prevent the adversary from launching a successful attack.
In recent years, trust management framework has drawn many researchers’ attentions to this field. Trust management framework provides a high-level consideration of security policy and management, and this framework is used comprehensively for misbehavior detection, secure routing protocol, and key management and extends to access control, e-commerce, data aggregation, and even researching potential threats in mobile ad hoc networks. In trust management framework, node requires absorbing the other nodes’ opinions to improve the robustness of this framework. However, nodes periodically exchange their direct observation to each others, which consumes more bandwidth even generates a traffic jam. Whereas, it is not objective when node only adopts its neighbors’ direct observation, and narrows the detection rate of misbehaved nodes.
In this thesis, we present a new method called virtual cluster data collection method (VCDC) for reactive collecting second-hand information rapidly in trust management framework. At first, user node sorts neighbor nodes according to the node density they observed with a common ways. Next, calculate covariance value between two nodes in the sorting order, then according to covariance value, dividing neighbor nodes into k groups. Last, choose an inheritor from a specific group to take over the following works. We also conduct simulations based on three key factors, and it shows that our method is very suitable for high-density mobile ad hoc networks and will be a powerful tool to assist nodes gathering useful information.
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