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学术报告 —— How to Integrate Selfishness in Future Internet

日期:2011年07月13日 编辑: 点击:

报告题目:How to integrate selfishness in future Internet

报告人:Kavé Salamatian(Professor,University of Savoie,French)

报告时间:2011年7月17日10:00

报告地点:学院会议室(望江校区基础教学大楼B314)

Abstract

The past years have seen the emergence of new scenarios in networks: sensor nets, DTNs, social networks, etc. All these interesting and challenging cases have in common that the traditional forwarding paradigm, that is "store and forward," is not really applicable. We will present in this talk a framework englobing all different forwarding paradigm in the above networks as well as classical networks. In particular, the framework integrates selfish nodes and provides a way to deal with incentives to cooperate in information diffusion in a network. It provide therefore a interesting framework to analyze future Internet.

We will illustrate this with an application where nodes have to exchange data to extend their local view of traffic to a global view to enable distributed anomaly detection. However their local information is correlated with their neighbors' information. Moreover, a node that is selfish wants to get the best glance on other nodes' state while giving away as little information on its own state as possible . Meaning that an incentive/punishment cooperative mechanism is needed to deal with node selfishness. Last but not least we assume that the level of precision needed about node states is not a priori known and it needs to be defined online during the operation of the system. We will show that the framework presented in the first part of the talk provide a communication scheme that addresses the above described challenges, i.e., an information exchange scheme that enables the extension of local to global view, respecting the node selfishness (expressed in terms of amount of data (in bits) that is exchanged with others) and the level of precision it is obtaining from other nodes. This will show that the new framework open new way for innovative approach in information diffusion in networks.