Reputation-Based Participant Incentive Approach in Opportunistic Cognitive Networks
Sufficient reputable participants are critical to effective data collections and data disseminations in opportunistic cognitive networks.However,it is difficult to identify reputable or malicious participants in opportunistic networks.Cognitive network technology can be applied to the communication system of opportunistic networks to provide reputation-aware schemes of the participants.Furthermore,keeping participants enthusiasm for activities of networks is also important.In this work,we propose a Reputation-Based Participant Incentive Approach (RBPIA) to motivate reputable participants.RBPIA scores participants using reputation degree according to their sensing data and bid price respectively and encourage them to keep interested in the activities with rewards.Simulations are performed in different scenarios to evaluate efficiency of the approach.The results show that RBPIA can identify participant types well,and remarkably reduce the incentive cost.
opportunistic cognitive networks reputation incentive bid price collusion multidimensional reverse auction
Jie Li Rui Liu Ruiyun Yu Xingwei Wang Zhijie Zhao
Computing Center,Northeastern University,Shenyang,China;Key Lab of Network Control Systems,Chinese A Department of Computing,Hong Kong Polytechnic University,Hong Kong Software College,Northeastern University,Shenyang,China College of Information Sci.and Eng.,Northeastern University,Shenyang,China Info.and Tech.Center of China Mobile Group Liaoning Co.,LTD
国际会议
ACA,Advanced Computer Architecture(2014年全国计算机体系结构学术会议)
沈阳
英文
201-214
2014-08-23（万方平台首次上网日期，不代表论文的发表时间）