Abstract
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.
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Li, J., Liu, R., Yu, R., Wang, X., Zhao, Z. (2014). Reputation-Based Participant Incentive Approach in Opportunistic Cognitive Networks. In: Wu, J., Chen, H., Wang, X. (eds) Advanced Computer Architecture. Communications in Computer and Information Science, vol 451. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-44491-7_16
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DOI: https://doi.org/10.1007/978-3-662-44491-7_16
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