4.7 Article

Secure Routing Based on Social Similarity in Opportunistic Networks

Journal

IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS
Volume 15, Issue 1, Pages 594-605

Publisher

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TWC.2015.2476466

Keywords

Opportunistic network; social similarity; incentive; trust routing

Funding

  1. National Natural Science Foundation of China [61173179, 61103146, 61202441]
  2. Program for New Century Excellent Talents in University [NCET-13-0083]
  3. Fundamental Research Funds for the Central Universities [DUT13JS10, DUT14YQ212]

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The lack of pre-existing infrastructure or dynamic topology makes it impossible to establish end-to-end connections in opportunistic networks (OppNets). Instead, a store-and-forward strategy can be employed. However, such loosely knit routing paths depend heavily on the cooperation among participating nodes. Selfish or malicious behaviors of nodes impact greatly on the network performance. In this paper, we design and validate a dynamic trust management model for secure routing optimization. We propose the concept of incorporating social trust into the routing decision process and design a trust routing based on social similarity (TRSS) scheme. TRSS is based on the observation that nodes move around and contact each other according to their common interests or social similarities. A node sharing more social features in social history record with the destination is more likely to travel close to the latter in the near future and should be chosen as the next-hop forwarder. Furthermore, social trust can be established based on an observed node's trustworthiness and its encounter history. Based on direct and recommended trust, those untrustworthy nodes will be detected and purged from the trusted list. Since only trusted nodes' packets will be forwarded, the selfish nodes have the incentives to behave well again. Simulation evaluation demonstrates that TRSS is very effective in detecting selfish or even malicious nodes and achieving better performance.

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