Fuzzy logic and neurofuzzy applications explained
Fuzzy logic and neurofuzzy applications explained
Ad-hoc On-Demand Distance Vector Routing
WMCSA '99 Proceedings of the Second IEEE Workshop on Mobile Computer Systems and Applications
Ad Hoc Wireless Networks
Wireless mesh networks: a survey
Computer Networks and ISDN Systems
A Fuzzy Trust Model for E-Commerce
CEC '05 Proceedings of the Seventh IEEE International Conference on E-Commerce Technology
Priority scheduling in wireless ad hoc networks
Wireless Networks
Trust and Recommendations in Mobile Ad hoc Networks
ICNS '07 Proceedings of the Third International Conference on Networking and Services
Throughput analysis of IEEE802.11 multi-hop ad hoc networks
IEEE/ACM Transactions on Networking (TON)
Too much mobility limits the capacity of wireless ad hoc networks
IEEE Transactions on Information Theory
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Ideally, in Ad-hoc networks all comprising nodes act cooperatively in establishing the communication paths from various sources to destinations. However, in practice such an ideal situation may not always be realized. Some nodes may act selfishly, refusing to accept the burden of forwarding packets. Some other nodes may act maliciously. Either way, such nodes degrade the network reliability or even disrupt its operation altogether. To alleviate the problems caused by the presence of such nodes, this paper proposes to establish quantifiable trust levels for the comprising nodes and to use them in making routing decisions. The proposed approach uses fuzzy logic concepts in evaluating the trust levels of the nodes. The trust levels are then used in the routing process. Using OPNET and MATLAB, the proposed approach is validated and further analyzed. It is shown that the utilization of this approach can result in significant enhancements of the performance and reliability of Ad-hoc networks.