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
Ariadne: a secure on-demand routing protocol for ad hoc networks
Wireless Networks
Perspectives on next generation mobile
BT Technology Journal
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)
Mobile and wireless networks: services, evolution and issues
International Journal of Mobile Communications
Too much mobility limits the capacity of wireless ad hoc networks
IEEE Transactions on Information Theory
Comparison of access control methods in mobile as-hoc networks
IMSAA'09 Proceedings of the 3rd IEEE international conference on Internet multimedia services architecture and applications
Secured reactive routing protocol for mobile nodes in sensor networks
WSEAS TRANSACTIONS on COMMUNICATIONS
GRAP: Grey risk assessment based on projection in ad hoc networks
Journal of Parallel and Distributed Computing
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A wireless Ad-hoc network is a group of wireless devices that communicate with each other without utilising any central management infrastructure. The operation of Ad-hoc networks depends on the cooperation among nodes to provide connectivity and communication routes. However, such an ideal situation may not always be achievable in practice. Some nodes may behave maliciously, resulting in degradation of the performance of the network or even disruption of its operation altogether. To mitigate the effect of such nodes and to achieve higher levels of security and reliability, this paper expands on relevant fuzzy logic concepts to propose an approach to establish quantifiable trust levels between the nodes of Ad-hoc networks. These trust levels are then used in the routing decision making process. Using OPNET and MATLAB simulators, the proposed approach is validated and further studied. The findings show that when the proposed approach is utilised, the overall performance of the Ad-hoc network is significantly improved.