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. The ease of establishment, along with the mobility capabilities that these networks offer, provides many advantages. On the other hand, these very characteristics, as well as the lack of any centralised administration, are the root of several nontrivial challenges in securing such networks. One of the key objectives of this thesis is to achieve improvements in the performance of Ad-hoc networks in the presence of malicious nodes. In general, malicious nodes are considered as nodes that subvert the capability of the network to perform its expected functions. Current Ad-hoc routing protocols, such as the Ad-hoc On demand Distance Vector (AODV), have been developed without taking the effects of misbehaving nodes into consideration. In this thesis, to mitigate the effects of such nodes and to attain high levels of security and reliability, an approach that is based on the utilisation of the behaviour history of all member nodes is proposed. The aim of the proposed approach is to identify routes between the source and the destination, which enclose no, or if that is not possible, a minimal number, of malicious nodes. This is in contrast to traditional approaches that predominantly tend to use other criteria such as shortest path alone. Simulation and experimental results collected after applying the proposed approach, show significant improvements in the performance of Ad-hoc networks even in the presence of malicious nodes. However, to achieve further enhancements, this approach is expanded to incorporate trust levels between the nodes comprising the Ad-hoc network. Trust is an important concept in any relation among entities that comprise a group or network. Yet it is hard to quantify trust or define it precisely. Due to the dynamic nature of Ad-hoc networks, quantifying trust levels is an even more challenging task. This may be attributed to the fact that different numbers of factors can affect trust levels between the nodes of Ad-hoc networks. It is well established that fuzzy logic and soft computing offer excellent solutions for handling imprecision and uncertainties. This thesis expands on relevant fuzzy logic concepts to propose an approach to establish quantifiable trust levels between the nodes of Ad-hoc networks. To achieve quantification of the trust levels for nodes, information about the behaviour history of the nodes is collected. This information is then processed to assess and assign fuzzy trust levels to the nodes that make up the Ad-hoc network. These trust levels are then used in the routing decision making process. The performance of an Ad-hoc network that implements the behaviour history based approach using OPtimised NETwork (OPNET) simulator is evaluated for various topologies. The overall collected results show that the throughput, the packet loss rate, and the round trip delay are significantly improved when the behaviour history based approach is applied. Results also show further enhancements in the performance of the Ad-hoc network when the proposed fuzzy trust evaluation approach is incorporated with a slight increase in the routing traffic overhead. Given the improvements achieved when the fuzzy trust approach is utilised, for further enhancements of security and reliability of Ad-hoc networks, future work to combine this approach with other artificial intelligent approaches may prove fruitful. The learning capability of Artificial Neural Networks makes them a prime target for combination with fuzzy based systems in order to improve the proposed trust level evaluation approach. / Doctor of Philosophy (PhD)
| Identifer | oai:union.ndltd.org:ADTP/236853 |
| Date | January 2007 |
| Creators | Hallani, Houssein, University of Western Sydney, College of Health and Science, School of Computing and Mathematics |
| Source Sets | Australiasian Digital Theses Program |
| Language | English |
| Detected Language | English |
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