Scalable and Secure Multicast Routing for Mobile Ad-hoc Networks

Mobile Ad-Hoc Networks (MANETs) are decentralized and autonomous communication systems: They can be used to provide connectivity when a natural disaster has brought down the infrastructure, or they can support freedom of speech in countries with governmental Internet restrictions. MANET design requires careful attention to scalability and security due to low-capacity and error-prone wireless links as well as the openness of these systems. In this thesis, we address the issue of multicast as a means to efficiently support the MANET application of group communication on the network layer. To this aim, we first survey the research literature on the current state of the art in MANET routing, and we identify a gap between scalability and security in multicast routing protocols–two aspects that were only considered in isolation until now. We then develop an explicit multicast protocol based on the design of a secure unicast protocol, aiming to maintain its security properties while introducing minimal overhead. Our simulation results reveal that our protocol reduces bandwidth utilization in group communication scenarios by up to 45 % compared to the original unicast protocol, while providing significantly better resilience under blackhole attacks. A comparison with pure flooding allows us to identify a practical group size limit, and we present ideas for better large-group support

A reliable trust-aware reinforcement learning based routing protocol for wireless medical sensor networks.

Interest in the Wireless Medical Sensor Network (WMSN) is rapidly gaining attention thanks to recent advances in semiconductors and wireless communication. However, by virtue of the sensitive medical applications and the stringent resource constraints, there is a need to develop a routing protocol to fulfill WMSN requirements in terms of delivery reliability, attack resiliency, computational overhead and energy efficiency. This doctoral research therefore aims to advance the state of the art in routing by proposing a lightweight, reliable routing protocol for WMSN. Ensuring a reliable path between the source and the destination requires making trustaware routing decisions to avoid untrustworthy paths. A lightweight and effective Trust Management System (TMS) has been developed to evaluate the trust relationship between the sensor nodes with a view to differentiating between trustworthy nodes and untrustworthy ones. Moreover, a resource-conservative Reinforcement Learning (RL) model has been proposed to reduce the computational overhead, along with two updating methods to speed up the algorithm convergence. The reward function is re-defined as a punishment, combining the proposed trust management system to defend against well-known dropping attacks. Furthermore, with a view to addressing the inborn overestimation problem in Q-learning-based routing protocols, we adopted double Q-learning to overcome the positive bias of using a single estimator. An energy model is integrated with the reward function to enhance the network lifetime and balance energy consumption across the network. The proposed energy model uses only local information to avoid the resource burdens and the security concerns of exchanging energy information. Finally, a realistic trust management testbed has been developed to overcome the limitations of using numerical analysis to evaluate proposed trust management schemes, particularly in the context of WMSN. The proposed testbed has been developed as an additional module to the NS-3 simulator to fulfill usability, generalisability, flexibility, scalability and high-performance requirements