Routing and Security in Mobile Ad Hoc Networks

A Mobile Ad hoc Network (MANET) consists of a set of nodes which can form a network among themselves. MANETs have applications in areas such as military, disaster rescue operations, monitoring animal habitats, etc. where establishing fixed communication infrastructure is not feasible. Routing protocols designed for MANETs can be broadly classified as position-based (geographic), topology-based and hybrid. Geographic routing uses location information of nodes to route messages. Topology-based routing uses network state information for route discovery and maintenance. Hybrid routing protocols use features in both position-based and topology-based approaches. Position-based routing protocols route packets towards the destination using greedy forwarding (i.e., an intermediate node forwards packets to a neighbor that is closer to the destination than itself). If a node has no neighbor that is closer to the destination than itself, greedy forwarding fails. In this case, we say there is void. Different position-based routing protocols use different methods for dealing with voids. Topology-based routing protocols can be classified into on-demand (reactive) routing protocols and proactive routing protocols. Generally, on-demand routing protocols establish routes when needed by flooding route requests throughout the entire network, which is not a scalable approach. Reactive routing protocols try to maintain routes between every pair of nodes by periodically exchanging messages with each other which is not a scalable approach also. This thesis addresses some of these issues and makes the following contribution. First, we present a position-based routing protocol called Greedy Routing Protocol with Backtracking (GRB) which uses a simple backtracking technique to route around voids, unlike existing position-based routing protocols which construct planarized graph of the local network to route around voids. We compare the performance of our protocol with the well known Greedy Perimeter Stateless Routing (GPSR) protocol and the Ad-Hoc On-demand Distance Vector (AODV) routing protocol as well as the Dynamic Source Routing (DSR) protocol. Performance evaluation shows that our protocol has less control overhead than those of DSR, AODV, and GPSR. Performance evaluation also shows that our protocol has a higher packet-delivery ratio, lower end-to-end delay, and less hop count, on average, compared to AODV, DSR and GPSR. We then present an on-demand routing protocol called ``Hybrid On-demand Greedy Routing Protocol with Backtracking for Mobile Ad-Hoc Networks which uses greedy approach for route discovery. This prevents flooding route requests, unlike the existing on-demand routing protocols. This approach also helps in finding routes that have lower hop counts than AODV and DSR. Our performance evaluation confirms that our protocol performs better than AODV and DSR, on average, with respect to hop count, packet-delivery ratio and control overhead. In MANETs, all nodes need to cooperate to establish routes. Establishing secure and valid routes in the presence of adversaries is a challenge in MANETs. Some of the well-known source routing protocols presented in the literature (e.g., Ariadne and endairA) which claim to establish secure routes are susceptible to hidden channel attacks. We address this issue and present a secure routing protocol called SAriadne, based on sanitizable signatures. We show that our protocol detects and prevents hidden channel attacks

A survey on subjecting electronic product code and non-ID objects to IP identification

Over the last decade, both research on the Internet of Things (IoT) and real-world IoT applications have grown exponentially. The IoT provides us with smarter cities, intelligent homes, and generally more comfortable lives. However, the introduction of these devices has led to several new challenges that must be addressed. One of the critical challenges facing interacting with IoT devices is to address billions of devices (things) around the world, including computers, tablets, smartphones, wearable devices, sensors, and embedded computers, and so on. This article provides a survey on subjecting Electronic Product Code and non-ID objects to IP identification for IoT devices, including their advantages and disadvantages thereof. Different metrics are here proposed and used for evaluating these methods. In particular, the main methods are evaluated in terms of their: (i) computational overhead, (ii) scalability, (iii) adaptability, (iv) implementation cost, and (v) whether applicable to already ID-based objects and presented in tabular format. Finally, the article proves that this field of research will still be ongoing, but any new technique must favorably offer the mentioned five evaluative parameters.Comment: 112 references, 8 figures, 6 tables, Journal of Engineering Reports, Wiley, 2020 (Open Access

Stability of secure routing protocol in ad hoc wireless network.

The contributions of this research are threefold. First, it offers a new routing approach to ad hoc wireless network protocols: the Enhanced Heading-direction Angle Routing Protocol (EHARP), which is an enhancement of HARP based on an on-demand routing scheme. We have added important features to overcome its disadvantages and improve its performance, providing the stability and availability required to guarantee the selection of the best path. Each node in the network is able to classify its neighbouring nodes according to their heading directions into four different zone-direction group. The second contribution is to present a new Secure Enhanced Heading-direction Angle Routing Protocol (SEHARP) for ad hoc networks based on the integration of security mechanisms that could be applied to the EHARP routing protocol. Thirdly, we present a new approach to security of access in hostile environments based on the history and relationships among the nodes and on digital operation certificates. We also propose an access activity diagram which explains the steps taken by a node. Security depends on access to the history of each unit, which is used to calculate the cooperative values of each node in the environment