Comparative Review of QoS-Aware On-Demand Routing in Ad Hoc Wireless Networks

In this paper, a representative set of QoS models and QoS-aware on-demand routing protocols are reviewed with emphasis on their ability to support QoS in mobile ad-hoc networks (MANETs) possibly used in WSNs. In particular IntServ, DiffServ, FQMM, and SWAN QoS models are reviewed followed by different QoS-aware on-demand routings in MANETs from different perspectives such as the challenges, classifica-tions, algorithmic aspects in QoS provisions. Tradeoff in providing support to real time (RT) and best effort (BE) traffic is highlighted. Finally, a detailed and comprehensive comparison table is provided for better un-derstanding of QoS provision in MANETs

Route discovery schemes in Mobile Ad hoc Networks with variable-range transmission power

Broadcasting in MANETs is important for route discovery but consumes significant amounts of power that is difficult to renew for devices that rely heavily on batteries. Most existing routing protocols make use of a broadcast scheme known as simple flooding. In such an on-demand routing protocol (e.g. AODV) the source node originates a Route Request (RREQ) packet that is blindly rebroadcast via neighbouring nodes to all nodes in the network. Simple flooding leads to serious redundancy, together with contention, and collisions, which is often called the broadcast storm problem. This thesis proposes two improvement strategies: topology control (adjusting transmission power) and reduced retransmissions (reducing redundant rebroadcasts) to reduce energy consumption. For energy efficient route discovery the main idea is to reduce the energy consumed per broadcast during route discovery. An Energy Efficient Adaptive Forwarding Algorithm (called EEAFA) is proposed to reduce the impact of RREQ packet flooding in on-demand routing protocols. The algorithm operates in two phases: 1) Topology construction phase, which establishes a more scalable and energy efficient network structure where nodes can adjust their transmission power range dynamically, based on their local density. 2) A Forwarding Node Determination phase, that utilises network information provided by the constructed topology, where nodes independently decide to forward a RREQ packet or not without relying on GPS or any distance calculations. A further Enhanced EEAFA (called E-EEAFA) algorithm is also proposed, which combines two techniques: graph colouring and sectoring techniques. Graph colouring increases awareness at network nodes to improve the determination of a forwarding node, while the sectoring technique divides neighbours into different forwarding sectors. This helps to reduce overlap between forwarding nodes and select suitable nodes in each sector to forward RREQ packets. These techniques are employed in a distributed manner and collaborate to reduce the number of forwarding nodes, which thus reduces the volume of RREQ packets populating the network. These algorithms have been validated as effective by NS2 simulation studies that are detailed in the thesis