Scalable routing strategies for large ad hoc wireless networks

As the wireless and embedded computing technologies continue to advance, increasing numbers of small size and high performance computing and communication devices will be capable of tetherless communications and ad hoc wireless networking. An ad hot wireless network is a self-organizing and self-configuring network with the capability of rapid deployment in response to application needs. An important characteristic which sets ad hoc networks apart from cellular networks is the fact that they do not rely on a fixed infrastructure. Ad hoc networks are very attractive for tactical communication in military and law enforcement. They are also expected to play an important role in civilian forums such as convention centers, conferences, and electronic classrooms. Mobility, potentially very large number of mobile nodes, and limited resources (e.g., bandwidth and power) make routing in ad hoc networks extremely challenging. The routing protocols for ad hoc wireless networks have to adapt quickly to the frequent and unpredictable changes of topology and must be parsimonious of communications and processing resources.; In this dissertation, we investigate routing strategies which scale well to large populations and can handle mobility. In addition, we address the need to support multimedia communications, with low latency requirements for interactive traffic and Quality of Service (QoS) support for real time streams (voice/video). In the wireless routing area, several schemes have already been proposed and implemented (e.g., hierarchical routing, on-demand routing etc.). All of these schemes have some limitations of their own. To overcome these limitations, we introduce three new schemes—Fisheye State Routing (FSR), Hierarchical State Routing (HSR) and Landmark Ad Hoe Routing (LANMAR). These schemes all include some hierarchical layering features, either explicit (HSR) or implicit (FSR and LANMAR) which yield good scalability properties and offer competitive advantages over the existing schemes. Next, we consider the on demand schemes and apply passive clustering techniques to improve their performance. We compare the performance of existing and proposed schemes via simulation.