A Generalized Broadcasting Technique for Mobile Ad Hoc Networks

Broadcasting is a major communication primitive required by many applications and protocols in Mobile Ad Hoc Networks (MANETs). It is frequently deployed for content distribution, service discovery or advertisement, and sensor data dissemination. Broadcast protocols are also a fundamental building block to realize principal middleware functionalities such as replication, group management and consensus. Broadcasting in MANETs has therefore been an active area of research recently. Most of the research conducted on broadcasting in MANETs has primarily focused only on carefully selected application and evaluation scenarios. Consequently, the developed broadcasting schemes do not yield good performance for other scenarios. Different comparative studies show that the existing broadcasting techniques are tailored to only one class of MANETs with respect to node density and node mobility, and are unfortunately not likely to operate well in other classes. Node spatial distribution is a key issue for the performance of broadcast protocols, since it determines the connectivity of the MANET. Our survey of potential MANET application scenarios shows a wide range of possible node spatial distributions and node mobilities. This leads to that a MANET generally shows a continuously changing network connectivity over space and time. Therefore, a generalized solution for broadcasting that accounts for the requirements of the various applications and adapts to the heterogeneous and evolving node spatial distribution and mobility is a major contribution. In this thesis, we present hypergossiping, a novel generalized broadcasting technique for MANETs. Hypergossiping integrates two adaptive schemes and efficiently switches between them depending on local node density. The first scheme is adaptive gossiping, which distributes messages within connected parts of the MANET. We adapted gossiping as follows. First, we established an analytical model for gossiping through adopting the SI mathematical model from the epidemiology. Then, we used the model to adapt the gossiping forwarding probability to local node density. As a result, we provide a simple analytical expression that nodes use to set the appropriate forwarding probability depending on the current number of neighbors. Simulation results showed that adaptive gossiping efficiently propagates messages within a network partition independent of the node spatial distribution and node mobility in that network