Modeling and optimizing Random Walk content discovery protocol over mobile ad-hoc networks

Content discovery is one of the challenges over mobile ad-hoc networks. Peer to peer content discovery techniques including structured and unstructured can be employed in MANETs by considering its special characteristics and limitations. The most important characteristics of MANETs are the mobility of the nodes, power consumption limitations and transitive links which create a dynamic topology. Unstructured techniques present higher performance compared to the structured ones over MANETs and among unstructured peer to peer protocols, Random Walk delivers less energy consumption and satisfactory hit rate awhile. This paper proposes an adaptive method to optimize the random walk unstructured content discovery protocol. First, it models this protocol using the G-network which is a queuing system with two types of customer, negative and positive. Then, it optimizes this protocol by the gradient descend technique based on a cost function which consists of three parameters. Two of these parameters are hit rate and response time which are derived from the content discovery protocol performance metric. The other parameter is energy consumption which is one of the most important performance metrics in MANET.