Effects of Different Mobility Models on Traffic Patterns in Wireless Sensor Networks

Recently, there has been a great deal of research on investigating the effects of mobility on network attributes such as capacity, connectivity, and coverage. In this paper, the node mobility is studied from a new perspective with an objective to reveal the inherent impact of different mobility models on the the traffic patterns in wireless sensor networks. Specifically, the transmission pattern of a mobile sensor node is first characterized by an alternating renewal process that changes states between the active and the inactive. Then, the active state distribution is investigated under four commonly used mobility models: random walk, random waypoint, discrete Brownian motion, and extended Levy walk. For each mobility model, the spectrum of the traffic oriented from a single node is analyzed based on renewal theory. According to this analysis, novel results regarding the impact of each mobility model on the traffic nature are found: random walk, random waypoint, and discrete Brownian motion can only induce short range dependent traffic, whose autocorrelation function decays exponentially fast. In contrast, the traffic under extended Levy walk exhibits pseudo long range dependence, in which the autocorrelation function decays slower than exponential and follows a power law form at large time lags. Finally, the revealed findings are verified by the statistical analysis on the collected traffic traces from the simulated transmissions.