Classifying Motion Patterns and Grouping in Sparse Mobile Networks

In a sparse mobile network where delay tolerant network routing helps message dissemination, some temporary or persistent stable concentrations of mobile nodes in time domain can help message disseminations more efficiently. The feature can be created due to movements revealing common goals intentionally or less intentionally. However, a mobile network application can generate complex mixing mobility patterns that disguise the rendering of these features. In this paper, we propose two entropy based metrics to identify the nodes with different mobility patterns and further use the metrics to accomplish clustering of nodes. Aiming at low-end devices which have no inputs of velocity and location, we employ neighbor info through hello messages and draw speed implication through neighbor change rates. The entropy based metrics are used in a clustering algorithm to find stable nodes as cluster heads. According to the simulation results, the two metrics, namely, speed entropy and relation entropy can be applied to distinguish active nodes from stable nodes in different group mixing configurations. The simulations also show that our new metric-based clustering algorithm generates more stable clusters.