Prediction of near likely nodes in data-centric mobile wireless networks

As the increasing amount of data is collected in mobile wireless networks for emerging pervasive applications, data-centric storage provides energy-efficient data dissemination and organization. One of the approaches in data-centric storage is that the nodes that collected data will transfer their data to other neighboring nodes that store the similar type of data. However, when the nodes are mobile, type-based data distribution alone cannot provide robust data storage and retrieval, since the nodes that store similar types may move far away and can not be easily reachable in the future. In order to minimize the communication overhead and achieve efficient data retrieval in data-centric mobile environments, we propose a fully distributed neighborhood prediction scheme that utilizes past node trajectory information to determine the near likely node in the future as the best content distributee. We developed two methods that predict the future neighborhood based on the correlations of the past trajectories. Our extensive simulation results demonstrate that our prediction approaches can effectively and efficiently predict the future neighborhood with high accuracy.