Abstract
Data-centric storage provides energy-efficient data dissemination and organization for the increasing amount of wireless data. 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 cannot be easily reachable in the future. In order to minimize the communication overhead and achieve efficient data retrieval in mobile environments, we propose a reinforcement learning-based framework called PARIS, which utilizes past node trajectory information to predict the near likely nodes in the future as the best content distributee. Our framework can adaptively improve the prediction accuracy by using the reinforcement learning technique. Our experiments demonstrate that our approach can effectively and efficiently predict the future neighborhood.
Highlights
The development of data-centric storage has enabled efficient data dissemination of wireless networks
We propose mechanisms to predict near likely nodes for data-centric mobile wireless networks to achieve efficient data storage and retrieval
We propose PARIS, a fully distributed neighborhood prediction framework based on reinforcement learning techniques that utilize past node trajectory information to determine the best content distributee for the future
Summary
The development of data-centric storage has enabled efficient data dissemination of wireless networks. In data-centric storage of wireless networks, wireless devices that collect the data are called collector nodes. Two representative examples are: (1) sensors are used for animal migration tracking, and (2) wireless devices are equipped with police officers to monitor their daily patrol routes, collect crime information by areas, and record corresponding law enforcement actions. In these two scenarios, efficient data retrieval can be achieved if the data-centric storage is enabled, that is, the data is stored by the types of animals, by the activities performed by the animals, or by the tasks that are carried out by the police officers. To reduce the communication overhead, the storage nodes are picked from the neighborhood, that is, the nodes in the transmission range, of the collector node
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