A Framework for Generating Network-Based Moving Objects
Geoinformatica
Resilient Data-Centric Storage in Wireless Ad-Hoc Sensor Networks
MDM '03 Proceedings of the 4th International Conference on Mobile Data Management
Data-centric storage in sensornets
ACM SIGCOMM Computer Communication Review
Distributed localization in wireless sensor networks: a quantitative comparison
Computer Networks: The International Journal of Computer and Telecommunications Networking - Special issue: Wireless sensor networks
On the Effect of Group Mobility to Data Replication in Ad Hoc Networks
IEEE Transactions on Mobile Computing
Media sharing based on colocation prediction in urban transport
Proceedings of the 14th ACM international conference on Mobile computing and networking
Sensing motion using spectral and spatial analysis of WLAN RSSI
EuroSSC'07 Proceedings of the 2nd European conference on Smart sensing and context
Mobility detection using everyday GSM traces
UbiComp'06 Proceedings of the 8th international conference on Ubiquitous Computing
EURASIP Journal on Wireless Communications and Networking
Hi-index | 0.00 |
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.