An adaptive and composite spatio-temporal data compression approach for wireless sensor networks
Proceedings of the 14th ACM international conference on Modeling, analysis and simulation of wireless and mobile systems
SBV-Cut: Vertex-cut based graph partitioning using structural balance vertices
Data & Knowledge Engineering
Cluster-based AAA architecture for wireless sensor and WiMax networks
Proceedings of the 6th International Conference on Ubiquitous Information Management and Communication
Energy efficient data gathering using prediction-based filtering in wireless sensor networks
International Journal of Information and Communication Technology
A framework for processing complex queries in wireless sensor networks
ACM SIGAPP Applied Computing Review
Duty-cycle-aware minimum-energy multicasting in wireless sensor networks
IEEE/ACM Transactions on Networking (TON)
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For many applications in wireless sensor networks (WSNs), users may want to continuously extract data from the networks for analysis later. However, accurate data extraction is difficult—it is often too costly to obtain all sensor readings, as well as not necessary in the sense that the readings themselves only represent samples of the true state of the world. Clustering and prediction techniques, which exploit spatial and temporal correlation among the sensor data provide opportunities for reducing the energy consumption of continuous sensor data collection. Integrating clustering and prediction techniques makes it essential to design a new data collection scheme, so as to achieve network energy efficiency and stability. We propose an energy-efficient framework for clustering-based data collection in wireless sensor networks by integrating adaptively enabling/disabling prediction scheme. Our framework is clustering based. A cluster head represents all sensor nodes in the cluster and collects data values from them. To realize prediction techniques efficiently in WSNs, we present adaptive scheme to control prediction used in our framework, analyze the performance tradeoff between reducing communication cost and limiting prediction cost, and design algorithms to exploit the benefit of adaptive scheme to enable/disable prediction operations. Our framework is general enough to incorporate many advanced features and we show how sleep/awake scheduling can be applied, which takes our framework approach to designing a practical algorithm for data aggregation: it avoids the need for rampant node-to-node propagation of aggregates, but rather it uses faster and more efficient cluster-to-cluster propagation. To the best of our knowledge, this is the first work adaptively enabling/disabling prediction scheme for clustering-based continuous data collection in sensor networks. Our proposed models, analysis, and framework are validated via simulation and comparison with competing techniques.