Utilizing Solar Power in Wireless Sensor Networks
LCN '03 Proceedings of the 28th Annual IEEE International Conference on Local Computer Networks
HEED: A Hybrid, Energy-Efficient, Distributed Clustering Approach for Ad Hoc Sensor Networks
IEEE Transactions on Mobile Computing
Proceedings of the 5th international conference on Information processing in sensor networks
New gradient-based variable step size LMS algorithms
EURASIP Journal on Advances in Signal Processing
GMSVM-based prediction for temporal data aggregation in sensor networks
WiCOM'09 Proceedings of the 5th International Conference on Wireless communications, networking and mobile computing
Clustering distributed sensor data streams using local processing and reduced communication
Intelligent Data Analysis - Ubiquitous Knowledge Discovery
An application-specific protocol architecture for wireless microsensor networks
IEEE Transactions on Wireless Communications
Making sensor networks immortal: an energy-renewal approach with wireless power transfer
IEEE/ACM Transactions on Networking (TON)
An Application-Specific Forecasting Algorithm for Extending WSN Lifetime
DCOSS '13 Proceedings of the 2013 IEEE International Conference on Distributed Computing in Sensor Systems
Data stream clustering: A survey
ACM Computing Surveys (CSUR)
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Wireless Sensor Networks (WSNs) have found many practical applications in recent years. Apart from both the vast new opportunities and challenges raised by the availability of large amounts of sensory data, energy conservation remains a challenging research topic that demands intelligent solutions. Various data aggregation techniques have been proposed in the literature, but the optimal tradeoff between algorithm complexity and prediction ability remains elusive. In this paper we concentrate on employing a few light-weight time series estimation algorithms for online predictive sensing. A number of performance metrics are proposed and employed to examine the effectiveness of the scheme using real-world datasets.