Storage Retrieval

Download Learning from Data Streams: Processing Techniques in Sensor by Joao Gama, Mohamed Medhat Gaber PDF

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By Joao Gama, Mohamed Medhat Gaber

Sensor networks include allotted self sustaining units that cooperatively computer screen an atmosphere. Sensors are built with capacities to shop info in reminiscence, approach this knowledge and converse with their acquaintances. Processing information streams generated from instant sensor networks has raised new examine demanding situations over the past few years a result of large numbers of information streams to be controlled continually and at a truly excessive rate.The booklet offers the reader with a entire assessment of circulation facts processing, together with recognized prototype implementations just like the Nile approach and the TinyOS working process. The set of chapters covers the state-of-art in information movement mining techniques utilizing clustering, predictive studying, and tensor research options, and utilising them to functions in safeguard, the average sciences, and education.This learn monograph offers to researchers and graduate scholars the state-of-the-art in info flow processing in sensor networks. the large bibliography deals an outstanding start line for extra studying and destiny study.

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Extra info for Learning from Data Streams: Processing Techniques in Sensor Networks

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Spatial and temporal attributes play a major role in sensor networks. These attributes have no major effect on data streams in other applications such as telecommunication data streams and web logs and click streams. The need to know where and when the sensor reading has occurred is crucial to sensor network applications. For example, it is important to have exact information about the areas affected by a fire. • Query design is based mostly on energy efficiency in sensor networks. Data streams generated from other sources have no energy constraints.

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Kargupta, In-network outlier detection in wireless sensor networks. In: Proceedings of the 26th International Conference on Distributed Computing Systems (ICDCS), 2006. [8] V. Cantoni, L. Lombardi, P. Lombardi, Challenges for data mining in distributed sensor networks. ICPR (1), 2006:1000–1007, 2006. K. Chong, S. W. Loke, A context-aware approach to conserving energy in wireless sensor networks. PerCom Workshops, 2005:401–405, 2005. [10] P. Corke, P. Sikka, P. Valencia, C. Crossman, G. Bishop-Hurley, D.

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