By Fei Hu, Qi Hao
''In the decade, instant or stressed out sensor networks have attracted a lot realization. in spite of the fact that, such a lot designs objective normal sensor community matters together with protocol stack (routing, MAC, etc.) and protection matters. This booklet specializes in the shut integration of sensing, networking, and shrewdpermanent sign processing through computer studying. in accordance with their world-class examine, the authors current the basics of intelligent Read more...
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Additional info for Intelligent sensor networks : the integration of sensor networks, signal processing and machine learning
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Kröse. Accurate activity recognition in a home setting. 10th International Conference on Ubiquitous Computing (UbiComp ‘08), New York, 2008, pp. 1–9. Kulakov, A. and D. Davcev. Tracking of unusual events in wireless sensor networks based on artificial neuralnetworks algorithms. International Conference on Information Technology: Coding and Computing (ITCC ‘05), Las Vegas, NV, 2005, pp. 534–539. Li, Y. Y. and L. E. Parker. Intruder detection using a wireless sensor network with an intelligent mobile robot response.
The instances in the same group are more similar to each other than to those in other clusters. 18 ■ Intelligent Sensor Networks The notion of a cluster varies between algorithms and the clusters found by different algorithms vary significantly in their properties. Typical cluster models include the following: ■ ■ ■ ■ ■ Connectivity models: An example of a connectivity model algorithm is hierarchical clustering, which builds models based on distance connectivity. Centroid models: A representative of this set of algorithms is the k-means algorithm.
Therefore, at each time step, there is a hidden variable and an observable output variable. In sensor network applications, the hidden variable could be the event or activity performed and the observable output variable is the vector of sensor readings. 8 shows an example HMM, where the states of the system Y are hidden, but the output variables X are visible. There are two dependency assumptions that define this model, represented by the directed arrows in the figure. 1. Markov assumption: the hidden variable at time t, namely Yt , depends only on the previous hidden variable Yt−1 (Rabiner 1989).