A platform for ubiquitous sensor deployment in occupational and domestic environments
Proceedings of the 6th international conference on Information processing in sensor networks
Design and implementation of a high-fidelity AC metering network
IPSN '09 Proceedings of the 2009 International Conference on Information Processing in Sensor Networks
ViridiScope: design and implementation of a fine grained power monitoring system for homes
Proceedings of the 11th international conference on Ubiquitous computing
Experiences with a high-fidelity wireless building energy auditing network
Proceedings of the 7th ACM Conference on Embedded Networked Sensor Systems
UbiComp '07 Proceedings of the 9th international conference on Ubiquitous computing
Proceedings of the 12th ACM international conference on Ubiquitous computing
Proceedings of the 8th ACM Conference on Embedded Networked Sensor Systems
Contactless sensing of appliance state transitions through variations in electromagnetic fields
Proceedings of the 2nd ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Building
TinyEARS: spying on house appliances with audio sensor nodes
Proceedings of the 2nd ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Building
@scale: insights from a large, long-lived appliance energy WSN
Proceedings of the 11th international conference on Information Processing in Sensor Networks
Monitoring massive appliances by a minimal number of smart meters
ACM Transactions on Embedded Computing Systems (TECS) - Special Section ESFH'12, ESTIMedia'11 and Regular Papers
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To smartly control the massive electrical appliances in buildings to save energy, the real-time on/off states of the electrical appliances are critically required as the fundamental information. However, it is generally a very difficult and costly problem, because N appliances have 2N states and the appliances are massively in modern buildings. This paper propose a novel compressive sensing model for monitoring the massive appliances' states, in which the sparseness of on/off switching events within a short observation interval is exploited. Based on such a temporal sparseness feature, a lightweight state tracking framework is proposed to track the on/off states of N appliances by deploying only m smart meters on the power load tree, where m ≪ N. Particularly, it firstly presents an online state decoding algorithm based on a Hidden Markov Model of sparse state transitions. It reduces the traditional O(t22N) complexity of Viterbi decoding to polynomial complexity of O(tnU+1) where n and U is an upper bound of the simultaneous switching events. To minimize the meter deployment cost, i. e., m, an entropy-based necessary condition for deploying the minimal number of meters while guaranteeing the state tracking accuracy is presented. Based on it, a greedy algorithm to optimize the meter deployment to meet any given state decoding accuracy requirement is proposed. These proposed results are verified extensively based on the simulated data and the real PowerNet data. Simulation results confirm the the good performances of the proposed methods, and also demonstrate some interesting structures of the problem.