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
Tracking states of massive electrical appliances by lightweight metering and sequence decoding
Proceedings of the Sixth International Workshop on Knowledge Discovery from Sensor Data
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This article presents a framework for deploying a minimal number of smart meters to accurately track the ON/OFF states of a massive number of electrical appliances which exploits the sparseness feature of simultaneous ON/OFF switching events of the massive appliances. A theoretical bound on the least number of required smart meters is studied by an entropy-based approach, which qualifies the impact of meter deployment strategies to the state tracking accuracy. It motivates a meter deployment optimization algorithm (MDOP) to minimize the number of meters while satisfying given requirements to state tracking accuracy. To accurately decode the real-time ON/OFF states of appliances by the readings of meters, a fast state decoding (FSD) algorithm based on the hidden Markov model (HMM) is presented to track the state sequence of each appliance for better accuracy. Although traditional HMM needs O(t22N) time complexity to conduct online sequence decoding, FSD improves the complexity to O(tnU+1), where n N and U is an upper bound of the simultaneous switching events. Both MDOP and FSD are verified extensively using simulations and real PowerNet data. The results show that the meter deployment cost can be saved by more than 80% while still getting over 90% state tracking accuracy.