Supero: A sensor system for unsupervised residential power usage monitoring

Phillips, Dennis E.; Tan, Rui; Moazzami, Mohammad-Mahdi; Xing, Guoliang; Chen, Jinzhu; Yau, David K. Y.

doi:10.1109/percom.2013.6526716

Cited by 24 publications

(11 citation statements)

References 13 publications

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“…Unless stated otherwise, we use the fading factor λ = 0.9 to update the length-frequency set (Definition 4). 1 The state forecasting and running time experiments and our source code can be found in [16].…”

Section: Methodsmentioning

confidence: 99%

“…These appliances are equipped with sensors to measure their power use (including patterns) and some (limited) storage, computational and communication capabilities. Here also a generic and unsupervised algorithm is more suited due to the huge amount of newly introduced appliances and sensors [1], [2]. Additionally, their ability to predict their own future states or usage patterns could be useful, e.g., for participating in a demand response program [3], or if electricity prices follow real-time dynamic pricing schemes, then drawing/storing energy from the grid when the price is cheap and using it later when the price is expensive would save money [4], [5].…”

Section: Introductionmentioning

confidence: 99%

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Online unsupervised state recognition in sensor data

Eberle

Wijaya

Aberer

2015

2015 IEEE International Conference on Pervasive Computing and Communications (PerCom)

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Abstract-Smart sensors, such as smart meters or smart phones, are nowadays ubiquitous. To be "smart", however, they need to process their input data with limited storage and computational resources. In this paper, we convert the stream of sensor data into a stream of symbols, and further, to higher level symbols in such a way that common analytical tasks such as anomaly detection, forecasting or state recognition, can still be carried out on the transformed data with almost no loss of accuracy, and using far fewer resources. We identify states of a monitored system and convert them into symbols (thus, reducing data size), while keeping "interesting" events, such as anomalies or transition between states, as it is. Our algorithm is able to find states of various length in an online and unsupervised way, which is crucial since behavior of the system is not known beforehand. We show the effectiveness of our approach using real-world datasets and various application scenarios.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Online unsupervised state recognition in sensor data

Eberle

Wijaya

Aberer

2015

2015 IEEE International Conference on Pervasive Computing and Communications (PerCom)

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“…ViridiScope [7], is an indirect sensing based power signatures detection framework where sensors like magnetometers, microphone, and light sensors are used to detect events. In Supero [8] multi-sensor fusion and unsupervised machine learning algorithms have been proposed. It can classify the appliance events of interest and autonomously associate measured power usage with the respective appliances.…”

Section: Related Workmentioning

confidence: 99%

“…Specifically, in this work we augment the appliances' acoustic signatures with its power consumption pattern to infer the individual usage, waste and safety. While the idea of combining the appliances' ambient acoustic signature and power sensing is certainly not new [8], [16], [19]; our differentiator is unique because we explicitly consider fine-grained state of appliances based on acoustic and power signatures and augment both to reciprocate each other. This improves the effectiveness of energy disaggregation algorithms and appliances life cycle management system.…”

Section: Introductionmentioning

confidence: 99%

Acoustic based appliance state identifications for fine-grained energy analytics

Pathak

Khan

Roy

2015

2015 IEEE International Conference on Pervasive Computing and Communications (PerCom)

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Fine-grained monitoring of everyday appliances can provide better feedback to the consumers and motivate them to change behavior in order to reduce their energy usage. It also helps to detect abnormal power consumption events, long-term appliance malfunctions and potential safety concerns. Commercially available plug meters can be used for individual appliance monitoring but for an entire house, each such individual plug meters are expensive and tedious to setup. Alternative methods relying on Non-Intrusive Load Monitoring techniques help disaggregate electricity consumption data and learn about the individual appliance's power states and signatures. However fine-grained events (e.g., appliance malfunctions, abnormal power consumption, etc.) remain undetected and thus inferred contexts (such as safety hazards etc.) become invisible. In this work, we correlate an appliance's inherent acoustic noise with its energy consumption pattern individually and in presence of multiple appliances. We initially investigate classification techniques to establish the relationship between appliance power and acoustic states for efficient energy disaggregation and abnormal events detection. While promising, this approach fails when there are multiple appliances simultaneously in 'ON' state. To further improve the accuracy of our energy disaggregation algorithm, we propose a probabilistic graphical model, based on a variation of Factorial Hidden Markov Model (FHMM) for multiple appliances energy disaggregation. We combine our probabilistic model with the appliances acoustic analytics and postulate a hybrid model for energy disaggregation. Our approach helps to improve the performance of energy disaggregation algorithms and provide critical insights on appliance longevity, abnormal power consumption, consumer behavior and their everyday lifestyle activities. We evaluate the performance of our proposed algorithms on real data traces and show that the fusion of acoustic and power signatures can successfully detect a number of appliances with 95% accuracy.

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“…For the purpose of saving energy, sensor nodes try to keep their radio off as much as possible, which squeezes the available time for packet transmissions among the nodes and potentially leads to collisions. In WSNs for event detection, e.g., [4], the unpredictable busty traffic caused by event occurrences further enlarges the chance of collisions.…”

Section: Introductionmentioning

confidence: 99%

Walking down the STAIRS: Efficient collision resolution for wireless sensor networks

Wang

et al. 2014

IEEE INFOCOM 2014 - IEEE Conference on Computer Communications

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Abstract-Collision resolution is a crucial issue in wireless sensor networks. The existing approaches of collision resolution have drawbacks with respect to energy efficiency and processing latency. In this paper, we propose ST AIRS, a time and energy efficient collision resolution mechanism for wireless sensor networks. STAIRS incorporates the constructive interference technique in its design and explicitly forms superimposed colliding signals. Through extensive observations and theoretical analysis, we show that the RSSI of the superimposed signals exhibit stairslike phenomenon with different number of contenders. That principle offers an attractive feature to efficiently distinguish multiple contenders and in turn makes collision-free schedules for channel access. In the design and implementation of STAIRS, we address practical challenges such as contenders alignment, online detection of RSSI change points, and fast channel assignment. The experiments on real testbed show that STARIS realizes fast and effective collision resolution, which significantly improves the network performance in terms of both latency and throughput.

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Supero: A sensor system for unsupervised residential power usage monitoring

Cited by 24 publications

References 13 publications

Online unsupervised state recognition in sensor data

Online unsupervised state recognition in sensor data

Acoustic based appliance state identifications for fine-grained energy analytics

Walking down the STAIRS: Efficient collision resolution for wireless sensor networks

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