Occupancy detection helps enable various emerging smart environment applications ranging from opportunistic HVAC (heating, ventilation, and air-conditioning) control, effective meeting management, healthy social gathering, and public event planning and organization. Ubiquitous availability of smartphones and wearable sensors with the users for almost 24 hours helps revitalize a multitude of novel applications. The inbuilt microphone sensor in smartphones plays as an inevitable enabler to help detect the number of people conversing with each other in an event or gathering. A large number of other sensors such as accelerometer and gyroscope help count the number of people based on other signals such as locomotive motion. In this work, we propose multimodal data fusion and deep learning approach relying on the smartphone’s microphone and accelerometer sensors to estimate occupancy. We first demonstrate a novel speaker estimation algorithm for people counting and extend the proposed model using deep nets for handling large-scale fluid scenarios with unlabeled acoustic signals. We augment our occupancy detection model with a magnetometer-dependent fingerprinting-based localization scheme to assimilate the volume of location-specific gathering. We also propose crowdsourcing techniques to annotate the semantic location of the occupant. We evaluate our approach in different contexts: conversational, silence, and mixed scenarios in the presence of 10 people. Our experimental results on real-life data traces in natural settings show that our cross-modal approach can achieve approximately 0.53 error count distance for occupancy detection accuracy on average.
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Non-Intrusive Air Leakage Detection in Residential Homes
Air leakages pose a major problem in both residential and commercial buildings. They increase the utility bill and result in excessive usage of Heating Ventilation and Air Conditioning (HVAC) systems, which impacts the environment and causes discomfort to residents. Repairing air leakages in a building is an expensive and time intensive task. Even detecting the leakages can require extensive professional testing. In this paper, we propose a method to identify the leaky homes from a set, provided their energy consumption data is accessible from residential smart meters. In the first phase, we employ a Non-Intrusive Load Monitoring (NILM) technique to disaggregate the HVAC data from total power consumption for several homes. We propose a recurrent neural network and a denoising autoencoder based approach to identify the 'ON' and 'OFF' cycles of the HVACs and their overall usages. We categorize the typical HVAC consumption of about 200 homes and any probable insulation and leakage problems using the Air Changes per Hour at 50 Pa (ACH50) metric in the Dataport datasets. We perform our proposed NILM analysis on different granularities of smart meter data such as 1 min, 15 mins, and 1 hour to observe its effect on classifying the leaky homes. Our results show that disaggregation can be used to identify the residential air-conditioning, at 1 min granularity which in turn helps us to identify the leaky potential homes, with an accuracy of 86%.
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- Award ID(s):
- 1544687
- NSF-PAR ID:
- 10073172
- Date Published:
- Journal Name:
- ICDCN '18 Proceedings of the 19th International Conference on Distributed Computing and Networking
- Page Range / eLocation ID:
- 1 to 10
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3154273.3154345
