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Predicting the occupancy related information in an environment has been investigated to satisfy the myriad requirements of various evolving pervasive, ubiquitous, opportunistic and participatory sensing applications. Infrastructure and ambient sensors based techniques have been leveraged largely to determine the occupancy of an environment incurring a significant deployment and retrofitting costs. In this paper, we advocate an infrastructure-less zero-configuration multimodal smartphone sensor-based techniques to detect fine-grained occupancy information. We propose to exploit opportunistically smartphones' acoustic sensors in presence of human conversation and motion sensors in absence of any conversational data. We develop a novel speaker estimation algorithm based on unsupervised clustering of overlapped and non-overlapped conversational data to determine the number of occupants in a crowded environment. We also design a hybrid approach combining acoustic sensing opportunistically with locomotive model to further improve the occupancy detection accuracy. We evaluate our algorithms in different contexts, conversational, silence and mixed in presence of 10 domestic users. Our experimental results on real-life data traces collected from 10 occupants in natural setting show that using this hybrid approach we can achieve approximately 0.76 error count distance for occupancy detection accuracy on average. 

To promote energy-efficient operations in residential and office buildings, non-intrusive load monitoring (NILM) techniques have been proposed to infer the fine-grained power consumption and usage patterns of appliances from power-line measurement data. Fine-grained monitoring of everyday appliances (such as toasters and coffee makers) can not only promote energy-efficient building operations, but also provide unique insights into the context and activities of individuals. Current building-level NILM techniques are unable to identify the consumption characteristics of relatively low-load appliances, whereas smart-plug based solutions incur significant deployment and maintenance costs. In this paper, we investigate an intermediate architecture, where smart circuit breakers provide measurements of aggregate power consumption at room (or section) level granularity. We then investigate techniques to identify the usage and energy consumption of individual appliances from such measurements. We first develop a novel correlation-based approach called CBPA to identify individual appliances based on both their unique transient and steady-state power signatures. While promising, CBPA fails when the set of candidate appliances is too large. To further improve the accuracy of appliance level usage estimation, we then propose a hybrid system called AARPA, which uses mobile sensing to first infer high-level activities of daily living (ADLs), and then uses knowledge of such ADLs to effectively reduce the set of candidate appliances that potentially contribute to the aggregate readings at any point. We evaluate two variants of this algorithm, and show, using real-life data traces gathered from 10 domestic users, that our fusion of mobile and power-line sensing is very promising: it identified all devices that were used in each data trace, and it identified the usage duration and energy consumption of low-load consumer appliances with 87% accuracy. 

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. 

Accurate estimation of localized occupancy related information in real time enables a broad range of intelligent smart environment applications. A large number of studies using heterogeneous sensor arrays reflect the myriad requirements of various emerging pervasive, ubiquitous and participatory sensing applications. In this paper, we introduce a zero-configuration and infrastructure-less smartphone based location specific occupancy estimation model. We opportunistically exploit smartphone’s acoustic sensors in a conversing environment and motion sensors in absence of any conversational data. We demonstrate a novel speaker estimation algorithm based on unsupervised clustering of overlapped and non-overlapped conversational data and a change point detection algorithm for locomotive motion of the users to infer the occupancy. We augment our occupancy detection model with a fingerprinting based methodology using smartphone’s magnetometer sensor to accurately assimilate location information of any gathering. We postulate a novel crowdsourcing-based approach to annotate the semantic location of the occupancy. We evaluate our algorithms in different contexts; conversational, silence and mixed in presence of 10 domestic users. Our experimental results on real-life data traces in natural settings show that using this hybrid approach, we can achieve approximately 0.76 error count distance for occupancy detection accuracy on average. 

Green building applications need to efficiently communicate fine-grained power consumption patterns of a wide variety of consumer-grade appliances for an effective adaptation and percolation of demand response models in the home environment. A key hurdle to the widespread adoption of such demand response policies in these appliances is the lack of efficient connectivity to a local area network. One solution is delivering telemetry data over existing electrical infrastructure to which the devices are already connected. The use of existing wiring produces a simple and cost-effective solution, avoiding many issues observed with wireless mesh networks (such as islands and bottlenecks), while helping to vacate increasingly congested spectrum. In this paper we explore the feasibility and efficacy of Power-line Communications (PLC) as a backbone of wireless communications in a home environment. We evaluate the behavior of several state-of-the art PLC modems using end-to-end measurements to establish their performance and throughput characteristics. Our preliminary results suggest that PLC is a promising technology for low-bandwidth hungry green building applications but more in depth study is required before making large-scale smart grid deployment.