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Real-world applications often involve irregular time series, for which the time intervals between successive observations are non-uniform. Irregularity across multiple features in a multi-variate time series further results in a different subset of features at any given time (i.e., asynchronicity). Existing pre-training schemes for time-series, however, often assume regularity of time series and make no special treatment of irregularity. We argue that such irregularity offers insight about domain property of the data—for example, frequency of hospital visits may signal patient health condition—that can guide representation learning. In this work, we propose PrimeNet to learn a self-supervised representation for irregular multivariate time-series. Specifically, we design a timesensitive contrastive learning and data reconstruction task to pre-train a model. Irregular time-series exhibits considerable variations in sampling density over time. Hence, our triplet generation strategy follows the density of the original data points, preserving its native irregularity. Moreover, the sampling density variation over time makes data reconstruction difficult for different regions. Therefore, we design a data masking technique that always masks a constant time duration to accommodate reconstruction for regions of different sampling density. We learn with these tasks using unlabeled data to build a pre-trained model and fine-tune on a downstream task with limited labeled data, in contrast with existing fully supervised approach for irregular time-series, requiring large amounts of labeled data. Experiment results show that PrimeNet significantly outperforms state-of-the-art methods on naturally irregular and asynchronous data from Healthcare and IoT applications for several downstream tasks, including classification, interpolation, and regression. 

Cyber-physical systems are starting to adopt neural network (NN) models for a variety of smart sensing applications. While several efforts seek better NN architectures for system performance improvement, few attempts have been made to study the deployment of these systems in the field. Proper deployment of these systems is critical to achieving ideal performance, but the current practice is largely empirical via trials and errors, lacking a measure of quality. Sensing quality should reflect the impact on the performance of NN models that drive machine perception tasks. However, traditional approaches either evaluate statistical difference that exists objectively, or model the quality subjectively via human perception. In this work, we propose an efficient sensing quality measure requiring limited data samples using smart voice sensing system as an example. We adopt recent techniques in uncertainty evaluation for NN to estimate audio sensing quality. Intuitively, a deployment at better sensing location should lead to less uncertainty in NN predictions. We design SQEE, Sensing Quality Evaluation at the Edge for NN models, which constructs a model ensemble through Monte-Carlo dropout and estimates posterior total uncertainty via average conditional entropy. We collected data from three indoor environments, with a total of 148 transmitting-receiving (t-r) locations experimented and more than 7,000 examples tested. SQEE achieves the best performance in terms of the top-1 ranking accuracy---whether the measure finds the best spot for deployment, in comparison with other uncertainty strategies. We implemented SQEE on a ReSpeaker to study SQEE's real-world efficacy. Experimental result shows that SQEE can effectively evaluate the data collected from each t-r location pair within 30 seconds and achieve an average top-3 ranking accuracy of over 94%. We further discuss generalization of our framework to other sensing schemes. 

Emerging building analytics rely on data-driven machine learning algorithms. However, writing these analytics is still challenging— developers need to know not only what data are required by the analytics but also how to reach the data in each individual building, despite the existing solutions to standardizing data and resource management in buildings. To bridge the gap between analytics development and the specific details of reaching actual data in each building, we present Energon, an open-source system that enables portable building analytics. The core of Energon is a new data organization for building as well as tools that can effectively manage building data and support building analytics development. More specifically, we propose a new "logic partition" of data resources in buildings, and this abstraction universally applies to all buildings. We develop a declarative query language accordingly to f ind data resources in this new logic view with high-level queries, thus substantially reducing development efforts. We also develop a query engine with automatic data extraction by traversing building ontology that widely exists in buildings. In this way, Energon enables mapping of analytics requirements to building resources in a building-agnostic manner. Using four types of real-world building analytics, we demonstrate the use of Energon and its effectiveness in reducing development efforts. 

Smart Building Technologies hold promise for better livability for residents and lower energy footprints. Yet, the rollout of these technologies, from demand response controls to fault detection and diagnosis, significantly lags behind and is impeded by the current practice of manual identification of sensing point relationships, e.g., how equipment is connected or which sensors are co-located in the same space. This manual process is still error-prone, albeit costly and laborious.We study relation inference among sensor time series. Our key insight is that, as equipment is connected or sensors co-locate in the same physical environment, they are affected by the same real-world events, e.g., a fan turning on or a person entering the room, thus exhibiting correlated changes in their time series data. To this end, we develop a deep metric learning solution that first converts the primitive sensor time series to the frequency domain, and then optimizes a representation of sensors that encodes their relations. Built upon the learned representation, our solution pinpoints the relationships among sensors via solving a combinatorial optimization problem. Extensive experiments on real-world buildings demonstrate the effectiveness of our solution. 

Smart-home devices promise to make users’ lives more convenient. However, at the same time, such devices increase the possibility of breaching users’ privacy as they are tightly connected to the users’ daily lives and activities. To address privacy invasion through smart-home devices, we present ChatterHub. This novel approach accurately identifies smart-home devices’ activities with minimal monitoring of encrypted traffic in the home network. ChatterHub targets devices that can only connect to the Internet through a centralized smart-home hub (e.g., Samsung SmartThings) using Zigbee or Z-wave. Specifically, ChatterHub passively eavesdrops on encrypted network traffic from the hub and leverages machine learning techniques to classify events and states of smart-home devices. Using ChatterHub, an adversary can identify smart-home devices’ specific activities without prior knowledge of the target smart home (e.g., list of deployed devices, types of communication protocols). We evaluated the accuracy and efficiency of ChatterHub in three real-world smart-home environments, and the evaluation results show that an attacker can successfully disclose smart-home devices’ behaviors with over 88% F1 score. We further demonstrate that ChatterHub successfully recognizes privacy-sensitive activities, including open and close of a smart door lock and turn on and off of smart LED. Additionally, to mitigate the threats posed by ChatterHub, we introduce two approaches, packet padding and random sequence injection. These mitigation approaches can effectively prevent threats from ChatterHub with only 9.2MB of additional network traffic per day. 

A key barrier to applying any smart technology to a building is the requirement of locating and connecting to the necessary resources among the thousands of sensing and control points, i.e., the metadata mapping problem. Existing solutions depend on exhaustive manual annotation of sensor metadata - a laborious, costly, and hardly scalable process. To reduce the amount of manual effort required, this paper presents a multi-oracle selective sampling framework to leverage noisy labels from information sources with unknown reliability such as existing buildings, which we refer to as weak oracles, for metadata mapping. This framework involves an interactive process, where a small set of sensor instances are progressively selected and labeled for it to learn how to aggregate the noisy labels as well as to predict sensor types. Two key challenges arise in designing the framework, namely, weak oracle reliability estimation and instance selection for querying. To address the first challenge, we develop a clustering-based approach for weak oracle reliability estimation to capitalize on the observation that weak oracles perform differently in different groups of instances. For the second challenge, we propose a disagreement-based query selection strategy to combine the potential effect of a labeled instance on both reducing classifier uncertainty and improving the quality of label aggregation. We evaluate our solution on a large collection of real-world building sensor data from 5 buildings with more than 11, 000 sensors of 18 different types. The experiment results validate the effectiveness of our solution, which outperforms a set of state-of-the-art baselines.