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Pervasive sensing has enabled continuous monitoring of user physiological state through mobile and wearable devices, allowing for large scale user studies to be conducted, such as those found in mHealth. However, current mHealth studies are limited in their ability of allowing users to express their privacy preferences on the data they share across multiple entities involved in a research study. In this work, we present mPolicy, a privacy policy language for study participants to express the context-aware and data-handling policies needed for mHealth. In addition, we provide a privacy-adaptive policy creation mechanism for byproduct data (such as motion inferences). Lastly, we create a software library called privLib for implementing parsing, enforcement, and policy creation on byproduct data for mPolicy. We evaluate the latency overhead of these operations, and discuss future improvements for scaling to realistic mHealth scenarios. 

Buildings account for 32% of worldwide energy usage. A new regime of exciting new “applications” that span a distributed fabric of sensors, actuators and humans has emerged to improve building energy efficiency and operations management. These applications leverage the technological advances in embedded sensing, processing, networking and methods by which they can be coupled with supervisory control and data acquisition systems deployed in modern buildings and with users on mobile wireless platforms. There are, however, several technical challenges to confront before such a vision of smart building applications and cyber-physical systems can be realized. The sensory data produced by these systems need significant curation before it can be used meaningfully. This is largely a manual, cost-prohibitive task and hence such solutions rarely experience widespread adoption due to the lack of a common descriptive schema. Recent attempts have sought to address this through data standards and metadata schemata but fall short in capturing the richness of relationships required by applications. This paper describes Brick, a uniform metadata schema for representing buildings that builds upon recent advances in the area. Our schema defines a concrete ontology for sensors, subsystems and the relationships between them, which enables portable applications. We demonstrate the completeness and effectiveness of Brick by using it to represent the entire vendor-specific sensor metadata of six diverse buildings across different campuses, comprising 17,700 data points, and running eight unmodified energy efficiency applications on these buildings. 

Edge devices rely extensively on machine learning for intelligent inferences and pattern matching. However, edge devices use a multitude of sensing modalities and are exposed to wide ranging contexts. It is difficult to develop separate machine learning models for each scenario as manual labeling is not scalable. To reduce the amount of labeled data and to speed up the training process, we propose to transfer knowledge between edge devices by using unlabeled data. Our approach, called RecycleML, uses cross modal transfer to accelerate the learning of edge devices across different sensing modalities. Using human activity recognition as a case study, over our collected CMActivity dataset, we observe that RecycleML reduces the amount of required labeled data by at least 90% and speeds up the training process by up to 50 times in comparison to training the edge device from scratch. 

Traditional machine learning approaches for recognizing modes of transportation rely heavily on hand-crafted feature extraction methods which require domain knowledge. So, we propose a hybrid deep learning model: Deep Convolutional Bidirectional-LSTM (DCBL) which combines convolutional and bidirectional LSTM layers and is trained directly on raw sensor data to predict the transportation modes. We compare our model to the traditional machine learning approaches of training Support Vector Machines and Multilayer Perceptron models on extracted features. In our experiments, DCBL performs better than the feature selection methods in terms of accuracy and simplifies the data processing pipeline. The models are trained on the Sussex-Huawei Locomotion-Transportation (SHL) dataset. The submission of our team, Vahan, to SHL recognition challenge uses an ensemble of DCBL models trained on raw data using the different combination of sensors and window sizes and achieved an F1-score of 0.96 on our test data. 

Today large amount of data is generated by cities. Many of the datasets are openly available and are contributed by different sectors, government bodies and institutions. The new data can affect our understanding of the issues faced by cities and can support evidence based policies. However usage of data is limited due to difficulty in assimilating data from different sources. Open datasets often lack uniform structure which limits its analysis using traditional database systems. In this paper we present Citadel, a data hub for cities. Citadel's goal is to support end to end knowledge discovery cyber-infrastructure for effective analysis and policy support. Citadel is designed to ingest large amount of heterogeneous data and supports multiple use cases by encouraging data sharing in cities. Our poster presents the proposed features, architecture, implementation details and initial results. 

Commercial buildings have long since been a primary target for applications from a number of areas: from cyber-physical systems to building energy use to improved human interactions in built environments. While technological advances have been made in these areas, such solutions rarely experience widespread adoption due to the lack of a common descriptive schema which would reduce the now-prohibitive cost of porting these applications and systems to different buildings. Recent attempts have sought to address this issue through data standards and metadata schemes, but fail to capture the set of relationships and entities required by real applications. Building upon these works, this paper describes Brick, a uniform schema for representing metadata in buildings. Our schema defines a concrete ontology for sensors, subsystems and relationships among them, which enables portable applications. We demonstrate the completeness and effectiveness of Brick by using it to represent the entire vendor-specific sensor metadata of six diverse buildings across different campuses, comprising 17,700 data points, and running eight complex unmodified applications on these buildings.