Pervasive computing applications commonly involve user's personal smartphones collecting data to influence application behavior. Applications are often backed by models that learn from the user's experiences to provide personalized and responsive behavior. While models are often pre-trained on massive datasets, federated learning has gained attention for its ability to train globally shared models on users' private data without requiring the users to share their data directly. However, federated learning requires devices to collaborate via a central server, under the assumption that all users desire to learn the same model. We define a new approach, opportunistic federated learning, in which individual devices belonging to different users seek to learn robust models that are personalized to their user's own experiences. However, instead of learning in isolation, these models opportunistically incorporate the learned experiences of other devices they encounter opportunistically. In this paper, we explore the feasibility and limits of such an approach, culminating in a framework that supports encounter-based pairwise collaborative learning. The use of our opportunistic encounter-based learning amplifies the performance of personalized learning while resisting overfitting to encountered data.

With the rapid development and usage of Internet-of-Things (IoT) and smart-home devices, researchers continue efforts to improve the "smartness" of those devices to address daily needs in people's lives. Such efforts usually begin with understanding evolving user behaviors on how humans utilize the devices and what they expect in terms of their behavior. However, while research efforts abound, there is a very limited number of datasets that researchers can use to both understand how people use IoT devices and to evaluate algorithms or systems for smart spaces. In this paper, we collect and characterize more than 50,000 recipes from the online If-This-Then-That (IFTTT) service to understand a seemingly straightforward but complicated question: "What kinds of behaviors do humans expect from their IoT devices?" The dataset we collected contains the basic information of the IFTTT rules, trigger and action events, and how many people are using each rule.

Advances in mobile computing capabilities and an increasing number of Internet of Things (IoT) devices have enriched the possibilities of the IoT but have also increased the cognitive load required of IoT users. Existing context-aware systems provide various levels of automation in the IoT. Many of these systems adaptively take decisions on how to provide services based on assumptions made a priori. The approaches are difficult to personalize to an individual's dynamic environment, and thus today's smart IoT spaces often demand complex and specialized interactions with the user in order to provide tailored services. We propose rIoT, a framework for seamless and personalized automation of human-device interaction in the IoT. rIoT leverages existing technologies to operate across heterogeneous devices and networks to provide a one-stop solution for device interaction in the IoT. We show how rIoT exploits similarities between contexts and employs a decision-tree like method to adaptively capture a user's preferences from a small number of interactions with the IoT space. We measure the performance of rIoT on two real-world data sets and a real mobile device in terms of accuracy, learning speed, and latency in comparison to two state-of-the-art machine learning algorithms.

The domain of access control has long suffered from a lack of expressiveness in specifying access control policies. Recent approaches have leveraged contextual fingerprinting to formulate access control frameworks for both generating and enforcing access control policies. However, effectively and automatically identifying the context attributes relevant for access has proven challenging and cumbersome. An approach that shows promise in supporting more expressive and easy-to-use attribute-based access control relies on recent advances in continuous neighbor discovery protocols and low cost wireless communication technologies such as Bluetooth Low Energy (BLE). These technologies have created opportunities to build smart environments that can seamlessly and inexpensively provide rich contextual data. These capabilities have the potential to enable new transparent and automatic approaches to defining and evaluating access control policies for mobile users and for detecting anomalous access patterns in smart environments. In this paper, we present the LAD framework that uses raw contextual data available via technologies such as BLE to derive real-time attributes defined by the presence of mobile and static nodes in the nearby environment. Based on user interactions in these environments, our framework learns appropriate access control policies and enforces these policies based on attributes that change in real-time as users move in the smart environment.