Low-power wireless communication has been widely used in cyber-physical systems that require time-critical data delivery. Achieving this goal is challenging because of link burstiness and interference. Based on significant empirical evidence of 21 days and over 3.6 M packet transmissions per link, we propose both routing and scheduling algorithms that produce latency bounds of the real-time periodic streams and accounts for both link bursts and interference. The solution is achieved through the definition of a new metric B max that characterizes links by their maximum burst length, and by choosing a novel least-burst-route that minimizes the sum of worst-case burst lengths over all links in the route. With extensive data-driven analysis, we show that our algorithms outperform existing solutions by achieving accurate latency bound with much less energy consumption. In addition, a testbed evaluation consisting of 48 nodes spread across a floor of a building shows that we obtain 100% reliable packet delivery within derived latency bounds. We also demonstrate how performance deteriorates and discuss its implications for wireless networks with insufficient high-quality links.

Advances in wireless sensor networks have enabled the monitoring of daily activities of elderly people. The goal of these monitoring applications is to learn normal behavior in terms of daily activities and look for any deviation, i.e., Anomalies, so that alerts can be sent to relatives or caregivers. However, human behavior is very complex, and many existing anomaly detection systems are too simplistic which cause many false alarms, resulting in unreliable systems. We present Holmes, a comprehensive anomaly detection system for daily in-home activities. Holmes accurately learns a resident's normal behavior by considering variability in daily activities based not only on a per day basis, but also considering specific days of the week, different time periods such as per week and per month, and collective, temporal, and correlation based features. This approach of learning complicated normal behaviors reduces false alarms. Also, based on resident and expert feedback, Holmes learns semantic rules that explain specific variations of activities in specific scenarios to further reduce false alarms. We evaluate Holmes using data collected from our own deployed system, public data sets, and data collected by a senior safety system provider company from an elderly resident's home. Our evaluation shows that compared to state of the art systems, Holmes reduces false positives and false negatives by at least 46% and 27%, respectively.

We monitor activities of daily living of smart home residents to detect anomalies in their behavior. Unlike traditional anomaly detection systems, we aim to reduce false positives in anomaly detection with the help of semantic rules. Some of these rules are predefined based on expert knowledge and the rest are learned by the system with the help of resident/expert feedback. We also correlate trend of change in different activities to improve anomaly detection. In addition to monitor statistical deviation from regular behavior, we also detect deviation from healthy and social norms (defined by experts) as anomalies.

Inexpensive wireless sensing products are dramatically reducing the cost of in-home sensing. However, these sensors have been found to fail often and prohibitive maintenance costs may negate the cost benefits of inexpensive hardware and do-it-yourself installation. In this paper, we describe a new technique called SMART that uses application-level semantics to detect, assess, and adapt to sensor failures. SMART detects sensor failures at run-time by analyzing the relative behavior of multiple classifier instances trained to recognize the same set of activities based on different subsets of sensors. Once a failure is detected, SMART assesses its importance and adapts the classifier ensemble in attempt to avoid maintenance dispatch. Evaluation on three homes from two public datasets shows that SMART decreases the number of maintenance dispatches by 55% on average, identifies non-fail-stop failures at run-time with more than 85% accuracy, and improves the activity recognition accuracy under sensor failures by 15% on average.

This paper describes a novel group-based programming abstraction called a “Bundle” for cyber-physical systems (CPS). Similar to other programming abstractions, a Bundle creates logical collections of sensing devices. However, previous abstractions were focused on wireless sensor networks (WSNs) and did not address key aspects of CPS. Bundles elevate the programming domain from a single WSN to complex systems of systems by allowing the programming of applications involving multiple CPSs that are controlled by different administrative domains and support mobility both within and across CPSs. Bundles can seamlessly group not only sensors, but also actuators which constitute an important part of CPS. They enable programming in a multiuser environment with fine grained access right control and conflict resolution mechanism. Bundles support heterogeneous devices, such as motes, PDAs, laptops, and actuators according to the applications' requirements. They allow different applications to simultaneously use the same sensors and actuators. Bundles facilitate feedback control mechanisms by dynamic membership update and requirements reconfiguration based on feedback from the current members. The Bundle abstraction is implemented in Java which ensures ease and conciseness of programming. We present the design and implementation details of Bundles as well as a performance evaluation using 32 applications written with Bundles. This set includes across-network applications that have sophisticated sensing and actuation logic, mobile nodes that are heterogeneous, and feedback control mechanisms. Each of these applications is programmed in less than 60 lines of code.

It is very important to monitor quantity and quality of sleeping of elderly people. This paper presents a novel sleeping monitoring system based on RFID-based sensors called WISPs (Wireless identification and Sensing Platform). Main advantages of our system are that these WISP devices are noninvasive, inexpensive and do not require batteries. Our system provides feedback to the users/doctors about irregularity in sleeping. Results from our initial experiments show that our system can successfully detect sleeping periods and movements during sleeping.

As wireless sensor networks mature, they are increasingly being used in real-time applications. Many of these applications require reliable transmission within latency bounds. Achieving this goal is very difficult because of link burstiness and interference. Based on significant empirical evidence of 21 days and over 3,600,000 packets transmission per link, we propose a scheduling algorithm that produces latency bounds of the real-time periodic streams and accounts for both link bursts and interference. The solution is achieved through the definition of a new metric Bmax that characterizes links by their maximum burst length, and by choosing a novel least-burst-route that minimizes the sum of worst case burst lengths over all links in the route. A testbed evaluation consisting of 48 nodes spread across a floor of a building shows that we obtain 100% reliable packet delivery within derived latency bounds. We also demonstrate how performance deteriorates and discuss its implications for wireless networks with insufficient high quality links.

This paper describes the design and implementation of a pervasive computing framework, named Physicalnet. Essentially, Physicalnet is a generic paradigm for managing and programming world-wide distributed heterogeneous sensor and actuator resources in a multi-user and multi-network environment. Using a four-tier light-weight service oriented architecture, Physicalnet enables global uniform access to heterogeneous resources and decouples applications from particular resources, locations and networks. Through a negotiator module, it allows a large number of applications to concurrently execute on the same resources and to span multiple physical networks and logical administrative domains. By providing a fine-grained use-based access rights control and conflict resolution mechanism, Physicalnet not only ensures owners having total control of sharing and protecting their resources, but also dramatically increases the number of applications that can concurrently execute on the devices. Furthermore, Physicalnet supports resource dynamic location-aware mobility, application run-time reconfigurability and on-the-fly access rights specification. To quantify the performance, we evaluate Physicalnet based on memory usage, the number of concurrent applications, and dynamic responsiveness. The results show Physicalnet has excellent performance, but low overheads.

Physicalnet is a fully implemented middleware for wireless networks of sensors and actuators (WNSAs). Based on a lightweight service oriented architecture (SOA), Physicalnet allows the users to create administrative domains, in which owners of the nodes can share and protect their resources so that multiple applications can simultaneously use the same sensors and actuators with conflict resolution mechanisms. By using a general programming abstraction, named bundle, Physicalnet allows programming across networks and administrative domains, and has a good support for node mobility. Other features of Physicalnet include using the Java language, supporting heterogeneous devices, and providing a chain of user tools.