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.

One of the primary requirements in many cyber-physical systems (CPS) is that the sensor data derived from the physical world should be disseminated in a timely and reliable manner to all interested collaborative entities. However, providing reliable and timely data dissemination services is especially challenging for CPS since they often operate in highly unpredictable environments. Existing network middleware has limitations in providing such services. In this paper, we present a novel publish/subscribe-based middleware architecture called Real-time Data Distribution Service (RDDS). In particular, we focus on two mechanisms of RDDS that enable timely and reliable sensor data dissemination under highly unpredictable CPS environments. First, we discuss the semantics-aware communication mechanism of RDDS that not only reduces the computation and communication overhead, but also enables the subscribers to access data in a timely and reliable manner when the network is slow or unstable. Further, we extend the semantics-aware communication mechanism to achieve robustness against unpredictable workloads by integrating a control-theoretic feedback controller at the publishers and a queueing-theoretic predictor at the subscribers. This integrated control loop provides Quality-of-Service (QoS) guarantees by dynamically adjusting the accuracy of the sensor models. We demonstrate the viability of the proposed approach by implementing a prototype of RDDS. The evaluation results show that, compared to baseline approaches, RDDS achieves highly efficient and reliable sensor data dissemination as well as robustness against unpredictable workloads.

Achieving situation awareness is especially challenging for real-time data stream applications because they i) operate on continuous unbounded streams of data, and ii) have inherent real-time requirements. In this paper we show how formal data stream modeling and analysis can be used to better understand stream behavior, evaluate query costs, and improve application performance. We use MEDAL, a formal specification language based on Petri nets, to model the data stream queries and the Quality-of-Service (QoS) management mechanisms in a data stream system. MEDAL's ability to combine query logic and data admission control in one model allows us to design a single comprehensive model of the system. This model can be used to perform a large set of analyses to help improve the application's performance and QoS.

Continuous and reliable operation of WSNs is notoriously difficult to guarantee due to hardware degradation and environmental changes. In this paper, we propose and demonstrate a methodology for run-time assurance (RTA), in which we validate at run time that a WSN will function correctly, despite any changes to the operating conditions since it was originally designed and deployed. We use program analysis and compiler techniques to facilitate automated testing of a WSN at run time. As a proof of concept, we implemented a framework for designing and automatically testing WSN applications. We evaluate our implementation on a network of 21 TelosB nodes, and compare performance with an existing network health monitoring solution. Our results indicate that in addition to providing the application-level verification function, RTA misses 75% fewer system failures, produces 70% fewer maintenance dispatches, and incurs 33% less messaging overhead than network health monitoring.

The current rapid development and deployment of wireless sensor networks (WSNs) and their application in mission critical systems are exacerbating the need for high confidence WSNs. Achieving high confidence WSNs will require new assurance technologies. Most current solutions deal with faults and reliability and not with application level semantics and associated assurances. We propose the use of a novel WSN design and assurance mechanism, run time assurance (RTA), to guarantee that important application-level requirements are met in mission critical applications.