Zakirul Alam Bhuiyan

Deploying Wireless Sensor Networks with Fault-Tolerance for Structural Health Monitoring

Structural health monitoring (SHM) systems are implemented for structures (e.g., bridges, buildings) to monitor their operations and health status. Wireless sensor networks (WSNs) are becoming an enabling technology for SHM applications that are more prevalent and more easily deployable than traditional wired networks. However, SHM brings new challenges to WSNs: engineering-driven optimal deployment, a large volume of data, sophisticated computing, and so forth. In this paper, we address two important challenges: sensor deployment and decentralized computing. We propose a solution, to deploy wireless sensors at strategic locations to achieve the best estimates of structural health (e.g., damage) by following the widely used wired sensor system deployment approach from civil/structural engineering. We found that faults (caused by communication errors, unstable connectivity, sensor faults, etc.) in such a deployed WSN greatly affect the performance of SHM. To make the WSN resilient to the faults, we present an approach, called

Local Area Prediction-Based Mobile Target Tracking in Wireless Sensor Networks

{\tt FTSHM}

Detecting Movements of a Target Using Face Tracking in Wireless Sensor Networks

(fault-tolerance in SHM), to repair the WSN and guarantee a specified degree of fault tolerance.

Dependable Structural Health Monitoring Using Wireless Sensor Networks

{\tt FTSHM}

Understanding Graph-Based Trust Evaluation in Online Social Networks: Methodologies and Challenges

searches the repairing points in clusters in a distributed manner, and places a set of backup sensors at those points in such a way that still satisfies the engineering requirements.

Auction-based adaptive sensor activation algorithm for target tracking in wireless sensor networks

{\tt FTSHM}

Energy and bandwidth-efficient Wireless Sensor Networks for monitoring high-frequency events

also includes an SHM algorithm suitable for decentralized computing in the energy-constrained WSN, with the objective of guaranteeing that the WSN for SHM remains connected in the event of a fault, thus prolonging the WSN lifetime under connectivity and data delivery constraints. We demonstrate the advantages of

Sensing and Decision Making in Cyber-Physical Systems: The Case of Structural Event Monitoring

{\tt FTSHM}

e-Sampling: Event-Sensitive Autonomous Adaptive Sensing and Low-Cost Monitoring in Networked Sensing Systems

through extensive simulations and real experimental settings on a physical structure.

Following Targets for Mobile Tracking in Wireless Sensor Networks

Tracking mobile targets in wireless sensor networks (WSNs) has many important applications. As it is often the case in prior work that the quality of tracking (QoT) heavily depends on high accuracy in localization or distance estimation, which is never perfect in practice. These bring a cumulative effect on tracking, e.g., target missing. Recovering from the effect and also frequent interactions between nodes and a central server result in a high energy consumption. We design a tracking scheme, named t-Tracking, aiming to achieve two major objectives: high QoT and high energy efficiency of the WSN. We propose a set of fully distributed tracking algorithms, which answer queries like whether a target remains in a “specific area” (called a “face” in localized geographic routing, defined in terms of radio connectivity and local interactions of nodes). When a target moves across a face, the nodes of the face that are close to its estimated movements compute the sequence of the targets movements and predict when the target moves to another face. The nodes answer queries from a mobile sink called the “tracker”, which follows the target along with the sequence. t-Tracking has advantages over prior work as it reduces the dependency on requiring high accuracy in localization and the frequency of interactions. It also timely solves the target missing problem caused by node failures, obstacles, etc., making the tracking robust in a highly dynamic environment. We validate its effectiveness considering the objectives in extensive simulations and in a proof-of-concept system implementation.