Deploying Wireless Sensor Networks with Fault Tolerance for Structural Health Monitoring

8th IEEE International Conference on Distributed Computing in Sensor Systems, DCOSS 2012, Hangzhou, 16-18 May 2012Structural health monitoring (SHM) brings new challenges to wireless sensor networks (WSNs) : large volume of data, sophisticated computing, engineering-driven optimal deployment, and so forth. In this paper, we address two important challenges: sensor placement and decentralized computing. We propose a solution to place sensors at strategic locations to achieve the best estimates of geometric properties of a structure. To make the deployed network resilient to faults caused by communication errors, unstable network connectivity, and sensor faults, we present an approach, called FTSHM (fault tolerance in SHM), to repairing the network to guarantee a specified degree of fault tolerance. FTSHM searches the repairing points in clusters and places a set of backup sensors at those points by satisfying civil engineering requirements. FTSHM also includes a SHM algorithm suitable for decentralized computing in energy-constrained WSNs, with the objective to guarantee that the WSN for SHM remains connected in the event of a sensor fault thus prolonging the WSN lifetime under connectivity and data delivery constraints. We demonstrate the advantages of FTSHM through simulations and experiments on a real civil structure.Department of ComputingRefereed conference pape

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

2013 10th Annual IEEE Communications Society Conference on Sensing and Communication in Wireless Networks, SECON 2013, New Orleans, LA, 24-27 June 2013Wireless Sensor Networks (WSNs) are mostly deployed to detect events (i.e., objects or physical changes) at a high/low frequency sampling that is usually adapted by a central unit (or a sink), thus requiring additional resource usage in WSNs. However, the problem of autonomous adaptive sampling regarding the detection of events has not been studied before. In this paper, we propose a novel scheme, termed 'event-sensitive adaptive sampling and low-cost monitoring (e-Sampling)' by addressing the problem in two stages, which lead to reduced resource usage (e.g., energy, radio bandwidth) in WSNs. First, e-Sampling provides a solution to adaptive sampling that automatically switches between high- and low-frequency intervals to reduce the resource usage while minimizing false negative detections. Second, by analyzing the frequency content, e-Sampling presents an event identification algorithm suitable for decentralized computing in resource-constrained WSNs. In the absence of an event, 'uninteresting' data is not transmitted to the sink. We apply e-Sampling to structural health monitoring (SHM), which is a typical application of high frequency events. Evaluation via both simulations and experiments validates the advantages of e-Sampling in low-cost event monitoring, and in expanding the capacity of WSNs for high data rate applications.Department of ComputingRefereed conference pape

Local Monitoring and Maintenance for Operational Wireless Sensor Networks

12th IEEE International Conference on Trust, Security and Privacy in Computing and Communications, TrustCom 2013, Melbourne, VIC, 16-18 July 2013Best Paper AwardOne of the key mechanisms underlying a wireless sensor network (WSN) is to monitor the network itself. Many existing approaches perform centralized analysis and maintenance based on a large amount of status reports collected from the WSN, while others use add-on protocols/modules that not only require extra management cost, but also interrupt the normal operations of targeted WSN applications. Unlike existing work, we propose LoMoM, a new approach of Local Monitoring and Maintenance for a WSN, which combines monitoring operations for the WSN with the operations of a mobile event monitoring, in a manner that is both energy and latency-efficient. LoMoM includes a two-part monitoring architecture: a WSN and a 3G network. Our main interest is in the WSN-part, where we address two important issues: monitoring probable anomalies/faults of the nodes, and link failures. To achieve event monitoring efficiently, LoMoM conducts a prompt local maintenance when such a fault occurs. Fault and event detection status reports are observed by a remote monitoring center using the 3G network. Comprehensive evaluations via both simulations and real-world experiments are conducted to validate the effectiveness of LoMoM.Department of ComputingRefereed conference pape

Energy-Efficient and Fault-Tolerant Structural Health Monitoring in Wireless Sensor Networks

31st IEEE International Symposium on Reliable Distributed Systems, SRDS 2012, Irvine, CA, 8-11 October 2012Wireless sensor networks (WSNs) have become an increasingly compelling platform for structural health monitoring (SHM) due to relatively low-cost, easy installation, etc. However, the challenge of effectively monitoring structural health condition (e.g., damage) under WSN constraints (e.g., limited energy, narrow bandwidth) and sensor faults has not been studied before. In this paper, we focus on tolerating sensor faults in WSN-based SHM. We design a distributed WSN framework for SHM and then examine its ability to cope with sensor faults. We bring attention to an undiscovered yet interesting fact, i.e., the real measured signals introduced by faulty sensors may cause an undamaged location to be identified as damaged (false positive) or a damaged location as undamaged (false negative) diagnosis. This can be caused by faults in sensor bonding, precision degradation, amplification gain, bias, drift, noise, and so forth. We present a distributed algorithm to detect such types of faults, and offer an online signal reconstruction algorithm to recover from the wrong diagnosis. Through simulations and a WSN prototype system, we evaluate the effectiveness of our proposed algorithms.Department of ComputingRefereed conference pape

Modelling attacks in blockchain systems using petri nets

Blockchain technology has evolved through many changes and modifications, such as smart-contracts since its inception in 2008. The popularity of a blockchain system is due to the fact that it offers a significant security advantage over other traditional systems. However, there have been many attacks in various blockchain systems, exploiting different vulnerabilities and bugs, which caused a significant financial loss. Therefore, it is essential to understand how these attacks in blockchain occur, which vulnerabilities they exploit, and what threats they expose. Another concerning issue in this domain is the recent advancement in the quantum computing field, which imposes a significant threat to the security aspects of many existing secure systems, including blockchain, as they would invalidate many widely-used cryptographic algorithms. Thus, it is important to examine how quantum computing will affect these or other new attacks in the future. In this paper, we explore different vulnerabilities in current blockchain systems and analyse the threats that various theoretical and practical attacks in the blockchain expose. We then model those attacks using Petri nets concerning current systems and future quantum computers

A Genetic Algorithm approach to motion sensor placement in smart environments

Smart environments and ubiquitous computing technologies hold great promise for a wide range of real world applications. The medical community is particularly interested in high quality measurement of activities of daily living. With accurate computer modeling of older adults, decision support tools may be built to assist care providers. One aspect of effectively deploying these technologies is determining where the sensors should be placed in the home to effectively support these end goals. This work introduces and evaluates a set of approaches for generating sensor layouts in the home. These approaches range from the gold standard of human intuition-based placement to more advanced search algorithms, including Hill Climbing and Genetic Algorithms. The generated layouts are evaluated based on their ability to detect activities while minimizing the number of needed sensors. Sensor-rich environments can provide valuable insights about adults as they go about their lives. These sensors, once in place, provide information on daily behavior that can facilitate an aging-in-place approach to health care