Wireless sensor network for structural health monitoring: a contemporary review of technologies, challenges, and future direction

The importance of wireless sensor networks in structural health monitoring is unceasingly growing, because of the increasing demand for both safety and security in the cities. The speedy growth of wireless technologies has considerably developed the progress of structural monitoring systems with the combination of wireless sensor network technology. Wireless sensor network–based structural health monitoring system introduces a novel technology with compelling advantages in comparison to traditional wired system, which has the benefits of reducing installation and maintenance costs of structural health monitoring systems. However, structural health monitoring has brought an additional complex challenges in network design to wireless sensor networks. This article presents a contemporary review of collective experience the researchers have gained from the application of wireless sensor networks for structural health monitoring. Technologies of wired and wireless sensor systems are investigated along with wireless sensor node architecture, functionality, communication technologies, and its popular operating systems. Then, comprehensive summaries for the state-of-the-art academic and commercial wireless platform technologies used in laboratory testbeds and field test deployments for structural health monitoring applications are reviewed and tabulated. Following that, classification taxonomy of the key challenges associated with wireless sensor networks for structural health monitoring to assist the researchers in understanding the obstacles and the suitability of implementing wireless technology for structural health monitoring applications are deeply discussed with available research efforts in order to overcome these challenges. Finally, open research issues in wireless sensor networks for structural health monitoring are explored

ShakeNet: A portable wireless sensor network for instrumenting large civil structures

We report our findings from a U.S. Geological Survey (USGS) National Earthquake Hazards Reduction Program-funded project to develop and test a wireless, portable, strong-motion network of up to 40 triaxial accelerometers for structural health monitoring. The overall goal of the project was to record ambient vibrations for several days from USGS-instrumented structures. Structural health monitoring has important applications in fields like civil engineering and the study of earthquakes. The emergence of wireless sensor networks provides a promising means to such applications. However, while most wireless sensor networks are still in the experimentation stage, very few take into consideration the realistic earthquake engineering application requirements. To collect comprehensive data for structural health monitoring for civil engineers, high-resolution vibration sensors and sufficient sampling rates should be adopted, which makes it challenging for current wireless sensor network technology in the following ways: processing capabilities, storage limit, and communication bandwidth. The wireless sensor network has to meet expectations set by wired sensor devices prevalent in the structural health monitoring community. For this project, we built and tested an application-realistic, commercially based, portable, wireless sensor network called ShakeNet for instrumentation of large civil structures, especially for buildings, bridges, or dams after earthquakes. Two to three people can deploy ShakeNet sensors within hours after an earthquake to measure the structural response of the building or bridge during aftershocks. ShakeNet involved the development of a new sensing platform (ShakeBox) running a software suite for networking, data collection, and monitoring. Deployments reported here on a tall building and a large dam were real-world tests of ShakeNet operation, and helped to refine both hardware and software

Fault Discrimination in Wireless Sensor Networks

In current times, one of the promising and interesting areas of research is Wireless Sensor Networks. A Wireless Sensor Network consists of spatially distributed sensors to monitor environmental and physical conditions such as temperature, sound, pressure etc. It is built of nodes where each node is connected to one or more sensors. They are used for Medical applications, Security monitoring, Structural monitoring and Traffic monitoring etc. The number of sensor nodes in a Wireless Sensor Network can vary in the range of hundreds to thousands. In this project work we propose a distributed algorithm for detection of faults in a Wireless Sensor Network and to classify the faulty nodes. In our algorithm the sensor nodes are classified as being Fault Free, Transiently Faulty or Intermittently Faulty considering the energy differences from its neighbors in different rounds of the algorithm run. We have shown the simulation results in the form of the output messages from the nodes depicting their health and also compared the results in form of graphs for different average node degrees and different number of rounds of our algorithm run

A wirelessly programmable actuation and sensing system for structural health monitoring

Wireless sensor networks promise to deliver low cost, low power and massively distributed systems for structural health monitoring. A key component of these systems, particularly when sampling rates are high, is the capability to process data within the network. Although progress has been made towards this vision, it remains a difficult task to develop and program ’smart’ wireless sensing applications. In this paper we present a system which allows data acquisition and computational tasks to be specified in Python, a high level programming language, and executed within the sensor network. Key features of this system include the ability to execute custom application code without firmware updates, to run multiple users’ requests concurrently and to conserve power through adjustable sleep settings. Specific examples of sensor node tasks are given to demonstrate the features of this system in the context of structural health monitoring. The system comprises of individual firmware for nodes in the wireless sensor network, and a gateway server and web application through which users can remotely submit their requests

Joint optimization for wireless sensor networks in critical infrastructures

Energy optimization represents one of the main goals in wireless sensor network design where a typical sensor node has usually operated by making use of the battery with limited-capacity. In this thesis, the following main problems are addressed: first, the joint optimization of the energy consumption and the delay for conventional wireless sensor networks is presented. Second, the joint optimization of the information quality and energy consumption of the wireless sensor networks based structural health monitoring is outlined. Finally, the multi-objectives optimization of the former problem under several constraints is shown. In the first main problem, the following points are presented: we introduce a joint multi-objective optimization formulation for both energy and delay for most sensor nodes in various applications. Then, we present the Karush-Kuhn-Tucker analysis to demonstrate the optimal solution for each formulation. We introduce a method of determining the knee on the Pareto front curve, which meets the network designer interest for focusing on more practical solutions. The sensor node placement optimization has a significant role in wireless sensor networks, especially in structural health monitoring. In the second main problem of this work, the existing work optimizes the node placement and routing separately (by performing routing after carrying out the node placement). However, this approach does not guarantee the optimality of the overall solution. A joint optimization of sensor placement, routing, and flow assignment is introduced and is solved using mixed-integer programming modelling. In the third main problem of this study, we revisit the placement problem in wireless sensor networks of structural health monitoring by using multi-objective optimization. Furthermore, we take into consideration more constraints that were not taken into account before. This includes the maximum capacity per link and the node-disjoint routing. Since maximum capacity constraint is essential to study the data delivery over limited-capacity wireless links, node-disjoint routing is necessary to achieve load balancing and longer wireless sensor networks lifetime. We list the results of the previous problems, and then we evaluate the corresponding results

Wireless sensor integration for bridge model health monitoring

An integrated hardware and software system for a scalable wireless sensor network (WSN) is designed and developed for structural health monitoring. An extension sensor board is designed, developed, and calibrated to meet the requirements for structural vibration monitoring and modal identification. The extension sensor board has 3 axes of accelerometers in three directions and a temperature sensor. Software components have been implemented within the TinyOS operating system to provide a flexible software platform and scalable performance for structural health monitoring applications. The prototype WSN is deployed on a reduced-scale bridge model with 3 nodes in a single-hop network for performing dynamic monitoring civil engineering structures. Two output-only time-domain system identification methods are employed namely, the Frequency Domain Decomposition (FDD) method and the Natural Excitation Technique (NExT) combined with the Eigensystem Realization Algorithm (ERA). Testing results show that the WSN provides accurate vibration data for identifying vibration modes of a bridge

Low power wireless sensor network for structural health monitoring of buildings using MEMS strain sensors and accelerometers

Within the MEMSCON project, a wireless sensor network was developed for structural health monitoring of buildings to assess earthquake damage. The sensor modules use custom-developed capacitive MEMS strain and 3D acceleration sensors and a low power readout application-specific integrated circuit (ASIC). A low power network architecture was implemented on top of an 802.15.4 media access control (MAC) layer in the 900MHz band. A custom patch antenna was designed in this frequency for optimal integration into the sensor modules. The strain sensor modules measure periodically or on-demand from the base station and obtain a battery lifetime of 12 years. The accelerometer modules record during an earthquake event, which is detected using a combination of the local acceleration data and remote triggering from the base station, based on the acceleration data from multiple sensors across the building. They obtain a battery lifetime of 2 years. The MEMS strain sensor and its readout ASIC were packaged in a custom package suitable for mounting onto a reinforcing bar inside the concrete and without constraining the moving parts of the MEMS strain sensor. The wireless modules, including battery and antenna, were packaged in a robust housing compatible with mounting in a building and accessible for maintenance such as battery replacement

Efficient Information Access in Data-Intensive Sensor Networks

Recent advances in wireless communications and microelectronics have enabled wide deployment of smart sensor networks. Such networks naturally apply to a broad range of applications that involve system monitoring and information tracking (e.g., fine-grained weather/environmental monitoring, structural health monitoring, urban-scale traffic or parking monitoring, gunshot detection, monitoring volcanic eruptions, measuring rate of melting glaciers, forest fire detection, emergency medical care, disaster response, airport security infrastructure, monitoring of children in metropolitan areas, product transition in warehouse networks etc.).Meanwhile, existing wireless sensor networks (WSNs) perform poorly when the applications have high bandwidth needs for data transmission and stringent delay constraints against the network communication. Such requirements are common for Data Intensive Sensor Networks (DISNs) implementing Mission-Critical Monitoring applications (MCM applications).We propose to enhance existing wireless network standards with flexible query optimization strategies that take into account network constraints and application-specific data delivery patterns in order to meet high performance requirements of MCM applications.In this respect, this dissertation has two major contributions: First, we have developed an algebraic framework called Data Transmission Algebra (DTA) for collision-aware concurrent data transmissions. Here, we have merged the serialization concept from the databases with the knowledge of wireless network characteristics. We have developed an optimizer that uses the DTA framework, and generates an optimal data transmission schedule with respect to latency, throughput, and energy usage. We have extended the DTA framework to handle location-based trust and sensor mobility. We improved DTA scalability with Whirlpool data delivery mechanism, which takes advantage of partitioning of the network. Second, we propose relaxed optimization strategy and develop an adaptive approach to deliver data in data-intensive wireless sensor networks. In particular, we have shown that local actions at nodes help network to adapt in worse network conditions and perform better. We show that local decisions at the nodes can converge towards desirable global network properties e.g.,high packet success ratio for the network. We have also developed a network monitoring tool to assess the state and dynamic convergence of the WSN, and force it towards better performance

Performance testing of a low power consumption wireless sensor communication system integrated with an energy harvesting power source

This paper presents the performance testing results of a wireless sensor communication system with low power consumption integrated with a vibration energy harvesting power source. The experiments focus on the system’s capability to perform continuous monitoring and to wirelessly transmit the data acquired from the sensors to a user base station, completely battery-free. Energy harvesting technologies together with system design optimisation for power consumption minimisation ensure the system’s energy autonomous capability demonstrated in this paper by presenting the promising testing results achieved following its integration with Structural Health Monitoring (SHM) and Body Area Network (BAN) applications

Market-based frequency domain decomposition for automated mode shape estimation in wireless sensor networks

Wireless sensing technology has paved the way for the cost-effective deployment of dense networks of sensing transducers within large structural systems. By leveraging the embedded computing power residing within networks of wireless sensors, it has been shown that powerful data analyses can be performed autonomously and in-network, without the need for central data processing. In this study, the power and flexibility of agent-based data processing in the wireless structural monitoring environment is illuminated through the application of market-based techniques to in-network mode shape estimation. Specifically, by drawing on previous wireless sensor work in both decentralized frequency domain decomposition (FDD) and market-based resource allocation, an algorithm derived from free-market principles is developed through which an agent-based wireless sensor network can autonomously and optimally shift emphasis between improving the accuracy of its mode shape calculations and reducing its dependency on any of the traditional limitations of wireless sensor networks: processing time, storage capacity, and power consumption. The developed algorithm is validated by estimating mode shapes using a network of wireless sensors deployed on the mezzanine balcony of Hill Auditorium located at the University of Michigan. Copyright © 2010 John Wiley & Sons, Ltd.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/78217/1/415_ftp.pd