Wireless sensor networks for structural and environmental monitoring

The article discusses various applications of Wireless Sensor Networks (WNS) in environment and civil engineering constructions monitoring with a particular emphasis on the evaluation of the influence of individual node failures on the operation of the whole network.The dynamic development of civilization more and more necessitates the use of applications of integrated measure and control systems enabling constant monitoring and tracking of environmental conditions as well as industrial and civilian constructions in real time. Appropriate measurements of relocations and deformations of geotechnical and hydrotechnical structures have a key significance for ensuring safety precautions and their appropriate use and operation.The article explores possibilities for the application of WNS's in the above fields. WNS networks make it possible to monitor engineering constructions, to keep track of any changes occurring in buildings and structures and their immediate surroundings, to measure the settlement and vertical dislocation of structures or surfaces, or to monitor land-slips of bridges, tunnels or road embankments. Information collected by sensor networks is reported and dispatched progressively from measure points (nodes) providing accurate description of abrupt or gradual changes in the state of a building and the conditions of the surrounding area. However, due to harsh environmental conditions, unattended operation and unique characteristics of WSN's, sensors are subjected to various hazards and risks. It is pointed out in the article that those features that decide on the practicality and applicability of the networks are at the same time instrumental in the vulnerability of the system or that their application involves a risk of purposeful malicious actions aiming at disruption of the whole monitoring system

Wireless sensor networks for structural and environmental monitoring

The article discusses various applications of Wireless Sensor Networks (WNS) in environment and civil engineering constructions monitoring with a particular emphasis on the evaluation of the influence of individual node failures on the operation of the whole network.The dynamic development of civilization more and more necessitates the use of applications of integrated measure and control systems enabling constant monitoring and tracking of environmental conditions as well as industrial and civilian constructions in real time. Appropriate measurements of relocations and deformations of geotechnical and hydrotechnical structures have a key significance for ensuring safety precautions and their appropriate use and operation.The article explores possibilities for the application of WNS's in the above fields. WNS networks make it possible to monitor engineering constructions, to keep track of any changes occurring in buildings and structures and their immediate surroundings, to measure the settlement and vertical dislocation of structures or surfaces, or to monitor land-slips of bridges, tunnels or road embankments. Information collected by sensor networks is reported and dispatched progressively from measure points (nodes) providing accurate description of abrupt or gradual changes in the state of a building and the conditions of the surrounding area. However, due to harsh environmental conditions, unattended operation and unique characteristics of WSN's, sensors are subjected to various hazards and risks. It is pointed out in the article that those features that decide on the practicality and applicability of the networks are at the same time instrumental in the vulnerability of the system or that their application involves a risk of purposeful malicious actions aiming at disruption of the whole monitoring system

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

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

Damage identification in structural health monitoring: a brief review from its implementation to the Use of data-driven applications

The damage identification process provides relevant information about the current state of a structure under inspection, and it can be approached from two different points of view. The first approach uses data-driven algorithms, which are usually associated with the collection of data using sensors. Data are subsequently processed and analyzed. The second approach uses models to analyze information about the structure. In the latter case, the overall performance of the approach is associated with the accuracy of the model and the information that is used to define it. Although both approaches are widely used, data-driven algorithms are preferred in most cases because they afford the ability to analyze data acquired from sensors and to provide a real-time solution for decision making; however, these approaches involve high-performance processors due to the high computational cost. As a contribution to the researchers working with data-driven algorithms and applications, this work presents a brief review of data-driven algorithms for damage identification in structural health-monitoring applications. This review covers damage detection, localization, classification, extension, and prognosis, as well as the development of smart structures. The literature is systematically reviewed according to the natural steps of a structural health-monitoring system. This review also includes information on the types of sensors used as well as on the development of data-driven algorithms for damage identification.Peer ReviewedPostprint (published version

Design of Wireless Sensor Nodes for Structural Health Monitoring applications

Enabling low-cost distributed monitoring, wireless sensor networks represents an interesting solution for the implementation of structural health monitoring systems. This work deals with the design of wireless sensor networks for health monitoring of civil structures, specifically focusing on node design in relation to the requirements of different structural monitoring application classes. Design problems are analysed with specific reference to a large-scale experimental setup (the long-term structural monitoring of the Basilica S. Maria di Collemaggio, L’Aquila, Italy). Main limitations emerged are highlighted, and adopted solution strategies are outlined, both in the case of commercial sensing platform and of full custom solutions

Structural health monitoring of offshore wind turbines: A review through the Statistical Pattern Recognition Paradigm

Offshore Wind has become the most profitable renewable energy source due to the remarkable development it has experienced in Europe over the last decade. In this paper, a review of Structural Health Monitoring Systems (SHMS) for offshore wind turbines (OWT) has been carried out considering the topic as a Statistical Pattern Recognition problem. Therefore, each one of the stages of this paradigm has been reviewed focusing on OWT application. These stages are: Operational Evaluation; Data Acquisition, Normalization and Cleansing; Feature Extraction and Information Condensation; and Statistical Model Development. It is expected that optimizing each stage, SHMS can contribute to the development of efficient Condition-Based Maintenance Strategies. Optimizing this strategy will help reduce labor costs of OWTs׳ inspection, avoid unnecessary maintenance, identify design weaknesses before failure, improve the availability of power production while preventing wind turbines׳ overloading, therefore, maximizing the investments׳ return. In the forthcoming years, a growing interest in SHM technologies for OWT is expected, enhancing the potential of offshore wind farm deployments further offshore. Increasing efficiency in operational management will contribute towards achieving UK׳s 2020 and 2050 targets, through ultimately reducing the Levelised Cost of Energy (LCOE)