Damage detection on a full-scale highway sign structure with a distributed wireless sensor network

Produced sand in oil pipelines poses a serious problem in many production situations, since a small amount of sand in the produced fluid can result in significant erosion in a very short time stage. Reliable sand detection is an important component of oil production systems; it would be valuable for assisting with optimizing well productivity and detecting sand as early as possible. In this work, a new reliable distributed sand-monitoring framework using a Wireless Sensor Network (WSN) is proposed. The framework combines three modules: Data Acquisition (DA) module, Wireless Receiving and Transmission (WRT) module, and Distributed Data Fusion module (DDF). The WRT module implementation is based on TinyOS and Crossbow MICAz mote. The DDF module is based on a resource-aware Distributed Kalman Filter (DKF). The data is collected from pipeline using acoustic sensor (SENACO AS100) and Flow Analyzer (MC-II) in real time. The experimental results validate the proposed framework. Sand production is considered as one of the major problems facing the petroleum industry. In the oil and gas industry, the sand production causes considerable erosion damage in the well tubing, piping, fittings, separators, valves, and other equipment. It can cause poor performance in injection wells, and can lead to lost production. It arises in the case of failure of sand control measures. Sand screening is also a critical part of the mining process (1). Every year, cleaning and repairing operations related to sand production and restricted production rates cost the industry millions of dollars. However, a reliable sand monitor system is essential to decide whether sand control measures need to be installed during well operations or not. Sand monitoring allows timely actions to be taken to handle sand production such as: (a) Increasing inspection to detect erosion; (b) Reducing the flow rate or stopping the production in some of the extreme cases; (c) Installation of sand handling systems (2). The sand problem is not new and affects the entire oil industry. In some cases several tones of sand can come from a reservoir in one day. Deciding when it is necessary to deal with sand is a difficult decision. Several fields have installed a sand detection system. Installation of a system to monitor and measure sand production would be helpful to detect sand as early as possible. Early sand detection would then lead to possible remedial action that could avoid the erosion and improve the system production. However, to be able to do this, we need repeatability, sensitivity, and also real time measurements (3). Oil and gas companies started to integrate their legacy sensors with Wireless Sensor Networks (WSNs) technology to help them to increase production, streamline operations, and reduce expenses. We introduced some solutions toward this direction. In (4), a reliable prototype of a remote measuring of flow rate using a MC-II Flow Analyzer was introduced. In (5), we developed a Wireless Sensor Data Acquisition (WSDA) prototyping. It composed of both legacy and modern sensors. Some other solutions introduced centralized and distributed fusion algorithms for sand detection using WSN (6-9). These fusion algorithms include Fuzzy Art (FA), Maximum Likelihood Estimator (MLE), Moving Average Filter (MAF), and Distributed Kalman Filter (DKF). The DKF showed promising results over the other algorithms. Unlike the other algorithms, Kalman filter does not lend itself for easy implementation in WSN; this is because WSN has limited computation capability, small memory, and limited energy. DKF involves seventeen matrix operations: ten matrix multiplications, two matrix inversions, four matrix additions, and one matrix subtraction. These operations are computationally intensive and could strain the energy resources of any single computational node in WSN. In order to share the information gathered by all the sensors, we encounter fundamental problems: communication congestion and scalability. The scalability issue and communication congestion are closely related in DKF algorithm. The more sensors we add to our system, the more communication we will require. Moreover, the scalability problem is not only related to communication issues, but also to computation problems. Higher dimensional measurement vectors mean higher computational demands. In this paper, a new distributed sand-monitoring framework using WSN is proposed. In order to meet with constraints of WSN, a resource-aware DKF is proposed. The proposed DKF is based on fast polynomial to increase the convergence rate. Fast convergence contributes to energy saving because it saves time. In addition, we reduce the communication traffic by exchanging the estimates between the nodes instead of exchanging the estimates and its variance. Each node uses its measurement, its associated variance, and its neighbors' estimates. This leads not only to significant energy saving, but also reduces the memory usage and avoids congestion. Moreover, we redesigned the DKF to reduce the number of arithmetic operations. Thus, the proposed optimizations make the DKF applicable to be implemented in WSN. The experimental results validate the proposed framework.

Impact of time synchronization error on the mode-shape identification and damage detection/localization in WSNs for structural health monitoring

This pager surveys the use of wireless sensors and sensor networks in monitoring Dam safety. Engineers paid much attention to fucus on the research of Dam safety monitoring (DSM), Since it could increase the safety and reduce the maintenance costs of engineering structures. Recently, wireless sensor network (WSN) has been explored for adoption to improve the centralized cable-based DSM system performances. This paper presents a multi-agent design method and system evaluation for wireless sensor network based Dam safety monitoring to validate the efficiency of the multi-agent technology. Through the cooperation of four different agents for DSM applications, the distributed wireless sensor network can automatically allocate DSHM tasks; self-organize the sensor network and aggregate different sensor information.

Structural health monitoring and damage detection can help people avoid emergency casualties and take preventive measures, and it is very important in practical application. After consulting authoritative information, the structural health often affected by the surrounding environmental safety factors and structural safety factors. This paper begins with the harm of the inadequate building safety and the factors affecting building safety. To detect structural health problems and avoid many kinds of disasters, three monitoring methods have been mentioned, two new building safety detection methods and a damage detection scheme, called wireless sensor network subsystem, global navigation satellite system technology, and ultrasonic flaw detection technology. The principles of three methods are mainly expounded, and their advantages and disadvantages are analyzed, by putting forward suggestions for future research prospects and giving some practical examples. Finally, it is concluded that wireless sensor network method is simple to achieve but the monitoring data is single; GNSS system has high precision but cannot be widely used; And ultrasonic flaw detection technology can detect steel damage single efficiency.

Nowadays, the efficiency of multiagent systems in wireless sensor networks prompts the researchers to use these emerging mobile software packets in different simulated approaches or real-world applications. Heterogeneous and distributed wireless sensor networks could be integrated with the multiagent systems to map the real-world challenges into the artificial intelligence world. The multiagent systems have been applied from simulated approaches like object detection/tracking, healthcare, control/assistant, and security systems to real-world applications, including medical/human-care systems and unmanned aerial vehicles. Furthermore, the integration of wireless sensor networks with multiagent systems have emerged novel application, which is known as mobile robots. However, the extensive use of mobile agents in wireless sensor networks has posed different challenges for researchers, including security, resource, and timing limitation. In this work, we review recent simulated approaches and real-world applications of multiagent systems in wireless sensor networks, in which a set of common factors about the things that have been studied are extracted and compared to analyze the performance of mobile agent–based systems in the wireless sensor networks, as well. This analysis provides new research directions about multiagent systems in wireless sensor networks for interested researchers. Finally, a novel framework for dealing with the challenges of multiagent-based applications in the wireless sensor networks which have been mentioned is suggested.

Wireless Sensor Networks (WSNs) has grown exponentially in last few years and WSN based applications, including Home automation, healthcare, the environment, and the military are being used on a large scale. Despite of their powerful capabilities, as sensor nodes are resource constraint devices, implementation of WSNs is a challenging task. Lots of research has been already done to minimize energy, sensor nodes consumed in performing miscellaneous tasks, to improve the life time of the WSNs. LEACH protocol is most common bases for designing advanced routing protocols for improved power efficient WSNs. In this paper, a comprehensive review of existing power efficacy improved routing protocols has been done. Performance of these routing protocols has been also compared in term of various WSN characteristics. This paper also highlights key technological differences between most recent routing protocols. The review may be considered as a base for future research to design more routing protocols, for WSN applications, with better energy management techniques.

A wireless sensor is a small, low-cost, low-powered device with sensing, wireless communication, and computation capabilities. While the tiny size and low cost of sensors make them lucrative for use in various applications and services, the constraints associated with those also curb exploitation of computation intensive tasks. This in fact comes as the major concern while thinking about using public key cryptography (PKC) in wireless sensor network (WSN). Within the last few years, we have seen a steep growth of research works addressing various aspects of security in wireless sensor networks. While most of the works suggest using symmetric key cryptography, a good number of works also present their success stories of using PKC in WSN. In this chapter, we present a survey on these noteworthy implementations done so far in this area. Alongside presenting the survey of the significant implementations, we also give an overview of PKC, discuss why and how it is needed for WSN, and the major hurdles to its implementation in WSN.

The wireless sensor network (WSN), is a type of selforganized distribution system without complex infrastructure requirement. However, WSN?s independence and un-attended usage limit its power supply and life expectancy. These become a critical issue for any WSN implementation. The in-network processing which, intends to minify data volume locally can greatly reduce the energy consumption of data delivery over a long distance to the sink. However, open problems are still remain with in-network processing, such as how to carry out in-network processing, and how to combine routing scheme to the sink (e.g. the long distance delivery). For any WSN application, the following preassumption is vital. There exists a physical signal field which bridges physical events to the sensors of WSN, otherwise WSN does not work. In fact, the physical signal field can be depicted by sensory data of all the nodes, and can even be used for data local convergence (e.g. innetwork processing). An immediate solution which we propose is that, sensory data of the nodes can be converged to local extremes of the physical signal field along its gradient direction. Furthermore, the physical signal field is integrated with the cost field routing scheme [2, 3]. The cost field routing is in charge of delivering data to the sink. During the in-network processing, the delivery direction is derived by combining linearly the gradient directions of the cost field and physical signal field. Also simulation results show that the proposed schemes are robust, adaptable, and reliable to the variation of physical events.

Drive‐by structural health monitoring (SHM) is a cost‐efficient alternative to the direct SHM of short‐ to medium‐size bridges requiring no sensors to be installed on the structure. However, drive‐by SHM is generally known as a short‐term monitoring technique due to the challenges associated with using multiple passages of instrumented vehicles for a long time. This paper proposes combining the potentiality of connected and autonomous vehicles (CAVs) into drive‐by damage detection by introducing In‐Fleet SHM. To the authors’ knowledge, this is the first study that proposes using CAVs for SHM application in civil engineering structures. Each In‐Fleet CAV could automatically collect the vehicle's persistent and temporal data by the embedded sensors and transmit them to edge computing systems for analysis. These persistent data include type and model and temporal parameters encompassing position, speed, heading, and vertical acceleration of CAVs. Knowing the persistent and temporal data of the passing vehicles over the transportation infrastructures enables the identification of the dynamic parameters of the bridge from the vehicles’ vertical acceleration response using drive‐by techniques and, on the other hand, reconstruction of the finite element model of the passing vehicles over the supporting bridges in a near real‐time manner. In contrast to the drive‐by SHM, In‐Fleet monitoring has an expanded spatial and temporal coverage, enabling continuous near real‐time monitoring of highway bridges of the transportation network. The accuracy and resolution of the identified modal components in In‐Fleet SHM are enhanced due to the crowdsensing nature of the collected data. Furthermore, by offering a unique set of characteristics, this method fills the crucial gap in implementing Industry 4.0 technologies and digital twins for SHM of bridges.

Structural health monitoring (SHM) has been adopted as a technique to monitor the structure performance to detect damage in aging infrastructure. The ultimate goals of implementing an SHM system are to improve infrastructure maintenance, increase public safety, and minimize the economic impact of an extreme loading event by streamlining repair and retrofit measures. With the recent advances in wireless communication technology, wireless SHM systems have emerged as a promising alternative solution for rapid, accurate and low-cost structural monitoring. This article presents an enabling, developing damage algorithm to advance the detection and diagnosis of damage to structures for SHM using networks of wireless smart sensors. Networks of wireless smart sensors are being used as a vibration based structural monitoring network that allows extraction of mode shapes from output-only vibration data from an underground structure. The mode shape information can further be used in modal methods of damage detection. These sensors are being used to experimentally verify analytical models of post-earthquake evaluation based on system identification analysis. Damage measurement system could play a significant role in monitoring/recording with a higher level of completeness the actual seismic response of structures and in non-destructive seismic damage assessment techniques based on dynamic signature analysis.

Target localization based on energy considerations in distributed sensor networks

Sensitive data collected by a wireless sensor network (WSN) should be protected by a secret key shared between two adjacent (or neighboring) sensors. Sensors are mobile devices with limited memory and computational power, and sensors are deployed in a field randomly in which relative positions among sensors cannot be pre‐determined. Key distribution in WSN is a challenging research problem. Random key distribution is one of the most popular key distribution techniques in WSN; but it is a probabilistic key distribution scheme since two neighboring sensors may or may not share any pairwise key. There are many published papers to improve the probability of connectivity of two neighboring sensors. In this paper, a novel design of lightweight key establishment is proposed based on top of random key distribution. The first scheme can increase the probability of connectivity of two neighboring sensors, and the second scheme can also enable sensors to authenticate the established pairwise key. Both schemes need only XOR operation so they are lightweight and especially suitable for WSNs.

Unsupervised learning methods are effective and suitable tools for damage detection. The main reason for the popularity of these methods in structural health monitoring originates from the fact that the process of learning can be implemented by information of the only normal condition called training data. In contrast, supervised learning methods require information of both normal and current conditions for the process of interest. Because civil engineering structures are expensive and complex, it is not reasonable and economical to impose intentional damage on providing training data. Hence, it is not simple to directly exploit supervised learning techniques in structural health monitoring. To deal with this limitation, this article proposes a novel two-level strategy including three algorithms for using the concepts of both unsupervised learning and supervised learning. The major contribution of this strategy is to consider supervised learning as a validation tool for damage detection. First, the results of damage detection are obtained from two unsupervised learning methods developed by Mahalanobis squared distance and a deep autoencoder neural network in the first two algorithms of the proposed strategy. The main objective is to separate accurate and confusing results of damage detection based on Type I and Type II errors. Second, the confusing results are fed into the third algorithm to train a classifier and compute their classification margins for making the final decision and validating damage detection. The effectiveness and applicability of the proposed strategy are assessed by a numerical concrete beam and an experimental laboratory frame. Results show that this strategy with the aid of the Naive Bayes classifier enables the unsupervised learning methods to make accurate decisions.

The distributed wireless sensor network (WSN) technologies have experienced rapid developments in recent years owing to mobility, scalability, and flexibility of solutions with different kinds of consensus achieved using the Kalman filter (KF). In this article, we employ a robust linear technique known as unbiased finite impulse response (UFIR) filtering and develop it for distributed WSNs with optimal consensus on estimates. A batch of distributed UFIR (dUFIR) filter is designed to operate on individual and average data horizons and then represented with an iterative algorithm using recursions. Based on simulations of a circularly traveling and rapidly maneuvering object covered by a WSN with 50 nodes, the dUFIR filter is shown to have higher robustness against the distributed KF (dKF). An experimental verification is conducted for a known ground truth of a moving vehicle covered by a WSN with eight nodes. The trade-off between the dUFIR filter, dKF, and dH <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sub> filter is investigated in detail under different operation scenarios and tuning modes.

Comparison of WSN and IoT approaches for a real-time monitoring system of meal distribution trolleys: A case study