Sensor Networks For Health Monitoring Research Articles

Structural health monitoring of asphalt pavements using smart sensor networks: A comprehensive review

Abstract Early, effective and continuous monitoring allows to reduce costs and to extend life of road infrastructure. For this reason, over the years, more and more efforts have been made to implement more advanced and effective monitoring systems at ever more contained costs, going from impractical manual and destructive methods through automated in vehicle equipment to the most recent wireless sensor network (WSN) embedded into the pavement. The purpose of this paper is to provide a comprehensive, up-to-date critical literature review of wireless sensor networks for pavement health monitoring, considering, also, the experience gained for wired sensor as fundamental point of reference. This work presents both the methodology used to collect and analyse the current bibliography and provides a description and comments fundamental characteristics of wireless sensor networks for pavement monitoring for damage detection purposes, among which energy supply, the detection method, the hardware and network architecture and the performance validation procedures. A brief analysis of other possible complementary applications of smart sensor networks, such as traffic and surface condition monitoring, is provided. Finally, a comment is provided on the gaps and possible directions that future research could follow to allow the extensive use of wireless sensor networks for pavement health condition monitoring.

Energy-Neutral and QoS-Aware Protocol in Wireless Sensor Networks for Health Monitoring of Hoisting Systems

Hoisting equipment is core to many industrial systems and, therefore, their state of health significantly affects production lines and personnel safety; this is especially important in environments such as coal mines. The health of the hoisting system can be estimated by deploying energy harvesting wireless sensor nodes that monitor the drum surface stress. In this network of sensor devices, it is very costly to send highly sampled data as it causes radio congestion and consumes energy. However, from our experience of sensing hoist systems, we note that the data observed at the upper surface of the hoist are significantly more indicative of the state of health of the whole system, compared with data sensed at the lower surface. Therefore, we need to take advantage of this to optimize the communications of sensor nodes. However, scarce energy can be collected for these devices from the hoist itself, along with the prioritized Quality of Service (QoS) requirements (throughput, delay) of monitoring signals, raises important challenges for energy management. In this article, we use Lyapunov optimization techniques and propose an energy-neutral and QoS-aware protocol (EQP), including duty cycling and network scheduling to solve it. Extensive simulations show that the EQP helps sensor nodes realize consecutive monitoring, and achieve more than 38% utility gain compared with existing strategies.

Comparative Study of IoT-Based Topology Maintenance Protocol in a Wireless Sensor Network for Structural Health Monitoring

A structural health monitoring (SHM) system is an approach for identifying the damages caused to various kinds of structures using different system functions and providing the necessary feedback about structure’s conditions. As civil structures are the backbone of our society, to determine its daily operations is a very important issue. The performance measurement of those structures is manual whereas a computer-based monitoring system could automatically assess the structural damages and identify its exact location. Recently, wireless sensor networks (WSNs) have attracted a great deal of attention for remote sensing applications due to flexibility to measure of various activity of large scale network. Since technology is advancing day by day, the overall cost of a monitoring system is also decreased. However, the major challenging fact of a WSNs is to provide scalability for covering a large area. The main question is arisen how much capable have of a monitoring system to turn off unnecessary nodes to save energy while there are no events detected. To support the scalability required of an existing network and save the node energy for future use, we propose a topology maintenance protocol integrated with construction to address the issue of a node’s energy consumption by placing it optimally and extending the monitoring system’s lifetime. As per the authors’ acknowledgement that, a little attention has been paid to developing such a hybrid approach. To mitigate node energy consumption issue with large scale support, an Internet of Things (IoT)-based maintenance approach is the best candidate for obtaining better system lifetime responses. Therefore, the main goal of this work is to develop an ‘on-the-fly’-based topology maintenance monitoring system, which can maintain a network’s infrastructure while gathering a node’s information to switch its state regularly when the present network is no longer optimal.

Wireless Sensor Networks for Continuous Structural Health Monitoring of Historic Masonry Towers

ABSTRACT The recent developments of micro-electro-mechanical systems and wireless sensor networks allow today the use of low-cost and small-size sensors for continuous monitoring of civil structures. Both these features are very important for the low impact of the sensor grid in heritage structures, ensuring a low-cost and sustainable dynamic monitoring system. Over the last 20 years the use of sensor networks for continuous monitoring has received a growing interest. Anyway, still numerous questions remain opened about the sensitivity of measurement devices, the optimization of number and positioning of sensors, the energy efficiency of the network, and the development of algorithms for real-time data analysis. This paper, based on the aforementioned motivations, discusses about a monitoring system made of micro-electro-mechanical sensors connected through a wireless network. The architecture of the wireless sensor network and the automatized procedure proposed for the continuous processing of the recorded signals are discussed and described with reference to an explicative masonry tower case study. It is believed that the proposed technologies can provide an economical and relatively non-invasive tool for real-time structural monitoring and that, moreover, the availability of large amounts of data from actual measurements can give effective information on the structural behaviour of historic constructions.

Sensor Networks for Structural Health Monitoring

Sensor Networks for Structural Health Monitoring

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.

An ultra-thin printable nanocomposite sensor network for structural health monitoring

A nanocomposite-based sensor ink made from carbon black and polyvinyl pyrrolidone was developed for fabricating a new breed of sensor by an inkjet printing approach, to accommodate the general purposes of structural health monitoring. This ink can be directly deposited onto the surface of various substrates or engineering structures such as polyimide film via computer-aided design to configure nanocomposite sensor arrays or dense sensor networks. Strong structure adaptability and high flexibility make this sensor a promising candidate to alternate traditional piezoresistive and piezoelectric sensors, in signal acquisition of dynamic disturbance on complex engineering structures. Lightweight and without the need to use wires or cables, the printed sensor network significantly reduces the weight and volume penalty imposed on the host structures, even when the network is deployed at a large scale. It also minimizes the possibility of exfoliation of the sensors from the host structure under cyclic load. The printed pattern distinguishes superior performance in the perception of acousto-ultrasonic signals from static up to 500 kHz, with high signal-to-noise ratio, sensitivity, and fidelity. By virtue of the tunneling current between two adjacent nanoparticles that are in close proximity (within several nanometers), the printed sensor network is capable of perceiving ultrasonic waves. The fabrication process of the sensor network does not entail any specially made printing facilities, and the carbon black/polyvinyl pyrrolidone hybrid can easily be injected into an inkjet cartridge for printing. Several confirmatory experiments and a proof-of-concept test were carried out based on the printed sensor network to validate the capability of the printed sensor for structural health monitoring.

Implementation of wireless MEMS sensor network for detection of gait events

MEMS based wireless sensor network (WSN) for real-time human health monitoring is promising in home-based rehabilitation. It requires effective integration of a number of MEMS sensors and their placement on the human body to create a wireless body area network for continuous and timely monitoring of various biophysical parameters. This study attempts to develop an XBee-based WSN for real-time monitoring of human gait. Traditionally, the optical motion systems were used for gait analysis but they suffer from certain limitations such as the development of the complex algorithm and constrains in the work space. In comparison to the optical system, the attractive benefits of MEMS-based sensor systems are small size and low cost. Magnetic field angular rate and gravity sensor system can perform a complete analysis of the human gait. The sensor modules were developed using the inertial sensors mainly accelerometer and gyro sensor. LabVIEW software is used for data acquisition from the body sensor nodes and gait analysis. Biometrics Lab System is used as the standard system for calibration of data obtained from sensors. The joint angle range of motion was calculated using both the systems. The advantage of the proposed system is that it facilitates wireless transmission of gait parameters (joint angle measurement) for easy monitoring of human gait in various rehabilitation programmes.

A review of wireless sensor networks for structural health monitoring: offshore wind turbines deployment

Offshore wind turbine is being the trend because the consistency wind resource on open seas. But the harsh environmental condition could lead to damage, corrosion, and erosion. With these challenges, structural health monitoring (SHM) plays an important role to ensure the functionality of offshore turbines, and wireless sensor networks (WSNs) help to reduce costs compared to the wire-based ones. This paper presents a literature review for two of main parameters to deploy WSNs for SHM; data rate and bandwidth which indicate the efficiency of communication for each nodes.

Secure and intelligent architecture for cloud-based healthcare applications in wireless body sensor networks

Wireless Sensor Networks (WSNs) are becoming a significant enabling technology for a wide variety of applications. Advances in WSN have facilitated the realisation of pervasive health monitoring for both homecare and hospital environments. Sensor nodes are capable of sensing, processing, and communicating one or more vital signs, and they can be used in Wireless Body Sensor Networks (WBSNs) for health monitoring. Many studies were performed and are on-going in order to develop flexible, reliable, secure, real-time, and power-efficient WBSNs suitable for healthcare applications. This paper concentrates on the development of intelligent secure architecture for cloud based healthcare applications in wireless sensor networks. This paper describes the applications, issues and challenges of BSN in healthcare, energy efficient body sensor network architecture using aggregation, secure data collection, storage and data sharing using an authentication algorithm and a novel prediction model which act as an expert system for disease management.

Fiber Grating-Based Strain Sensor Array for Health Monitoring of Pipelines

Pipelines are one of the most important modern energy transportation methods, used especially for the transportation of certain dangerous energy media materials such as crude oil, natural gas, and chemical raw materials. New requirements have been put forward for the health monitoring and early security warning of pipelines because of the large-scale and complicated development trend of the pipe network system. To achieve an accurate assessment of the health conditions of pipeline infrastructure, obtaining as many precise operating parameters as possible, particularly at some critical parts of the pipeline, is necessary. Therefore, a novel type of fiber grating strain sensor array is proposed herein to monitor the pipeline hoop strain. The sensor utilizes fiber grating characteristics such as light weight, corrosion resistance, remote transmission, and strong environmental adaptability. The fiber containing the grating measurement points is implanted into the composite material to complete the sensitization encapsulation and protection of the bare fiber grating. The design of the sensor array fulfills the requirements for monitoring pipeline mass data, making it easy to form a pipeline health monitoring sensor network. The sensor sensitivity is researched by using a combination of theoretical and experimental analysis. A sensitivity test, as well as linearity and stability tests, are performed on the sensor. The experimental results show that the average sensitivity of the sensor is 14.86 pm/με, and the error from the theoretical calculation analysis value is 8.75%. Due to its high reliability, good linear response and long-term stability, and the ability to reflect the exact strain change of the outer wall of the pipeline, the designed sensor can support longterm online pipeline monitoring. The fiber grating sensor array network has successfully realized the monitoring of the pipeline’s internal operation by using external strain changes. In addition to the performance benefits, there are other merits associated with the applicability of the sensor namely simple structure, compact size, manufacturing ease, and exterior installation ease.

Damage identification in aircraft structures with self‐powered sensing technology: A machine learning approach

Progress in self-powered wireless sensor networks for structural health monitoring (SHM) have motivated the development of power-efficient data communication protocols. One such approach is the energy-aware pulse switching architecture, which employs ultrasonic pulses to transmit binary data through the material substrate. However, this technology creates time delays on the generated data due to the power budgets demanded for sensing and communication. The nature of data collected from such protocol thus requires the development of new analysis and interpretation methods. This study presents a robust damage identification strategy for aircraft structures, within the context of data-driven SHM, using discrete time-delayed binary data. A novel machine learning framework integrating low-rank matrix completion, pattern recognition, k-nearest neighbor, and a data fusion model was developed for damage identification. Performance and accuracy of the proposed data-driven SHM strategy was investigated and tested for an aircraft horizontal stabilizer wing. Damage states were simulated on a finite element model by reducing stiffness in a region of the stabilizer's skin. The reliability of the proposed strategy with noise-polluted data was also validated. Further, the effect of variations in harvested energy on the performance of the approach was investigated. Results demonstrate that the developed machine learning framework can effectively detect the presence and location of damage based on time-delayed binary data from a self-powered sensor network.

Cost⁻Benefit Optimization of Structural Health Monitoring Sensor Networks.

Structural health monitoring (SHM) allows the acquisition of information on the structural integrity of any mechanical system by processing data, measured through a set of sensors, in order to estimate relevant mechanical parameters and indicators of performance. Herein we present a method to perform the cost–benefit optimization of a sensor network by defining the density, type, and positioning of the sensors to be deployed. The effectiveness (benefit) of an SHM system may be quantified by means of information theory, namely through the expected Shannon information gain provided by the measured data, which allows the inherent uncertainties of the experimental process (i.e., those associated with the prediction error and the parameters to be estimated) to be accounted for. In order to evaluate the computationally expensive Monte Carlo estimator of the objective function, a framework comprising surrogate models (polynomial chaos expansion), model order reduction methods (principal component analysis), and stochastic optimization methods is introduced. Two optimization strategies are proposed: the maximization of the information provided by the measured data, given the technological, identifiability, and budgetary constraints; and the maximization of the information–cost ratio. The application of the framework to a large-scale structural problem, the Pirelli tower in Milan, is presented, and the two comprehensive optimization methods are compared.

Performance analysis of a wireless sensor network with cognitive radio capabilities in structural health monitoring applications: A discrete model

This article studies the performance of a wireless sensor network with cognitive radio capabilities to gather information about structural health monitoring of buildings in case of seismic activity. Since the use of the local area network is intensive in office and home environments, we propose the use of empty cellular channels (primary system). As such, the structural health monitoring does not degrade the local communications. Thus, the wireless sensor network for structural health monitoring acts as secondary network. Two discrete-time analytical approaches are proposed and developed to evaluate the system performance in terms of both the average packet delay and average energy consumption. The first one is an approximation suitable for the case when the time slot duration is small relative to the mean call inter-arrival time. The second model is accurate for any time slot duration and inter-arrival times.

Structural Health Monitoring Sensor Network Optimization through Bayesian Experimental Design

Structural health monitoring (SHM) may be exploited to estimate the mechanical properties of existing structures and check for potential damage. Among commonly used methodologies for property characterization, the Bayesian approach holds the lead because it is endowed with the particular advantage of quantifying associated uncertainties. These uncertainties arise owing to diverse factors including (1) sensor accuracy and positioning, (2) environmental influences, and (3) modeling errors. In minimizing the influence of sensor-related uncertainties, an optimal design may be adopted for the SHM campaign to maximize the information content of the measurements. Here, a procedure based on Bayesian experimental design is proposed to quantify the expected utility of the sensor network. The positions of the used sensors are selected in a way that maximizes the Shannon information gain between the prior and posterior probability distributions of the parameters to be estimated. In order to numerically solve the resulting optimization problem, surrogate models based on polynomial chaos expansion (PCE) and stochastic optimization methods are used. The use of surrogates allows one to reduce the computational cost of the associated model runs. The method is applied to a large-scale example, namely the Pirelli Tower in Milan.

An Experimental Study on Static and Dynamic Strain Sensitivity of Embeddable Smart Concrete Sensors Doped with Carbon Nanotubes for SHM of Large Structures.

The availability of new self-sensing cement-based strain sensors allows the development of dense sensor networks for Structural Health Monitoring (SHM) of reinforced concrete structures. These sensors are fabricated by doping cement-matrix mterials with conductive fillers, such as Multi Walled Carbon Nanotubes (MWCNTs), and can be embedded into structural elements made of reinforced concrete prior to casting. The strain sensing principle is based on the multifunctional composites outputting a measurable change in their electrical properties when subjected to a deformation. Previous work by the authors was devoted to material fabrication, modeling and applications in SHM. In this paper, we investigate the behavior of several sensors fabricated with and without aggregates and with different MWCNT contents. The strain sensitivity of the sensors, in terms of fractional change in electrical resistivity for unit strain, as well as their linearity are investigated through experimental testing under both quasi-static and sine-sweep dynamic uni-axial compressive loadings. Moreover, the responses of the sensors when subjected to destructive compressive tests are evaluated. Overall, the presented results contribute to improving the scientific knowledge on the behavior of smart concrete sensors and to furthering their understanding for SHM applications.

Study on a Cylindrical Sensor Network for Intelligent Health Monitoring and Prognosis

In this paper, intelligent body healthcare management based on body-centric wireless communications (BCWCs) is considered, and a wearable sensor network is studied. The interaction between electromagnetic waves and the human body is studied at 5 GHz. With the development of BCWCs, numerous on-body sensors need to be interconnected and transmitting information about human body healthcare. Structures of uniform cylindrical sensor networks for intelligent health monitoring and prognosis are proposed and studied. The reason for choosing a cylindrical sensor network is that the shape of a human torso can be approximated as a cylinder. Different numbers and arrangements of wearable antennas are considered and compared, and the far-field beam pattern is considered an important indicator in the investigation. Finally, a sparse cylindrical antenna array is also proposed, and good directional beam pattern performance is obtained. Electromagnetic simulation is performed mainly by the finite-difference time-domain method. This paper provides guidelines for the arrangement of wearable sensor networks.

An energy-efficient communication system using joint beamforming in multi-hop health monitoring sensor networks

This paper presents an energy-efficient wireless ad hoc communication system and baseband transceiver for sensor networks that gather vital sign information of patients. The design of the proposed system is aimed to facilitate an energy-efficient and low-latency vital sign data transmission service. Based on this aim, the proposed system adopts multi-hop transmission and joint transmitter-receiver beamforming technique to efficiently transmit data at significantly reduced transmission power. Furthermore, to shorten the transmission latency and reduce channel contention, we proposed a novel Medium Access Control (MAC) mechanism which includes neighborhood discovery, packet collision detection and prioritized transmission scheme based on an Orthogonal Frequency Division Multiple Access (OFDMA) physical layer. Simulation of transmission power, transmission latency, and data rate, together with proposed hardware architecture, were all carried out to verify the effectiveness and feasibility of the proposed system.

Model driven flexible design of a wireless body sensor network for health monitoring

The Wireless Body Sensor Network (WBSN) is a wireless network that is designed to allow communication among sensor nodes that are attached to a human body to monitor the body’s vital parameters and environment. The design and development of such WBSN systems for health monitoring have received a large amount of attention recently, in research studies and in industry. This attention is mainly motivated by costly health care and by recent advances in the development of miniature health monitoring devices as well as emerging technologies, such as the Internet of Things (IoT), which contribute to the main challenges of 5G. The existence of an explicit approach to address the required software design and verification should be very beneficial for the construction and maintenance of such systems. This paper presents a preventive health care system that has a flexible design. The proposed system is based on an architecture that has heterogeneous nodes and provides both daily continuous monitoring as well as specific controls. We defined a model to describe the WBSN’s global behavior. An important aspect of this work is that we propose a model-driven engineering (MDE) approach to address the derivation of each node’s behavior in the WBSN from the WBSN global behavior. This approach allows developers to obtain a system design from the global specification of its requirement. To ensure the conformance of this design to its specification, the derived behaviors should be validated and verified before their deployment. In fact, formal methods are powerful tools for software engineers to verify the logical correctness of concurrent software at different levels of its life cycle. Model checking is one of the most powerful formal methods for verifying the logical correctness of such concurrent systems. In this work, we make use of a model checking approach that is based on a model transformation to validate the automatically derived behavior of a WBSN for health monitoring. This model-driven approach will check whether the derived system behaves correctly according to its global specification, while the objective is to increase the system’s performance and QoS. This approach allows the developer to reason about a model of the global system rather than about the system itself.

Maximizing Lifetime in Wireless Sensor Network for Structural Health Monitoring With and Without Energy Harvesting

This paper presents an optimization framework to maximize the lifetime of wireless sensor networks for structural health monitoring with and without energy harvesting. We develop a mathematical model and formulate the problem as a large-scale mixed integer non-linear programming problem. We also propose a solution based on the Branch-and-Bound algorithm augmented with reducing the search space. The proposed strategy builds up the optimal route from each source to the sink node by providing the best set of hops in each route and the optimal power allocation of each sensor node. To reduce the computational complexity, we propose heuristic routing algorithms. In this heuristic algorithm, the power levels are selected from the optimal predefined values, the problem is formulated by an integer non-linear programming, and the Branch-and-Bound reduced space algorithm is used to solve the problem. Moreover, we propose two sub-optimal algorithms to reduce the computation complexity. In the first algorithm, after selecting the optimal transmission power levels from a predefined value, a genetic algorithm is used to solve the integer non-linear problem. In the second sub-optimal algorithm, we solve the problem by decoupling the optimal power allocation scheme from the optimal route selection. Therefore, the problem is formulated by an integer non-linear programming, which is solved using the Branch-and-Bound space-reduced method with reduced binary variables (i.e., reduced complexity), and after the optimum route selection, the optimal power is allocated for each node. The numerical results reveal that the presented algorithm can prolong the network lifetime significantly compared with the existing schemes. Moreover, we mathematically formulate the adaptive energy harvesting period to increase the network lifetime with the possibility to approach infinity. Finally, the minimum harvesting period to have infinite lifetime is obtained.