A Novel RFID-Based Strain Sensor for Wireless Structural Health Monitoring

Conventional tools for deformation measurement are widely used for structural health monitoring—i.e. strain gauges and fiber optic sensors—due to their relative low cost and efficiency. However, these techniques still present some drawbacks particularly in limited access locations, since their usage requires some sort of cabling or battery-driven electronics. Thus, in a few situations there may be room for new technologies for deformation monitoring of structures. One of the options are passive radio frequency identification (RFID)-based sensors. In synthesis, like conventional methods, these sensors are attached to the surface a test-piece, but are then remotely interrogated and powered by a transmitter. The solidarity of the sensor to the underlying material means that the detected signal is modified in a way that can be correlated to sample strain in a given direction. This paper encompasses the project of a wireless deformation sensor based on an inverted-F antenna focusing on its miniaturization and performance optimization. For this purpose, such an antenna was designed with the help of finite element modelling tools for operation on a 2.0 mm-thick aluminium sheet, and then experimentally validated through static loading tests. Finally, even though sensors of this kind are still in early developments, results show that RFID sensors represents a promising method for remote deformation assessment in components.     

Wireless Acoustic Emission Sensor Network for Structural Monitoring

The paper presents a prototype wireless system for the detection of active fatigue cracks in aging railways bridges in real-time. The system is based on a small low-cost sensor node, called an AEPod, that has four acoustic emission (AE) channels and a strain channel for sensing, as well as the capability to communicate in a wireless fashion with other nodes and a base station. AEPods are placed at fracture-critical bridge locations. The strain sensor detects oncoming traffic and triggers the AEPod out of its hibernation mode. As the train stresses the fracture-critical member, acoustic emission and strain data are acquired. The data are compressed and filtered at the AEPod and transmitted off the bridge using cell-phone communication.     

Powering Mobile Networks with Optimal Green Energy for Sustainable Development

Green wireless networking is an emerging area for many societies, especially academia and industry, in light of economic and ecological perspectives. Empowering wireless infrastructures exploiting green power sources can enhance sustainability due to the adverse effects of conventional power sources and atmospheric circumstances. Moreover, the specific power supply requirements for a base station (BS), such as cost effectiveness, efficiency, sustainability, and reliability, can be met by utilizing technological advances in renewable energy. Numerous drivers and motivators are involved in the deployment of renewable energy technologies and the transition toward green energy. Renewable energy is free, clean, and abundant in most locations throughout the year. In this work, a sustainable optimal stand-alone solar-powered model envisioning green cellular BSs for urban locations in Oman is proposed. This model can extend 24 h uninterrupted power supply support to a cellular BS that fully utilizes an integrated storage device. The system analysis is conducted using a hybrid optimization model for electric renewables (HOMER) based on actual prevailing conditions of the regions and their technical feasibility. The results showed can be achieved operational expenditure savings up to 16%. These outcomes provide a huge benefit to the cellular operators of Oman economically, technically, and ecologically.     

A Wireless Sensor Network for Cold-Chain Monitoring

This paper deals with a wireless sensor network that was specifically designed to monitor temperature-sensitive products during their distribution with the aim of conforming to the cold-chain assurance requirements. The measurement problems and the constraints that have been encountered in this application are initially highlighted, and then, an architecture that takes such problems into account is proposed. The proposed architecture is based on specifically designed measuring nodes that are inserted into the products to identify their behavior under real operating conditions, e.g., during a typical distribution. Such product nodes communicate through a wireless channel with a base station, which collects and processes the data sent by all the nodes. A peculiarity of the product nodes is the low cost, which allows the information on the cold-chain integrity to be provided to the final customer. The results that refer to the functional tests of the proposed system and to the experimental tests performed on a refrigerated vehicle during a distribution are reported.     

Piezoelectric Glass-Fiber-Reinforced Polymer Sensor for Structural Health Monitoring and Stiffness Sensing

Strengthening and repairing infrastructure with fiber-reinforced polymers while achieving low-cost, real-time monitoring of cracks in engineering structures is highly challenging. Herein, a piezoelectric glass-fiber-reinforced polymer (piezo-GFRP) sensor is used to monitor the mechanical behavior of cement beams with cracks under cyclic bending. The output voltage and frequency of the sensor are shown to be effective feedback signals for determining the mechanical behavior. The thin piezo-GFRP sensor shows sufficient piezoelectric sensitivity to monitor the slight variations in the mechanical behavior of the cement beams during the early stages of cracking. It is indicated in this result that such sensors have the potential for the multifunctional structural health monitoring of engineering structures.     

浅述无线传感器网络技术

无线传感器网络能够通过节点上的传感器对被监测量进行感知、采集和实时监测,并将这些信息传递给监测中心,具有部署快、无人值守、功耗低、性价比高等优点,十分适用于环境监测。     

Hybrid Sensor Selection Technique for Lifetime Extension of Wireless Sensor Networks

Energy conservation is a crucial issue to extend the lifetime of wireless sensor networks (WSNs) where the battery capacity and energy sources are very restricted. Intelligent energy-saving techniques can help designers overcome this issue by reducing the number of selected sensors that report environmental measurements by eliminating all replicated and unrelated features. This paper suggests a Hybrid Sensor Selection (HSS) technique that combines filter-wrapper method to acquire a rich-informational subset of sensors in a reasonable time. HSS aims to increase the lifetime of WSNs by using the optimal number of sensors. At the same time, HSS maintains the desired level of accuracy and manages sensor failures with the most suitable number of sensors without compromising the accuracy. The evaluation of the HSS technique has adopted four experiments by using four different datasets. These experiments show that HSS can extend the WSNs lifetime and increase the accuracy using a sufficient number of sensors without affecting the WSN functionality. Furthermore, to ensure HSS credibility and reliability, the proposed HSS technique has been compared to other corresponding methodologies and shows its superiority in energy conservation at premium accuracy measures.

     

Illumimote: Multimodal and High-Fidelity Light Sensor Module for Wireless Sensor Networks

We describe the system requirements, design, system integration, and performance evaluation of the Illumimote, a new light-sensing module for wireless sensor networks. The Illumimote supports three different light-sensing modalities: incident light intensity, color intensities, and incident light angle (the angle of ray arrival from the strongest source); and two situational sensing modalities: attitude and temperature. The Illumimote achieves high performance, comparable to commercial light meters, while conforming to the size and energy constraints imposed by its application in wireless sensor networks. We evaluated the performance of our Illumimote for light intensity, color temperature, and incident light angle measurements and verified the function of the attitude sensor. The Illumimote consumes about 90 mW when all features on board are activated. We describe our design and the experiment design for the performance evaluation.     

Validation of HDOP Measure for Impact Detection in Sensor Network-Based Structural Health Monitoring

This paper studies the validity of horizontal dilution of precision (HDOP) measure to evaluate sensor network geometries when localizing impacts in structural health monitoring (SHM). First, HDOP is defined similarly to navigation applications. Even though several low-complexity closed-form solutions have been proposed recently, HDOP measure has been theoretically justified only for iterative least-squares approach. The localization errors of popular impact localization methods are experimentally collected and compared with HDOP data for validation. The experimental setup is also described. It is shown that HDOP, in general, correlates with the positioning error and can be used to characterize geometry.      

A Ring-Shaped Photodiode Designed for Use in a Reflectance Pulse Oximetry Sensor in Wireless Health Monitoring Applications

We report a photodiode for use in a reflectance pulse oximeter for use in autonomous and low-power homecare applications. The novelty of the reflectance pulse oximeter is a large ring shaped backside silicon pn photodiode. The ring-shaped photodiode gives optimal gathering of light and thereby enable very low light-emitting diode (LED) driving currents for the pulse oximeter. The photodiode also have a two layer SiO₂/SiN interference filter yielding 98% transmission at the measuring wavelengths, 660 nm and 940 nm, and suppressing other wavelengths down to 50% transmission. The photodiode has a radius of 3.68 mm and a width of 0.78 mm giving an area of 18 mm². The capacitance of the photodiode is measured to 34.5 nF. The quantum efficiency of the photodiode is measured to 55% and 62% at 660 nm and 940 nm, respectively. It is acceptable for this prototype but can be improved. The sensor also has an on-chip integrated Au thermistor for measuring the skin temperature of the body. The thermistor has a Temperature Coefficient of Resistance of 2.7·10^-3 K^-1 and a repeatability on temperature measurements of ±0.26°C. The photodiode is fabricated in a clean room environment by two diffusion processes and an Advanced Silicon Etch to make the hole in the middle for the LEDs. The sensor is designed to be integrated in a sticking patch of hydrocolloid polymer together with integrated electronics, radio communication unit, and a coin cell battery. The reflectance pulse oximetry sensor is demonstrated to work in a laboratory setup with a Ledtronics dual LED with wavelengths of 660 and 940 nm. Using this setup photoplethysmograms which clearly show the cardiovascular cycle have been recorded. The sensor is shown to work very well with low currents of less than 10 mA.     

Experimental Characterization of Wireless Sensor Networks for Industrial Applications

The effects of interference in the setup of wireless sensor networks (WSNs) represent a critical issue, and as such, it needs to be carefully addressed. To this aim, helpful information can be achieved through measurements to be carried out in advance on suitable prototypes and testbeds. In this paper, the measurement of industrial WSN performance is dealt with. In particular, a suitable testbed enlisting IEEE 802.15.4 wireless sensor nodes is presented along with the results of some experiments carried out even in the presence of interference. The purpose is to show how to evaluate some specific parameters of a WSN employed for industrial applications to obtain useful information for its setup optimization in the presence of interference. The analysis will show that from the measurement of these parameters (number of failed pollings, polling round-trip time, experimental cycle time, and alarm latency), interference effects can effectively be recognized, and the network setup can be optimized.      

Feature Extraction and Sensor Fusion for Ultrasonic Structural Health Monitoring Under Changing Environmental Conditions

Strongly reverberating diffuse-like ultrasonic waves can interrogate large areas of complex structures that do not support more easily interpreted guided waves. However, sensitivity to environmental changes such as temperature and surface wetting can degrade the performance of a structural health monitoring system using these types of waves. Surface wetting is investigated here with a simplified experiment where controlled amounts of water are applied to the surface of a specimen in conjunction with incrementally introduced artificial damage. A feature-based approach is taken whereby differential features between a signal and a baseline are defined that are sensitive to damage but less sensitive to surface wetting, and multiple features obtained from a spatially distributed sensor array are combined via a voting strategy. In addition, the features considered are insensitive to moderate temperature changes, which are unavoidable even in the laboratory. Experimental results show a probability of detection greater than 90% when detecting damage in the presence of modest surface wetting while maintaining a false alarm rate under 5%.     

A Reliable and High-Bandwidth Multihop Wireless Sensor Network for Mine Tunnel Monitoring

Wireless sensor network (WSN) is considered here for remote monitoring of mine tunnels. Since mine transport and exploiting plane monitoring are the complex tasks that require video monitoring, a major challenge of remote mine tunnel monitoring is the multipath and diffraction effect due to unreliable channel and limited capacity. We propose here a braided cooperative reliable transport (BCRT) algorithm for reliable video transmission within mine tunnel. BCRT maintains one-hop reliability to ensure end-to-end reliability so that it is robust to path breaks. In addition, the BCRT-based WSN uses three consecutive forward neighbors to adjust the data rate to improve the sensing reliability. The proposed WSN is implemented in a real-life mine tunnel. And it is demonstrated to be able to enhance the reliability of the end-to-end monitoring of mine tunnels.     

A PSO based Energy Efficient Coverage Control Algorithm for Wireless Sensor Networks

Wireless Sensor Networks (WSNs) are large-scale and high-density networks that typically have coverage area overlap. In addition, a random deployment of sensor nodes cannot fully guarantee coverage of the sensing area, which leads to coverage holes in WSNs. Thus, coverage control plays an important role in WSNs. To alleviate unnecessary energy wastage and improve network performance, we consider both energy efficiency and coverage rate for WSNs. In this paper, we present a novel coverage control algorithm based on Particle Swarm Optimization (PSO). Firstly, the sensor nodes are randomly deployed in a target area and remain static after deployment. Then, the whole network is partitioned into grids, and we calculate each grid’s coverage rate and energy consumption. Finally, each sensor nodes’ sensing radius is adjusted according to the coverage rate and energy consumption of each grid. Simulation results show that our algorithm can effectively improve coverage rate and reduce energy consumption     

A Parallel Simulated Annealing Architecture for Model Updating in Wireless Sensor Networks

In recent years, wireless sensing technologies have provided a much sought-after alternative to expensive cabled monitoring systems. Wireless sensing networks forego the high data transfer rates associated with cabled sensors in exchange for low-cost and low-power communication between a large number of sensing devices, each of which features embedded data processing capabilities. As such, a new paradigm in large-scale data processing has emerged; one where communication bandwidth is somewhat limited but distributed data processing centers are abundant. By taking advantage of this grid of computational resources, data processing tasks once performed independently by a central processing unit can now be parallelized, automated, and carried out within a wireless sensor network. By utilizing the intelligent organization and self-healing properties of many wireless networks, an extremely scalable multiprocessor computational framework can be developed to perform advanced engineering analyses. In this study, a novel parallelization of the simulated annealing stochastic search algorithm is presented and used to update structural models by comparing model predictions to experimental results. The resulting distributed model updating algorithm is validated within a network of wireless sensors by identifying the mass, stiffness, and damping properties of a three-story steel structure subjected to seismic base motion.     

Reputation-Enabled Self-Modification for Target Sensing in Wireless Sensor Networks

Wireless sensor networks provide new tools for sensing physical environments. However, the general existence of faulty sensor measurements in networks will cause degradation of the network service quality and huge burden of the precious energy. While cryptography-based approaches are helpless of information generation, reputation systems are demonstrated of positive results. In this paper, we investigate the benefits of a distributed reputation system in target localization. A node reputation is defined as its measurement performance and is computed by the Dirichlet distribution. By assuming the sensing model of each node to be mixed Gaussian, we use reputation to estimate parameters of the sensing model and modify a node's original measurement. We also develop a reputation-based local voting algorithm to filter the untrustworthy data and then estimate the target location by a particle swarm optimization algorithm. To assure energy efficiency of the proposed approach, we use a reputation-based model to indicate the information importance of each data packet and ensure that a more important packet can be delivered with higher reliability. Finally, we experimentally evaluate the reputation system and demonstrate its accuracy and energy efficiency.      

Enhanced Archimedes Optimization Algorithm for Clustered Wireless Sensor Networks

Wireless sensor networks (WSN) encompass a set of inexpensive and battery powered sensor nodes, commonly employed for data gathering and tracking applications. Optimal energy utilization of the nodes in WSN is essential to capture data effectively and transmit them to destination. The latest developments of energy efficient clustering techniques can be widely applied to accomplish energy efficiency in the network. In this aspect, this paper presents an enhanced Archimedes optimization based cluster head selection (EAOA-CHS) approach for WSN. The goal of the EAOA-CHS method is to optimally choose the CHs from the available nodes in WSN and then organize the nodes into a set of clusters. Besides, the EAOA is derived by the incorporation of the chaotic map and pseudo-random performance. Moreover, the EAOA-CHS technique determines a fitness function involving total energy consumption and lifetime of WSN. The design of EAOA for CH election in the WSN depicts the novelty of work. In order to exhibit the enhanced efficiency of EAOA-CHS technique, a set of simulations are applied on 3 distinct conditions dependent upon the place of base station (BS). The simulation results pointed out the better outcomes of the EAOA-CHS technique over the recent methods under all scenarios.     

Bandwidth-Efficient Geographic Multicast Routing Protocol for Wireless Sensor Networks

We present geographic multicast routing (GMR), a new multicast routing protocol for wireless sensor networks. It is a fully localized algorithm that efficiently delivers multicast data messages to multiple destinations. It does not require any type of flooding throughout the network. Each node propagating a multicast data message needs to select a subset of its neighbors as relay nodes towards destinations. GMR optimizes the cost over progress ratio where the cost is equal to the number of neighbors selected for relaying and the progress is the overall reduction of the remaining distances to destinations. Such neighbor selection achieves a good tradeoff between the bandwidth of the multicast tree and the effectiveness of the data distribution. Our cost-aware neighbor selection is based on a greedy set merging scheme achieving a O(Dnmin(D,n)³) computation time, where n is the number of neighbors of current node and D is the number of destinations. As in traditional geographic routing algorithms, delivery to all destinations is guaranteed by applying face routing when necessary. Our simulation results show that GMR outperforms position based multicast in terms of cost of the trees and computation time over a variety of networking scenarios     

An Asynchronous Clustering and Mobile Data Gathering Schema Based on Timer Mechanism in Wireless Sensor Networks

Recently, Wireless sensor networks (WSNs) have become very popular research topics which are applied to many applications. They provide pervasive computing services and techniques in various potential applications for the Internet of Things (IoT). An Asynchronous Clustering and Mobile Data Gathering based on Timer Mechanism (ACMDGTM) algorithm is proposed which would mitigate the problem of “hot spots” among sensors to enhance the lifetime of networks. The clustering process takes sensors’ location and residual energy into consideration to elect suitable cluster heads. Furthermore, one mobile sink node is employed to access cluster heads in accordance with the data overflow time and moving time from cluster heads to itself. Related experimental results display that the presented method can avoid long distance communicate between sensor nodes. Furthermore, this algorithm reduces energy consumption effectively and improves package delivery rate.