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

Energy Efficiency in Communications and Networks

The topic of "Energy Efficiency in Communications and Networks" attracts growing attention due to economical and environmental reasons. The amount of power consumed by information and communication technologies (ICT) is rapidly increasing, as well as the energy bill of service providers. According to a number of studies, ICT alone is responsible for a percentage which varies from 2% to 10% of the world power consumption. Thus, driving rising cost and sustainability concerns about the energy footprint of the IT infrastructure. Energy-efficiency is an aspect that until recently was only considered for battery driven devices. Today we see energy-efficiency becoming a pervasive issue that will need to be considered in all technology areas from device technology to systems management. This book is seeking to provide a compilation of novel research contributions on hardware design, architectures, protocols and algorithms that will improve the energy efficiency of communication devices and networks and lead to a more energy proportional technology infrastructure

The design and evaluation of Wireless Sensor Networks for applications in industrial locations

In manufacturing industries, there exist many applications where Wireless Sensor Networks (WSN\u27s) are integrated to provide wireless solution for the automated manufacturing processes. It is well known that industrial environments characterized by extreme conditions such as high temperature, pressure, and electromagnetic (EM) interference that can affect the performance of the WSN\u27s. The key solution to overcome this performance issue is by monitoring the received Signal Strength Index (RSSI) at the received sensor of the WSN device and track frame error rate of wireless packets. ZigBee is a wireless sensor network (WSN) standard designed for specific needs of the remote monitoring sensor system. Zigbee networks can be established by three different topologies: start, hybrid, and mesh. In this research project, the interest in analyzing the characteristics of the Zigbee performance was completed using a star topology network. Three performance parameters were obtained: the RSSI signal to monitor the received wireless packets from the sending node, path-lost exponent to determine the effect of industrial environment on wireless signals, and the frame error rate to know the discontinue time. The study was in three phases and took place in two settings: The first was at the manufacturing laboratory at the University of Northern Iowa, the second and the third were at the facility of a Midwestern manufacturing company. The study aimed to provide an analytical tool to evaluate the performances of Zigbee networks in industrial environments and compare the results to show that harsh environments do affect its performance. The study also involved testing the performance of WSN. This was done by simulating input/output Line passing with digital and analog data. Packets were sent from one node and counted at the receiving side to measure the packet error rate of WSN in industrial environment. In conclusion, investigating the WSN\u27s systems performance in industrial environment provides is crucial to identify the effects of the harsh conditions. It is necessary to run similar investigation to prevent the malfunction of the manufacturing applications. Testing a simple WSN in industrial environment can be capable of predicting the performance of the network. It is also recommended to have an embedded approach to WSN applications that can self-monitor its performance

Proposal of a health care network based on big data analytics for PDs

Health care networks for Parkinson's disease (PD) already exist and have been already proposed in the literature, but most of them are not able to analyse the vast volume of data generated from medical examinations and collected and organised in a pre-defined manner. In this work, the authors propose a novel health care network based on big data analytics for PD. The main goal of the proposed architecture is to support clinicians in the objective assessment of the typical PD motor issues and alterations. The proposed health care network has the ability to retrieve a vast volume of acquired heterogeneous data from a Data warehouse and train an ensemble SVM to classify and rate the motor severity of a PD patient. Once the network is trained, it will be able to analyse the data collected during motor examinations of a PD patient and generate a diagnostic report on the basis of the previously acquired knowledge. Such a diagnostic report represents a tool both to monitor the follow up of the disease for each patient and give robust advice about the severity of the disease to clinicians

Towards a cyber physical system for personalised and automatic OSA treatment

Obstructive sleep apnea (OSA) is a breathing disorder that takes place in the course of the sleep and is produced by a complete or a partial obstruction of the upper airway that manifests itself as frequent breathing stops and starts during the sleep. The real-time evaluation of whether or not a patient is undergoing OSA episode is a very important task in medicine in many scenarios, as for example for making instantaneous pressure adjustments that should take place when Automatic Positive Airway Pressure (APAP) devices are used during the treatment of OSA. In this paper the design of a possible Cyber Physical System (CPS) suited to real-time monitoring of OSA is described, and its software architecture and possible hardware sensing components are detailed. It should be emphasized here that this paper does not deal with a full CPS, rather with a software part of it under a set of assumptions on the environment. The paper also reports some preliminary experiments about the cognitive and learning capabilities of the designed CPS involving its use on a publicly available sleep apnea database

Simulation tool implementing centralized and distributed algorithms for tracking acoustic targets

The goal of this document is the implementation of a software tool for the simulation of the acoustic tracking problem over a wireless sensor network working in a centralized or distributed manner. Its Graphical User Interface (GUI) allows the user to configure the parameters associated to the diffusion adaptive algorithms implemented in the simulation tool, in order to offer a visual representation of the behavior of a real sensor network working with those settings. For illustration we ran several simulations, which allowed us to visualize the performance of different network configurations. The results obtained with the implemented simulation tool show it can be very helpful to study the audio target tracking problem and ultimately for the design of sensor networks that can guarantee certain performance criteria. Moreover, we have developed the code for the implementation of a real acoustictracking sensor network working in a centralized manner, using ©Libelium’sWaspmote™ sensor boards as the network nodes and using ©Libelium’s Meshlium-Xtreme™ as central node.Ingeniería de Sistemas Audiovisuale

Occupancy Estimation in Smart Building using Hybrid CO2/Light Wireless Sensor Network

Smart building, which delivers useful services to residents at lowest cost and maximum comfort, has gained increasing attention in recent years. A variety of emerging information technologies have been adopted in modern buildings, such as wireless sensor networks, internet of things, big data analytics, deep machine learning, etc. Most people agree that a smart building should be energy efficient, and consequently, much more affordable to building owners. Building operation accounts for major portion of energy consumption in the United States. HVAC (heating, ventilating, and air conditioning) equipment is a particularly expensive and energy consuming of building operation. As a result, the concept of “demand-driven HVAC control” is currently a growing research topic for smart buildings. In this work, we investigated the issue of building occupancy estimation by using a wireless CO2 sensor network. The concentration level of indoor CO2 is a good indicator of the number of room occupants, while protecting the personal privacy of building residents. Once indoor CO2 level is observed, HVAC equipment is aware of the number of room occupants. HVAC equipment can adjust its operation parameters to fit demands of these occupants. Thus, the desired quality of service is guaranteed with minimum energy dissipation. Excessive running of HVAC fans or pumps will be eliminated to conserve energy. Hence, the energy efficiency of smart building is improved significantly and the building operation becomes more intelligent. The wireless sensor network was selected for this study, because it is tiny, cost effective, non-intrusive, easy to install and flexible to configure. In this work, we integrated CO2 and light senors with a wireless sensor platform from Texas Instruments. Compare with existing occupancy detection methods, our proposed hybrid scheme achieves higher accuracy, while keeping low cost and non-intrusiveness. Experimental results in an office environment show full functionality and validate benefits. This study paves the way for future research, where a wireless CO2 sensor network is connected with HVAC systems to realize fine-grained, energy efficient smart building