The Internet of Things (IoT) shall be able to incorporate transparently and seamlessly a large number of different and heterogeneous end systems, while providing open access to selected subsets of data for the development of a plethora of digital services. Building a general architecture for the IoT is hence a very complex task, mainly because of the extremely large variety of devices, link layer technologies, and services that may be involved in such a system. In this paper, we focus specifically to an urban IoT system that, while still being quite a broad category, are characterized by their specific application domain. Urban IoTs, in fact, are designed to support the Smart City vision, which aims at exploiting the most advanced communication technologies to support added-value services for the administration of the city and for the citizens. This paper hence provides a comprehensive survey of the enabling technologies, protocols, and architecture for an urban IoT. Furthermore, the paper will present and discuss the technical solutions and best-practice guidelines adopted in the Padova Smart City project, a proof-of-concept deployment of an IoT island in the city of Padova, Italy, performed in collaboration with the city municipality.

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Internet of Things for Smart Cities

The PASCAL Visual Object Classes Challenge

This introduction begins with a brief history of SHM technology development. Recent research has begun to recognise that a productive approach to the Structural Health Monitoring (SHM) problem is to regard it as one of statistical pattern recognition (SPR); a paradigm addressing the problem in such a way is described in detail herein as it forms the basis for the organisation of this book. In the process of providing the historical overview and summarising the SPR paradigm, the subsequent chapters in this book are cited in an effort to show how they fit into this overview of SHM. In the conclusions are stated a number of technical challenges that the authors believe must be addressed if SHM is to gain wider acceptance.

An introduction to structural health monitoring

Human skin is a remarkable organ. It consists of an integrated, stretchable network of sensors that relay information about tactile and thermal stimuli to the brain, allowing us to maneuver within our environment safely and effectively. Interest in large-area networks of electronic devices inspired by human skin is motivated by the promise of creating autonomous intelligent robots and biomimetic prosthetics, among other applications. The development of electronic networks comprised of flexible, stretchable, and robust devices that are compatible with large-area implementation and integrated with multiple functionalities is a testament to the progress in developing an electronic skin (e-skin) akin to human skin. E-skins are already capable of providing augmented performance over their organic counterpart, both in superior spatial resolution and thermal sensitivity. They could be further improved through the incorporation of additional functionalities (e.g., chemical and biological sensing) and desired properties (e.g., biodegradability and self-powering). Continued rapid progress in this area is promising for the development of a fully integrated e-skin in the near future.

25th Anniversary Article: The Evolution of Electronic Skin (E-Skin): A Brief History, Design Considerations, and Recent Progress

A number of image processing techniques IPTs have been implemented for detecting civil infrastructure defects to partially replace human-conducted onsite inspections. These IPTs are primarily used to manipulate images to extract defect features, such as cracks in concrete and steel surfaces. However, the extensively varying real-world situations e.g., lighting and shadow changes can lead to challenges to the wide adoption of IPTs. To overcome these challenges, this article proposes a vision-based method using a deep architecture of convolutional neural networks CNNs for detecting concrete cracks without calculating the defect features. As CNNs are capable of learning image features automatically, the proposed method works without the conjugation of IPTs for extracting features. The designed CNN is trained on 40 K images of 256 × 256 pixel resolutions and, consequently, records with about 98% accuracy. The trained CNN is combined with a sliding window technique to scan any image size larger than 256 × 256 pixel resolutions. The robustness and adaptability of the proposed approach are tested on 55 images of 5,888 × 3,584 pixel resolutions taken from a different structure which is not used for training and validation processes under various conditions e.g., strong light spot, shadows, and very thin cracks. Comparative studies are conducted to examine the performance of the proposed CNN using traditional Canny and Sobel edge detection methods. The results show that the proposed method shows quite better performances and can indeed find concrete cracks in realistic situations.

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Deep Learning-Based Crack Damage Detection Using Convolutional Neural Networks

In recent years, there has been an increasing interest in the adoption of emerging sensing technologies for instrumentation within a variety of structural systems. Wireless sensors and sensor networks are emerging as sensing paradigms that the structural engineering field has begun to consider as substitutes for traditional tethered monitoring systems. A benefit of wireless structural monitoring systems is that they are inexpensive to install because extensive wiring is no longer required between sensors and the data acquisition system. Researchers are discovering that wireless sensors are an exciting technology that should not be viewed as simply a substitute for traditional tethered monitoring systems. Rather, wireless sensors can play greater roles in the processing of structural response data; this feature can be utilized to screen data for signs of structural damage. Also, wireless sensors have limitations that require novel system architectures and modes of operation. This paper is intended to serve as a summary review of the collective experience the structural engineering community has gained from the use of wireless sensors and sensor networks for monitoring structural performance and health.

http://www-personal.umich.edu/~jerlynch/papers/SVDReview.pdf

A summary review of wireless sensors and sensor networks for structural health monitoring

Wireless monitoring has emerged in recent years as a promising technology that could greatly impact the field of structural monitoring and infrastructure asset management. This paper is a summary of research efforts that have resulted in the design of numerous wireless sensing unit prototypes explicitly intended for implementation in civil structures. Wireless sensing units integrate wireless communications and mobile computing with sensors to deliver a relatively inexpensive sensor platform. A key design feature of wireless sensing units is the collocation of computational power and sensors; the tight integration of computing with a wireless sensing unit provides sensors with the opportunity to self-interrogate measurement data. In particular, there is strong interest in using wireless sensing units to build structural health monitoring systems that interrogate structural data for signs of damage. After the hardware and the software designs of wireless sensing units are completed, the Alamosa Canyon Bridge in New Mexico is utilized to validate their accuracy and reliability. To improve the ability of low-cost wireless sensing units to detect the onset of structural damage, the wireless sensing unit paradigm is extended to include the capability to command actuators and active sensors.

An overview of wireless structural health monitoring for civil structures

Since the discovery of carbon nanotubes, researchers have been fascinated by their mechanical and electrical properties, as well as their versatility for a wide array of applications. In this study, a carbon nanotube–polyelectrolyte composite multilayer thin film fabricated by a layer-by-layer (LbL) method is proposed to develop a multifunctional material for measuring strain and corrosion processes. LbL fabrication of carbon nanotube composites yields mechanically strong thin films in which multiple sensing transduction mechanisms can be encoded. For example, judicious selection of carbon nanotube concentrations and polyelectrolyte matrices can yield thin films that exhibit changes in their electrical properties to strain and pH. In this study, experimental results suggest a consistent trend between carbon nanotube concentrations and strain sensor sensitivity. Furthermore, by simply altering the type of polyelectrolyte used, pH sensors of high sensitivity can be developed to potentially monitor environmental factors suggesting corrosion of metallic structural materials (e.g. steel, aluminum). (Some figures in this article are in colour only in the electronic version)

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Multifunctional layer-by-layer carbon nanotube-polyelectrolyte thin films for strain and corrosion sensing

As researchers continue to explore wireless sensors for use in structural monitoring systems, validation of field performance must be done using actual civil structures. In this study, a network of low-cost wireless sensors was installed in the Geumdang Bridge, Korea to monitor the bridge response to truck loading. Such installations allow researchers to quantify the accuracy and robustness of wireless monitoring systems within the complex environment encountered in the field. In total, 14 wireless sensors were installed in the concrete box girder span of the Geumdang Bridge to record acceleration responses to forced vibrations introduced by a calibrated truck. In order to enhance the resolution of the capacitive accelerometers interfaced to the wireless sensors, a signal conditioning circuit that amplifies and filters low-level accelerometer outputs is proposed. The performance of the complete wireless monitoring system is compared to a commercial tethered monitoring system that was installed in parallel. The performance of the wireless monitoring system is shown to be comparable to that of the tethered counterpart. Computational resources (e.g. microcontrollers) coupled with each wireless sensor allow the sensor to estimate modal parameters of the bridge such as modal frequencies and operational displacement shapes. This form of distributed processing of measurement data by a network of wireless sensors represents a new data management paradigm associated with wireless structural monitoring. (Some figures in this article are in colour only in the electronic version)

http://wang.ce.gatech.edu/sites/default/files/docs/Performance%20monitoring%20of%20the%20Geumdang%20Bridge%20using%20a%20dense%20network%20of%20high-resolution%20wireless%20sensors.pdf

Performance monitoring of the Geumdang Bridge using a dense network of high-resolution wireless sensors

Impact damage, excessive loading, and corrosion have been identified as critical and long-term problems that constantly threaten the integrity and reliability of structural systems (e.g., civil infrastructures, aircrafts, and naval vessels). While a variety of sensing transducers have been proposed for structural health monitoring, most sensors only offer measurement of structural behavior at discrete structural locations. Here, a conformable carbon nanotube-polyelectrolyte sensing skin fabricated via the layer-by-layer technique is proposed to monitor strain and impact damage over spatial areas. Specifically, electrical impedance tomographical (EIT) conductivity mapping techniques are employed to offer two-dimensional damage maps from which damage location and severity can be easily and accurately quantified. This study deposits carbon nanotube-based sensing skins upon metallic structural plates with electrodes installed along the plate boundary. Based on boundary electrical measurements, EIT mapping captures both strain in the underlying substrate as well as damage (e.g., permanent deformation and cracking) introduced using an impact apparatus.

/pdf/carbon-nanotube-sensing-skins-for-spatial-strain-and-impact-2z3g39i0io.pdf

Jerome P. Lynch

Papers

A summary review of wireless sensors and sensor networks for structural health monitoring

An overview of wireless structural health monitoring for civil structures

Multifunctional layer-by-layer carbon nanotube-polyelectrolyte thin films for strain and corrosion sensing

Performance monitoring of the Geumdang Bridge using a dense network of high-resolution wireless sensors

Carbon nanotube sensing skins for spatial strain and impact damage identification