IoT sensors for modern structural health monitoring. A new frontier

The problem of determining the structural safety level of buildings and civil engineering infrastructures (CEIs) is raising growing concern worldwide. Most of the reinforced concrete constructions have a design life not greater than 100 years, and today it is necessary to face the problem of assessing their level of safety and structural integrity. Such problem is even more pressing when a construction is subjected to extreme environmental conditions. The long-term goal of this study is the realization of wireless low- cost devices, and a data management software, for the structural health monitoring of buildings and CEIs, with remotely controlled sensors embedded in, or installed on, the structural elements, to measure stresses together with accelerations. Once equipped with such system, each construction can become part of the Internet of Things, permitting users and authorities to be alerted in case structural safety is diminished or compromised. A crucial aspect is the unaltered preservation of measurement data over time, which cannot just rely on third parties, and for which it is necessary the exploitation of suitable data-protection technologies. This study have been carried out by experimental testing and validation, both in lab and on site, of the monitoring devices designed and realized. Results show that it is possible to realize low-cost monitoring systems, and related installation techniques, for integration in every new or existing buildings and CEIs

Embedding Principal Component Analysis for Data Reductionin Structural Health Monitoring on Low-Cost IoT Gateways

Principal component analysis (PCA) is a powerful data reductionmethod for Structural Health Monitoring. However, its computa-tional cost and data memory footprint pose a significant challengewhen PCA has to run on limited capability embedded platformsin low-cost IoT gateways. This paper presents a memory-efficientparallel implementation of the streaming History PCA algorithm.On our dataset, it achieves 10x compression factor and 59x memoryreduction with less than 0.15 dB degradation in the reconstructedsignal-to-noise ratio (RSNR) compared to standard PCA. More-over, the algorithm benefits from parallelization on multiple cores,achieving a maximum speedup of 4.8x on Samsung ARTIK 710

Innovative IoT Solutions and Wearable Sensing Systems for Monitoring Human Biophysical Parameters: A Review

none3noDigital and information technologies are heavily pervading several aspects of human activities, improving our life quality. Health systems are undergoing a real technological revolution, radically changing how medical services are provided, thanks to the wide employment of the Internet of Things (IoT) platforms supporting advanced monitoring services and intelligent inferring systems. This paper reports, at first, a comprehensive overview of innovative sensing systems for monitoring biophysical and psychophysical parameters, all suitable for integration with wearable or portable accessories. Wearable devices represent a headstone on which the IoT-based healthcare platforms are based, providing capillary and real-time monitoring of patient’s conditions. Besides, a survey of modern architectures and supported services by IoT platforms for health monitoring is presented, providing useful insights for developing future healthcare systems. All considered architectures employ wearable devices to gather patient parameters and share them with a cloud platform where they are processed to provide real-time feedback. The reported discussion highlights the structural differences between the discussed frameworks, from the point of view of network configuration, data management strategy, feedback modality, etc.Article Number: 1660openRoberto De Fazio; Massimo De Vittorio; Paolo ViscontiDE FAZIO, Roberto; DE VITTORIO, Massimo; Visconti, Paol

A novel low-cost inclinometer sensor based on fusion technology for structural health monitoring applications

The fundamental purpose of structural health monitoring (SHM) is to examine the accuracy of the structural health state and predict its future strength. Lately, researchers have been paying close attention to the structural damage detection process employing inclinometers. However, this technique can only be used with unique structures with a sizable Structural Health Monitoring (SHM) budget due to the high cost of inclinometers. Therefore, the use of low-cost sensors by implementing various techniques to improve their accuracy compared to high-cost precision sensors has attracted much attention for structural assessment. This paper introduces a novel, low-cost inclinometer that measures inclination by fusion technology combining gyroscopes and accelerometers. The microcontroller technology used in this gadget is an open-source Internet of Things (IoT) based platform, allowing for wireless data streaming and free commercial software for data collecting. Not only are the coding and placement issues of these sensors thoroughly explained, but detailed answers to the problems mentioned above are also provided, as well as an efficient way to assemble and prepare the sensors.Postprint (published version

A Toolchain Architecture for Condition Monitoring Using the Eclipse Arrowhead Framework

Condition Monitoring is one of the most critical applications of the Internet of Things (IoT) within the context of Industry 4.0. Current deployments typically present interoperability and management issues, requiring human intervention along the engineering process of the systems; in addition, the fragmentation of the IoT landscape, and the adoption of poor architectural solutions often make it difficult to integrate third-party devices in a seamless way. In this paper, we tackle these issues by proposing a tool-driven architecture that supports heterogeneous sensor management through well-established interoperability solutions for the IoT domain, i.e. the Eclipse Arrowhead framework and the recent Web of Things (WoT) standard released by the W3C working group. We deploy the architecture in a real Structural Health Monitoring (SHM) scenario, which validates each developed tool and demonstrates the increased automation derived from their combined usage

Performance Evaluation of an IoT Sensor Node for Health Monitoring of Artwork and Ancient Wooden Structures

In this paper, an IoT sensor node, based on smart Bluetooth low energy (BLE), for the health monitoring of artworks and large wooden structures is presented. The measurements from sensors on board the node are collected in real-time and sent to a remote gateway. The sensor node allows for the monitoring of environmental parameters, in particular, temperature and humidity, with accurate and robust integrated sensors. The developed node also embeds an accelerometer, which also allows other mechanical quantities (such as tilt) to be derived. This feature can be exploited to perform structural monitoring, exploiting the processing of data history to detect permanent displacements or deformations. The node is triggered by acceleration transients; therefore, it can also generate alarms related to shocks. This feature is crucial, for instance, in the case of transportation. The developed device is low-cost and has very good performance in terms of power consumption and compactness. A reliability assessment showed excellent durability, and experimental tests proved very satisfactory robustness against working condition variations. The presented results confirm that the developed device allows for the realization of pervasive monitoring systems, in the context of the IoT paradigm, with sensor nodes devoted to the monitoring of each artwork present in a museum or in a church

An Advanced Conceptual Diagnostic Healthcare Framework for Diabetes and Cardiovascular Disorders

The data mining along with emerging computing techniques have astonishingly influenced the healthcare industry. Researchers have used different Data Mining and Internet of Things (IoT) for enrooting a programmed solution for diabetes and heart patients. However, still, more advanced and united solution is needed that can offer a therapeutic opinion to individual diabetic and cardio patients. Therefore, here, a smart data mining and IoT (SMDIoT) based advanced healthcare system for proficient diabetes and cardiovascular diseases have been proposed. The hybridization of data mining and IoT with other emerging computing techniques is supposed to give an effective and economical solution to diabetes and cardio patients. SMDIoT hybridized the ideas of data mining, Internet of Things, chatbots, contextual entity search (CES), bio-sensors, semantic analysis and granular computing (GC). The bio-sensors of the proposed system assist in getting the current and precise status of the concerned patients so that in case of an emergency, the needful medical assistance can be provided. The novelty lies in the hybrid framework and the adequate support of chatbots, granular computing, context entity search and semantic analysis. The practical implementation of this system is very challenging and costly. However, it appears to be more operative and economical solution for diabetes and cardio patients.Comment: 11 PAGE