Monitoring the hydraulic stability of Antifer blocks : an IoT-based approach

Breakwaters are resilient marine infrastructures, or barriers, built out into the sea to protect a coast or a harbour from the force of waves. The environmental conditions that these structures continuously face are challenging and the continuous monitorization of its hydraulic stability is a key success factor for preventive maintenance of these critical infrastructures. This paper introduces the architecture of an IoT solution designed to monitor the hydraulic stability of Antifer blocks, a common building block used for breakwater infrastructures construction, by measuring, recording, processing, and communicating the data related to the displacement of an Antifer block, in a laboratory context. The IoT device has been designed to meet the following requirements: 3D displacement measurement (up to 25 mm); corrosion-proof and waterproof; wireless charging and wireless communication; and autonomy above one month. Preliminary results have shown that the SmartAntifer prototype fulfills the core application requirements and presents an average consumption of 90 mA, which results in 11 hours of autonomy when equipped with a battery with a capacity of 1000 mAh.5311-8814-F0ED | Sara Maria da Cruz Maia de Oliveira PaivaN/

Crowd quantification with flow direction estimation : a low-cost IoT-enabled solution

Monitoring crowds in public environments is of great value for understanding human routines and managing crowd routes in indoor or outdoor environments. This type of information is crucial to improve the business strategy of an organization, and can be achieved by performing crowd quantification and flow direction estimation to generate information that can be later used by a business intelligence/analytic layer to improve sales of a specific service or targeting a new specific product. In this paper, we propose the design of an IoT Crowd sensor composed of an array of ultrasonic ping sensors that is responsible for detecting movement in specific directions. The proposed device has a built-in algorithm that is optimized to quantify and detect the human flow direction in indoor spaces such as hallways. Results have shown an average accuracy above 86% in the five scenarios evaluated when using an array with three elements.5311-8814-F0ED | Sara Maria da Cruz Maia de Oliveira PaivaN/

GNSS-free outdoor localization techniques for resource-constrained IoT architectures : a literature review

Large-scale deployments of the Internet of Things (IoT) are adopted for performance improvement and cost reduction in several application domains. The four main IoT application domains covered throughout this article are smart cities, smart transportation, smart healthcare, and smart manufacturing. To increase IoT applicability, data generated by the IoT devices need to be time-stamped and spatially contextualized. LPWANs have become an attractive solution for outdoor localization and received significant attention from the research community due to low-power, low-cost, and long-range communication. In addition, its signals can be used for communication and localization simultaneously. There are different proposed localization methods to obtain the IoT relative location. Each category of these proposed methods has pros and cons that make them useful for specific IoT systems. Nevertheless, there are some limitations in proposed localization methods that need to be eliminated to meet the IoT ecosystem needs completely. This has motivated this work and provided the following contributions: (1) definition of the main requirements and limitations of outdoor localization techniques for the IoT ecosystem, (2) description of the most relevant GNSS-free outdoor localization methods with a focus on LPWAN technologies, (3) survey the most relevant methods used within the IoT ecosystem for improving GNSS-free localization accuracy, and (4) discussion covering the open challenges and future directions within the field. Some of the important open issues that have different requirements in different IoT systems include energy consumption, security and privacy, accuracy, and scalability. This paper provides an overview of research works that have been published between 2018 to July 2021 and made available through the Google Scholar database.5311-8814-F0ED | Sara Maria da Cruz Maia de Oliveira PaivaN/

Impacts of indoor radon on health: a comprehensive review on causes, assessment and remediation strategies

Indoor radon exposure is raising concerns due to its impact on health, namely its known relationship with lung cancer. Consequently, there is an urgent need to understand the risk factors associated with radon exposure, and how this can be harmful to the health of exposed populations. This article presents a comprehensive review of studies indicating a correlation between indoor radon exposure and the higher probability of occurrence of health problems in exposed populations. The analyzed studies statistically justify this correlation between exposure to indoor radon and the incidence of lung diseases in regions where concentrations are particularly high. However, some studies also showed that even in situations where indoor radon concentrations are lower, can be found a tendency, albeit smaller, for the occurrence of negative impacts on lung cancer incidence. Lastly, regarding risk remediation, an analysis has been conducted and presented in two core perspectives: (i) focusing on the identification and application of corrective measures in pre-existing buildings, and (ii) focusing on the implementation of preventive measures during the project design and before construction, both focusing on mitigating negative impacts of indoor radon exposure on the health of populations.991B-C3B6-3D4F | Salete SoaresN/

Low-cost traffic sensing system based on LoRaWAN for urban areas

The advent of Low Power Wide Area Networks (LPWAN) has enabled the feasibility of wireless sensor networks for environmental traffic sensing across urban areas. In this study, we explore the usage of LoRaWAN end nodes as traffic sensing sensors to offer a practical traffic management solution. The monitored Received Signal Strength Indicator (RSSI) factor is reported and used in the gateways to assess the traffic of the environment. Our technique utilizes LoRaWAN as a long-range communication technology to provide a large-scale system. In this work, we present a method of using LoRaWAN devices to estimate traffic flows. LoRaWAN end devices then transmit their packets to different gateways. Their RSSI will be affected by the number of cars present on the roadway. We used SVM and clustering methods to classify the approximate number of cars present. This paper details our experiences with the design and real implementation of this system across an area that stretches for miles in urban scenarios. We continuously measured and reported RSSI at different gateways for weeks. Results have shown that if a LoRaWAN end node is placed in an optimal position, up to 96% of correct environment traffic level detection can be obtained. Additionally, we share the lessons learned from such a deployment for traffic sensing.5311-8814-F0ED | Sara Maria da Cruz Maia de Oliveira PaivaN/

Wearables and Internet of Things (IoT) Technologies for Fitness Assessment: A Systematic Review

Wearable and Internet of Things (IoT) technologies in sports open a new era in athlete?s training, not only for performance monitoring and evaluation but also for fitness assessment. These technologies rely on sensor systems that collect, process and transmit relevant data, such as biomark ers and/or other performance indicators that are crucial to evaluate the evolution of the athlete?s condition, and therefore potentiate their performance. This work aims to identify and summarize recent studies that have used wearables and IoT technologies and discuss its applicability for fitness assessment. A systematic review of electronic databases (WOS, CCC, DIIDW, KJD, MEDLINE, RSCI, SCIELO, IEEEXplore, PubMed, SPORTDiscus, Cochrane and Web of Science) was undertaken according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. From the 280 studies initially identified, 20 were fully examined in terms of hardware and software and their applicability for fitness assessment. Results have shown that wearable and IoT technologies have been used in sports not only for fitness assessment but also for monitoring the athlete?s internal and external workloads, employing physiological status monitoring and activity recognition and tracking techniques. However, the maturity level of such technologies is still low, particularly with the need for the acquisition of more?and more effective?biomarkers regarding the athlete?s internal workload, which limits its wider adoption by the sports community.4811-99FE-2ECD | Luis Paulo RodriguesN/

A visual analytics approach for effective radon risk perception in the IoT era

Radon gas is one of the most relevant indoor pollutants in areas of slaty and granitic soils, and is considered by the World Health Organization (WHO) as the second-largest risk factor associated with lung cancer. In the IoT era, active radon detectors are becoming affordable and ubiquitous, and in the near future, data gathered by these IoT devices will be streamed and analyzed by cloud-based systems in order to perform the so-called mitigation actions. However, a poor radon risk communication, independently of the technologies and the data analytics adopted, can lead to a misperception of radon risk, and therefore, fail to produce the wanted risk reduction among the population. In this work we propose a visual analytics approach that can be used for effective radon risk perception in the IoT era. The proposed approach takes advantage of specific space-time clustering of time-series data and uses a simple color-based scale for radon risk assessment, specifically designed to aggregate, not only the legislation in force but also the WHO reference level, by means of a visual analytics approach. The proposed methodology is evaluated using real time-series radon data obtained during a long-term period of 7 months.2411-78B2-7CDB | Pedro Miguel MoreiraN/

Improvement of RSSI-Based LoRaWAN localization using Edge-AI

Localization is an essential element of the Internet of Things (IoT) leading to meaningful data and more effective services. Long-Range Wide Area Network (LoRaWAN) is a low-power communications protocol specifically designed for the IoT ecosystem. In this protocol, the RF signals used to communicate between IoT end devices and a LoRaWAN gateway (GW) can be used for communication and localization simultaneously, using distinct approaches, such as Received Signal Strength Indicator (RSSI) or Time Difference of Arrival (TDoA). Typically, in a LoRaWAN network, different GWs are deployed in a wide area at distinct locations, contributing to different error sources as they experience a specific network geometry and particular environmental effects. Therefore, to improve the location estimation accuracy, the weather effect on each GW can be learned and evaluated separately to improve RSSI-based distance and location estimation. This work proposes an RSSI-based LoRaWAN location estimation method based on Edge-AI techniques, namely an Artificial Neural Network (ANN) that will be running at each GW to learn and reduce weather effects on estimated distance. Results have shown that the proposed method can effectively improve the RSSIbased distance estimation accuracy between 6% and 49%, and therefore reduce the impact of the environmental changes in different GWs. This leads to a location estimation improvement of approximately 101 m.5311-8814-F0ED | Sara Maria da Cruz Maia de Oliveira PaivaN/