CityIOT: Air quality in the city supported by an IoT ecosystem

Air pollution is a worldwide problem, which affects millions of people and can have serious consequences for public health, economy and for social issues. Constant emissions of gaseous pollutants, as a result of industrial operations, combustion of fossil fuels and forest fires, affect air quality and contribute to an increase in this type of pollution. Internet of Things (IoT) has emerged as a response to the creation of intelligent monitoring systems that can be used for management and identification of pollution sources, and for health protection. The development of new equipment and communication technologies has allowed the creation of real-time air quality monitoring applications. This dissertation presents an implementation of an IoT system for monitoring outdoor air quality, based on a multisensory system with LoRa communication capabilities. The system incorporates low-cost sensors capable of detecting different air pollutants, as well as atmospheric parameters, such as temperature and relative humidity. To achieve a long-distance transmission of readings with low energy consumption, the use of LoRa technology was adopted. The frequency of sending readings to the network is based on the level of urban traffic in a certain location. Furthermore, a web/mobile application is presented, which allows the user to monitor in real time and to carry out a temporal analysis of the measurements taken by the system.A poluição do ar representa um problema global, que afeta milhões de pessoas, podendo trazer graves consequências para a saúde pública, para a economia e também sociais. A constante emissão de poluentes em estado gasoso, resultante de operações industriais, combustão de combustíveis fósseis e fogos florestais, afetam a qualidade do ar e contribuem para o aumento deste tipo de poluição. A Internet das Coisas (IoT) emergiu como resposta para a criação de sistemas inteligentes de monitorização que podem ser usados para a gestão e identificação de fontes de poluição e para a proteção da saúde pública. O desenvolvimento de novos equipamentos e tecnologias de comunicação, têm vindo a permitir a criação de aplicações de monitorização da qualidade do ar, em tempo real. Esta dissertação apresenta uma implementação de um sistema IoT de monitorização de qualidade do ar exterior, baseado num sistema multisensor com capacidade de comunicação LoRa. O sistema incorpora sensores de baixo custo capazes de detetar diferentes poluentes atmosféricos, como também parâmetros atmosféricos, como temperatura e humidade relativa. De modo a ser obtida uma transmissão de leituras em longa distância e com baixo consumo energético, o uso da tecnologia LoRa foi adotado. A frequência do envio de leituras para a rede, é feita com base no nível de tráfego urbano, numa certa localização. Ainda é apresentada uma aplicação web/móvel, que permite ao utilizador acompanhar em tempo real e proceder a uma análise temporal, das medições efetuadas pelo sistema

Proposal of architecture for IoT solution for monitoring and management of plantations

The world population growth is increasing the demand for food production. Furthermore, the reduction of the workforce in rural areas and the increase in production costs are challenges for food production nowadays. Smart farming is a farm management concept that may use Internet of Things (IoT) to overcome the current challenges of food production This work presents a systematic review of the existing literature on smart farming with IoT. The systematic review reveals an evolution in the way data are processed by IoT solutions in recent years. Traditional approaches mostly used data in a reactive manner. In contrast, recent approaches allowed the use of data to prevent crop problems and to improve the accuracy of crop diagnosis. Based on the finds of the systematic review, this work proposes an architecture of an IoT solution that enables monitoring and management of crops in real time. The proposed architecture allows the usage of big data and machine learning to process the collected data. A prototype is implemented to validate the operation of the proposed architecture and a security risk assessment of the implemented prototype is carried out. The implemented prototype successfully validates the proposed architecture. The architecture presented in this work allows the implementation of IoT solutions in different scenarios of farming, such as indoor and outdoor

Low Power Wide Area Networks (LPWAN): Technology Review And Experimental Study on Mobility Effect

In the past decade, we have witnessed explosive growth in the number of low-power embedded and Internet-connected devices, reinforcing the new paradigm, Internet of Things (IoT). IoT devices like smartphones, home security systems, smart electric meters, garage parking indicators, etc., have penetrated deeply into our daily lives. These IoT devices are increasingly attached and operated in mobile objects like unmanned vehicles, trains, airplanes, etc. The low power wide area network (LPWAN), due to its long-range, low-power and low-cost communication capability, is actively considered by academia and industry as the future wireless communication standard for IoT. However, despite the increasing popularity of mobile IoT, little is known about the suitability of LPWAN for those mobile IoT applications in which nodes have varying degrees of mobility. To fill this knowledge gap, in this thesis:1. We present a thorough review on LPWAN technology focusing on the mobility effect. 2. We conduct an experimental study to evaluate, analyze, and characterize LPWAN in both indoor and outdoor mobile environments.Our experimental results indicate that the performance of LPWAN is surprisingly susceptible to mobility, even to minor human mobility, and the effect of mobility significantly escalates as the distance to the gateway increases. These results call for development of new mobility-aware LPWAN protocols to support mobile IoT

Recent Developments in Smart Healthcare

Medicine is undergoing a sector-wide transformation thanks to the advances in computing and networking technologies. Healthcare is changing from reactive and hospital-centered to preventive and personalized, from disease focused to well-being centered. In essence, the healthcare systems, as well as fundamental medicine research, are becoming smarter. We anticipate significant improvements in areas ranging from molecular genomics and proteomics to decision support for healthcare professionals through big data analytics, to support behavior changes through technology-enabled self-management, and social and motivational support. Furthermore, with smart technologies, healthcare delivery could also be made more efficient, higher quality, and lower cost. In this special issue, we received a total 45 submissions and accepted 19 outstanding papers that roughly span across several interesting topics on smart healthcare, including public health, health information technology (Health IT), and smart medicine

Applications

Volume 3 describes how resource-aware machine learning methods and techniques are used to successfully solve real-world problems. The book provides numerous specific application examples: in health and medicine for risk modelling, diagnosis, and treatment selection for diseases in electronics, steel production and milling for quality control during manufacturing processes in traffic, logistics for smart cities and for mobile communications

Applications

Volume 3 describes how resource-aware machine learning methods and techniques are used to successfully solve real-world problems. The book provides numerous specific application examples: in health and medicine for risk modelling, diagnosis, and treatment selection for diseases in electronics, steel production and milling for quality control during manufacturing processes in traffic, logistics for smart cities and for mobile communications

Low power wide area network, cognitive radio and the internet of things : potentials for integration

The Internet of Things (IoT) is an emerging paradigm that enables many beneficial and prospective application areas, such as smart metering, smart homes, smart industries, and smart city architectures, to name but a few. These application areas typically comprise end nodes and gateways that are often interconnected by low power wide area network (LPWAN) technologies, which provide low power consumption rates to elongate the battery lifetimes of end nodes, low IoT device development/purchasing costs, long transmission range, and increased scalability, albeit at low data rates. However, most LPWAN technologies are often confronted with a number of physical (PHY) layer challenges, including increased interference, spectral inefficiency, and/or low data rates for which cognitive radio (CR), being a predominantly PHY layer solution, suffices as a potential solution. Consequently, in this article, we survey the potentials of integrating CR in LPWAN for IoT-based applications. First, we present and discuss a detailed list of different state-of-the-art LPWAN technologies; we summarize the most recent LPWAN standardization bodies, alliances, and consortia while emphasizing their disposition towards the integration of CR in LPWAN.We then highlight the concept of CR in LPWAN via a PHY-layer front-end model and discuss the benefits of CR-LPWAN for IoT applications. A number of research challenges and future directions are also presented. This article aims to provide a unique and holistic overview of CR in LPWAN with the intention of emphasizing its potential benefits.This work was supported by the Council for Scientific and Industrial Research, Pretoria, South Africa, through the Smart Networks collaboration initiative and Internet of Things (IoT)-Factory Program (funded by the Department of Science and Innovation (DSI), South Africa).http://www.mdpi.com/journal/sensorsam2021Electrical, Electronic and Computer Engineerin

Improving Access and Mental Health for Youth Through Virtual Models of Care

The overall objective of this research is to evaluate the use of a mobile health smartphone application (app) to improve the mental health of youth between the ages of 14–25 years, with symptoms of anxiety/depression. This project includes 115 youth who are accessing outpatient mental health services at one of three hospitals and two community agencies. The youth and care providers are using eHealth technology to enhance care. The technology uses mobile questionnaires to help promote self-assessment and track changes to support the plan of care. The technology also allows secure virtual treatment visits that youth can participate in through mobile devices. This longitudinal study uses participatory action research with mixed methods. The majority of participants identified themselves as Caucasian (66.9%). Expectedly, the demographics revealed that Anxiety Disorders and Mood Disorders were highly prevalent within the sample (71.9% and 67.5% respectively). Findings from the qualitative summary established that both staff and youth found the software and platform beneficial