IoT based smart waste management system in aspect of COVID-19

The rapid evolution of the IoT has led to various research challenges for improving smart city applications. Owing to the characteristics and virtues of IoT services, waste management has emerged as a prominent issue in today's society. An undiscerning illegal eviction of waste, lack of waste disposal and management systems, and inept waste management policies have resulted in severe health and environmental challenges. Based on an integrative review, the proposed technique provides insight into the potential of smart cities and associated communities in assisting waste management initiatives. This study has referred to the existing waste management issues in urban areas and proposed an IoT-based smart waste management system of India in aspects of COVID-19 afflicted houses. Our system intends to improve waste management by making regular environmental sterility and making COVID situations more convenient. The proposed framework ensures a solution for efficiently handling waste generated in urban areas, focusing on the interaction among concessioners and waste generators to monitor the unfilled level of bins. This proposal offers dynamic waste collection scheduling and route optimization while achieving quality of service.

Machine learning-based IoT system for COVID-19 epidemics

The planet earth has been facing COVID-19 epidemic as a challenge in recent time. It is predictable that the world will be fighting the pandemic by taking precautions steps before an operative vaccine is found. The IoT produces huge data volumes, whether private or public, through the invention of IoT devices in the form of smart devices with an improved rate of IoT data generation. A lot of devices interact with each other in the IoT ecosystem through the cloud or servers. Various techniques have been presented in recent time, using data mining approach have proven help detect possible cases of coronaviruses. Therefore, this study uses machine learning technique (ABC and SVM) to predict COVID-19 for IoT data system. The system used two machine learning techniques which are Artificial Bee Colony algorithm with Support Vector Machine classifier on a San Francisco COVID-19 dataset. The system was evaluated using confusion matrix and had a 95% accuracy, 95% sensitivity, 95% specificity, 97% precision, 96% F1 score, 89% Matthews correlation coefficient for ABC-L-SVM and 97% accuracy, 95% sensitivity, 100% specificity, 100% precision, 97% F1 score, 93.1% Matthews correlation coefficient for ABC-Q-SVM. In conclusion, the system shows that the process of dimensionality reduction utilizing ABC feature extraction techniques can boost the classification production for SVM. It was observed that fetching relevant information from IoT systems before classification is relatively beneficial.

IoT-cloud based healthcare model for COVID-19 detection: an enhanced k-Nearest Neighbour classifier based approach

COVID - 19 affected severely worldwide. The pandemic has caused many causalities in a very short span. The IoT-cloud-based healthcare model requirement is utmost in this situation to provide a better decision in the covid-19 pandemic. In this paper, an attempt has been made to perform predictive analytics regarding the disease using a machine learning classifier. This research proposed an enhanced KNN (k NearestNeighbor) algorithm eKNN, which did not randomly choose the value of k. However, it used a mathematical function of the dataset’s sample size while determining the k value. The enhanced KNN algorithm eKNN has experimented on 7 benchmark COVID-19 datasets of different size, which has been gathered from standard data cloud of different countries (Brazil, Mexico, etc.). It appeared that the enhanced KNN classifier performs significantly better than ordinary KNN. The second research question augmented the enhanced KNN algorithm with feature selection using ACO (Ant Colony Optimization). Results indicated that the enhanced KNN classifier along with the feature selection mechanism performed way better than enhanced KNN without feature selection. This paper involves proposing an improved KNN attempting to find an optimal value of k and studying IoT-cloud-based COVID - 19 detection.

Platform for Hands-On Remote Labs Based on the ESP32 and NOD-red

Not only in Morocco, throughout the walks of the world covid 19 pandemics has seriously questioned policymakers from different sectors. Think-tank in the educational sector notably higher education addressed by such a wide range of challenges brought about by covid 19. The characteristic concern that educationalists in Moroccan universities have to reconsider in this pandemic period should not be beyond rethinking new pedagogical alternatives including approaches, methods, techniques and didactic materials which can successfully assist practioners of the teaching and learning process to keep up with the current alterations. Practical work (PW) is an indispensable type of teaching in scientific and technical training and meets a real complementary need through real, remote or virtual laboratories. Students can consolidate what they have learnt and develop analytical skills by comparing experimental results with those obtained during the manipulation. In this context, the Laboratory of Engineering Sciences and Energy Management (LASIME) at the Superior School of Technology of Agadir has developed a low-cost platform called LABERSIME installed in the cloud (LMS, IDE) and equipped with an embedded system to drive real laboratory equipment and perform experiments qualitatively more efficient than those in face-to-face mode. The ultimate goal is to stimulate self-learning motivation in students through a creative approach.

Vaccine cold chain management and cold storage technology to address the challenges of vaccination programs

The outbreaks of infectious diseases that spread across countries have generally existed for centuries. An example is the occurrence of the COVID-19 pandemic in 2020, which led to the loss of lives and economic depreciation. One of the essential ways of handling the spread of viruses is the discovery and administration of vaccines. However, the major challenges of vaccination programs are associated with the vaccine cold chain management and cold storage facilities. This paper discusses how vaccine cold chain management and cold storage technology can address the challenges of vaccination programs. Specifically, it examines different systems for preserving vaccines in either liquid or frozen form to help ensure that they are not damaged during distribution from manufacturing facilities. Furthermore, A vaccine is likely to provide very low efficacy when it is not properly stored. According to preliminary studies, the inability to store vaccine properly is partly due to the incompetency of many stakeholders, especially in technical matters. The novelty of this study is to thoroughly explore cold storage technology for a faster and more comprehensive vaccine distribution hence it is expected to be one of the reference and inspiration for stakeholders.

Open and Cost-Effective Digital Ecosystem for Lake Water Quality Monitoring

In some sectors of the water resources management, the digital revolution process is slowed by some blocking factors such as costs, lack of digital expertise, resistance to change, etc. In addition, in the era of Big Data, many are the sources of information available in this field, but they are often not fully integrated. The adoption of different proprietary solutions to sense, collect and manage data is one of the main problems that hampers the availability of a fully integrated system. In this context, the aim of the project is to verify if a fully open, cost-effective and replicable digital ecosystem for lake monitoring can fill this gap and help the digitalization process using cloud based technology and an Automatic High-Frequency Monitoring System (AHFM) built using open hardware and software components. Once developed, the system is tested and validated in a real case scenario by integrating the historical databases and by checking the performance of the AHFM system. The solution applied the edge computing paradigm in order to move some computational work from server to the edge and fully exploiting the potential offered by low power consuming devices.

Smart mask – Wearable IoT solution for improved protection and personal health

The use of face masks is an important way to fight the COVID-19 pandemic. In this paper, we envision the Smart Mask, an IoT supported platform and ecosystem aiming to prevent and control the spreading of COVID-19 and other respiratory viruses. The integration of sensing, materials, AI, wireless, IoT, and software will help the gathering of health data and health-related event detection in real time from the user as well as from their environment. In the larger scale, with the help of AI-based analysis for health data it is possible to predict and decrease medical costs with accurate diagnoses and treatment plans, where the comparison of personal data to large-scale public data enables drawing up a personal health trajectory, for example. Key research problems for smart respiratory protective equipment are identified in addition to future research directions. A Smart Mask prototype was developed with accompanying user application, backend and heath AI to study the concept.

Progress of Advanced Devices and Internet of Things Systems as Enabling Technologies for Smart Homes and Health Care

In the Internet of Things (IoT) era, various devices (e.g., sensors, actuators, energy harvesters, etc.) and systems have been developed toward the realization of smart homes/buildings and personal health care. These advanced devices can be categorized into ambient devices and wearable devices based on their usage scenarios, to enable motion tracking, health monitoring, daily care, home automation, fall detection, intelligent interaction, assistance, living convenience, and security in smart homes. With the rapidly increasing number of such advanced devices and IoT systems, achieving fully self-sustained and multimodal intelligent systems is becoming more and more important to realize a sustainable and all-in-one smart home platform. Hence, in this Review, we systematically present the recent progress of the development of advanced materials, fabrication techniques, devices, and systems for enabling smart home and health care applications. First, advanced polymer, fiber, and fabric materials as well as their respective fabrication techniques for large-scale manufacturing are discussed. After that, functional devices classified into ambient devices (at home ambiance such as door, floor, table, chair, bed, toilet, window, wall, etc.) and wearable devices (on body parts such as finger, wrist, arm, throat, face, back, etc.) are presented for diverse monitoring and auxiliary applications. Next, the current developments of self-sustained systems and intelligent systems are reviewed in detail, indicating two promising research directions in this field. Last, conclusions and outlook pinpointed on the existing challenges and opportunities are provided for the research community to consider.

An overview of data reduction solutions at the edge of IoT systems: a systematic mapping of the literature

Internet of Things (IoT) is a technology that connects devices of different types and characteristics through a network. The massive quantity of the heterogeneous generated data by the sensors imposes many challenges in making these data available to IoT applications. Data reduction and preprocessing are promising concepts that help to handle these data efficiently before storing them. Applying data reduction methods at the edge has emerged as an efficient solution. In such context, this systematic mapping is intended to investigate the data reduction solutions performed exclusively at the edge through a set of research questions. To reach this objective, we performed a Systematic Literature Mapping (SLM) in which 35 papers were strictly analyzed among a total of 853 articles. Finally, we present the results of these analyses answering questions that relate to the researcher’s used techniques, hardware technologies, used data type, and contributed objects to perform the data reduction techniques on the edge of the IoT systems.

A Framework for Pandemic Prediction Using Big Data Analytics

IoT (Internet of Things) devices and smart sensors are used in different life sectors, including industry, business, surveillance, healthcare, transportation, communication, and many others. These IoT devices and sensors produce tons of data that might be valued and beneficial for healthcare organizations if it becomes subject to analysis, which brings big data analytics into the picture. Recently, the novel coronavirus pandemic (COVID-19) outbreak is seriously threatening human health, life, production, social interactions, and international relations. In this situation, the IoT and big data technologies have played an essential role in fighting against the pandemic. The applications might include the rapid collection of big data, visualization of pandemic information, breakdown of the epidemic risk, tracking of confirmed cases, tracking of prevention levels, and adequate assessment of COVID-19 prevention and control. In this paper, we demonstrate a health monitoring framework for the analysis and prediction of COVID-19. The framework takes advantage of Big data analytics and IoT. We perform descriptive, diagnostic, predictive, and prescriptive analysis applying big data analytics using a novel disease real data set, focusing on different pandemic symptoms. This work's key contribution is integrating Big Data Analytics and IoT to analyze and predict a novel disease. The neural network-based model is designed to diagnose and predict the pandemic, which can facilitate medical staff. We predict pandemic using neural networks and also compare the results with other machine learning algorithms. The results reveal that the neural network performs comparatively better with an accuracy rate of 99%.

The role of 5G for digital healthcare against COVID-19 pandemic: Opportunities and challenges

COVID-19 pandemic caused a massive impact on healthcare, social life, and economies on a global scale. Apparently, technology has a vital role to enable ubiquitous and accessible digital health services in pandemic conditions as well as against “re-emergence” of COVID-19 disease in a post-pandemic era. Accordingly, 5G systems and 5G-enabled e-health solutions are paramount. This paper highlights methodologies to effectively utilize 5G for e-health use cases and its role to enable relevant digital services. It also provides a comprehensive discussion of the implementation issues, possible remedies and future research directions for 5G to alleviate the health challenges related to COVID-19.

Telesurgery and Robotics: An Improved and Efficient Era

Telesurgery, or remote surgery, is widely known as a master-slave technology. It has achieved a milestone in surgical technology and intervention, providing widespread prospects of operating on a patient in a remote area with increased accuracy and precision. It consists of one or more arms controlled by a surgeon and a master controller in a remote area accessing all the information being transferred via a telecommunication system. This paper reviews the present advancements and their benefits and limitations in the field of telesurgery. A handful of operations have been done so far. However, due to time-lag (latency), global networking problems, legal issues and skepticism, and on top of the cost of robotic systems and their affordability have led to the concept of telerobotics and surgery to lag. However, with more information and high speed, 5G networking, which has been in a trial to reduce latency to its minimum, is beneficial. Haptic feedback technology in telesurgery and robotics is another achievement that can be improved; further, this allows the robotic arms to mimic the natural hand movements of the surgeon in the control center so that the master controller can perform surgeries with more dexterity and acuity. Due to coronavirus (COVID-19), this type of surgery approach can reduce the probability of contracting the virus, saving more lives and the future.

On exploiting Data Visualization and IoT for Increasing Sustainability and Safety in a Smart Campus

In a world that is getting increasingly digital and interconnected, and where more and more physical objects are integrated into the information network (Internet of Things, IoT), Data Visualization can facilitate the understanding of huge volumes of data. In this paper, we present the design and implementation of a testbed where IoT and Data Visualization have been exploited to increase the sustainability and safety of the Cesena (Smart) Campus. In particular, we detail the overall system architecture and the interactive dashboard that facilitates the management of the campus premises and the timetabling. Exploiting our system, we show how we can improve the campus sustainability (in terms of energy saving) and safety (considering the COVID-19 restrictions and regulations).

VentQsys: Low-cost open IoT system for \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$CO_2$$\end{document}CO2 monitoring in classrooms

In educational context, a source of nuisance for students is carbon dioxide (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$CO_2$$\end{document} C O 2 ) concentration due to closed rooms and lack of ventilation or circulatory air. Also, in the pandemic context, ventilation in indoor environments has been proven as a good tool to control the COVID-19 infections. In this work, it is presented a low cost IoT-based open-hardware and open-software monitoring system to control ventilation, by measuring carbon dioxide (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$CO_2$$\end{document} C O 2 ), temperature and relative humidity. This system provides also support for automatic updating, auto-self calibration and adds some Cloud and Edge offloading of computational features for mapping functionalities. From the tests carried out, it is observed a good performance in terms of functionality, battery durability, compared to other measuring devices, more expensive than our proposal.

Wireless technologies, medical applications and future challenges in WBAN: a survey

Interest and need for Wireless Body Area Networks (WBANs) have significantly increased recently. WBAN consists of miniaturized sensors designed to collect and transmit data through wireless network, enabling medical specialists to monitor patients during their normal daily life and providing real time opinions for medical diagnosis. Many wireless technologies have proved themselves in WBAN applications, while others are still under investigations. The choice of the technology to adopt may depend on the disease to monitor and the performance requirements, i.e. reliability, latency and data rate. In addition, the suitable sensor is essential when seeking to extract the data related to a medical measure. This paper aims at surveying the wireless technologies used in WBAN systems. In addition to a detailed survey on the existing technologies, the use of the emerging Low Power Wide Area Network (LPWAN) technologies, and the future 5G, B5G and 6G is investigated, where the suitability of these technologies to WBAN applications is studied from several perspectives. Furthermore, medical applications of WBAN are discussed by presenting their methodologies, the adopted wireless technologies and the used sensors. Given that each medical application requires the appropriate sensor to extract the data, we highlight a wide range of the sensors used in the market for medical systems. Recent and future challenges in WBAN systems are given related to the power consumption, the emergence of the Internet of Things (IoT) technologies in WBAN and others.

An internet of things assisted drone based approach to reduce rapid spread of COVID-19

With the quick spread of pandemic disease, many individuals have lost their lives across different parts of the world. So, the need for a novel approach or model to overcome the problem becomes a necessity. In this paper, a mechanism is proposed called DBCMS (Drone Based Covid-19 Medical Service) for the safety of medical employees who are prone to Covid-19 infection. The proposed mechanism can effectively improve the treatment process of Covid-19 patients. Drones are nowadays commonly used in the field of medical emergency situations. The proposed model in this paper uses drone service to reduce the risk of infection to the doctors or other medical staff, thereby preventing the disease spread. This paper further assumes that the primary step is to isolate people at their home instead of admitting them to the hospitals, also called a situation of lockdown or curfew. Thus, in this way, the spread can be significantly reduced across the globe if DBCMS approach is implemented at cluster level.

Development of a Smart Splint to Monitor Different Parameters during the Treatment Process

For certain musculoskeletal complex rupture injuries, the only treatment available is the use of immobilization splints. This type of treatment usually causes discomfort and certain setbacks in patients. In addition, other complications are usually generated at the vascular, muscular, or articular level. Currently, there is a really possible alternative that would solve these problems and even allows a faster and better recovery. This is possible thanks to the application of engineering on additive manufacturing techniques and the use of biocompatible materials available in the market. This study proposes the use of these materials and techniques, including sensor integration inside the splints. The main parameters considered to be studied are pressure, humidity, and temperature. These aspects are combined and analyzed to determine any kind of unexpected evolution of the treatment. This way, it will be possible to monitor some signals that would be studied to detect problems that are associated to the very initial stage of the treatment. The goal of this study is to generate a smart splint by using biomaterials and engineering techniques based on the advanced manufacturing and sensor system, for clinical purposes. The results show that the prototype of the smart splint allows to get data when it is placed over the arm of a patient. Two temperatures are read during the treatment: in contact with the skin and between skin and splint. The humidity variations due to sweat inside the splint are also read by a humidity sensor. A pressure sensor detects slight changes of pressure inside the splint. In addition, an infrared sensor has been included as a presence detector.

How Connected Insurance Is Reshaping the Health Insurance Industry

The role of today's insurer is changing towards a more preventive and digital or connected approach. In this context, connected health insurance has the potential to contribute towards the general wellbeing of the population. New technologies put to use by the insurance industry might even help deal with major issues related to the rising number of people globally, of chronic disease patients and that of elders while keeping them healthier and the same time protected by insurance.

Congestion based mechanism for route discovery in a V2I-V2V system applying smart devices and IoT

The Internet of Things is a new paradigm in which objects in a specific context can be integrated into traditional communication networks to actively participate in solving a determined problem. The Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) technologies are specific cases of IoT and key enablers for Intelligent Transportation Systems (ITS). V2V and V2I have been widely used to solve different problems associated with transportation in cities, in which the most important is traffic congestion. A high percentage of congestion is usually presented by the inappropriate use of resources in vehicular infrastructure. In addition, the integration of traffic congestion in decision making for vehicular traffic is a challenge due to its high dynamic behavior. In this paper, an optimization model over the load balancing in the congestion percentage of the streets is formulated. Later, we explore a fully congestion-oriented route discovery mechanism and we make a proposal on the communication infrastructure that should support it based on V2I and V2V communication. The mechanism is also compared with a modified Dijkstra's approach that reacts at congestion states. Finally, we compare the results of the efficiency of the vehicle's trip with the efficiency in the use of the capacity of the vehicular network.