IoT Platform for COVID-19 Prevention and Control: A Survey

As a result of the worldwide transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), coronavirus disease 2019 (COVID-19) has evolved into an unprecedented pandemic. Currently, with unavailable pharmaceutical treatments and vaccines, this novel coronavirus results in a great impact on public health, human society, and global economy, which is likely to last for many years. One of the lessons learned from the COVID-19 pandemic is that a long-term system with non-pharmaceutical interventions for preventing and controlling new infectious diseases is desirable to be implemented. Internet of things (IoT) platform is preferred to be utilized to achieve this goal, due to its ubiquitous sensing ability and seamless connectivity. IoT technology is changing our lives through smart healthcare, smart home, and smart city, which aims to build a more convenient and intelligent community. This paper presents how the IoT could be incorporated into the epidemic prevention and control system. Specifically, we demonstrate a potential fog-cloud combined IoT platform that can be used in the systematic and intelligent COVID-19 prevention and control, which involves five interventions including COVID-19 Symptom Diagnosis, Quarantine Monitoring, Contact Tracing & Social Distancing, COVID-19 Outbreak Forecasting, and SARS-CoV-2 Mutation Tracking. We investigate and review the state-of-the-art literatures of these five interventions to present the capabilities of IoT in countering against the current COVID-19 pandemic or future infectious disease epidemics.Comment: 12 pages; Submitted to IEEE Internet of Things Journa

Towards Smart Healthcare: Challenges and Opportunities in IoT and ML

The COVID-19 pandemic and other ongoing health crises have underscored the need for prompt healthcare services worldwide. The traditional healthcare system, centered around hospitals and clinics, has proven inadequate in the face of such challenges. Intelligent wearable devices, a key part of modern healthcare, leverage Internet of Things technology to collect extensive data related to the environment as well as psychological, behavioral, and physical health. However, managing the substantial data generated by these wearables and other IoT devices in healthcare poses a significant challenge, potentially impeding decision-making processes. Recent interest has grown in applying data analytics for extracting information, gaining insights, and making predictions. Additionally, machine learning, known for addressing various big data and networking challenges, has seen increased implementation to enhance IoT systems in healthcare. This chapter focuses exclusively on exploring the hurdles encountered when integrating ML methods into the IoT healthcare sector. It offers a comprehensive summary of current research challenges and potential opportunities, categorized into three scenarios: IoT-based, ML-based, and the implementation of machine learning methodologies in the IoT-based healthcare industry. This compilation will assist future researchers, healthcare professionals, and government agencies by offering valuable insights into recent smart healthcare advancements.Comment: 32 pages, 3 tables, 2 figures, chapter 10 revised version of "IoT and ML for Information Management: A Smart Healthcare Perspective" under "Springer Studies in Computational Challenge" serie

Revolutionizing Public Health Surveillance: Computational Solution for Dengue Prediction

Dengue fever is a rapidly growing vector-borne viral illness that is posing a threat to an increasing number of areas worldwide. Numerous scientists have focused on various strategies to stop and limit the spread of illness. Additionally, the development of a variety of methods for ascertaining and predictive modeling through quantifiable, numerical analysis of machine learning (ML) is investigated. This research introduces a novel cubic-kernelized support vector machine with a bee optimizer (CSVM-BO) approach for dengue identification. The climate data is initially collected and preprocessed utilizing the decimal scale normalization method. In addition, we provide a feature extraction approach for linear discriminant analysis (LDA), which attempts to extract essential information for early identification and risk assessment. The findings demonstrate that the framework suggested in this research has considerable benefits in public health for dengue prediction and that our proposed CSVM-BO technique performs optimally in terms of latency (5 s), time complexity (0.62 s), and accuracy (98.57%). The problems identified by this thorough study offer a helpful foundation for epidemiology and public health research

An intelligent decision support system to prevent and control of dengue

Prevention and control of dengue fever are considered as a complex problem in day-to-day life. Noticeable changes in the human population growth, life style, and climate would cause more dengue outbreak in all over the world. The Government of India has developed a number of prevention and control strategies to protect individuals from dengue fever. Though, the strategies provided by the government are not identified based on people, space and time. In order to overcome this issue, the proposed approach presents various alternatives such as vaccination, disease surveillance, vector control, proper sanitation and increased accessed to safe drinking water, strengthening public health activities, awareness creation, and improving nutrition foods for women and child. The proposed alternatives are selected based on people, space and time criteria’s such as low temperature and heavy rain, high mean temperature and high humidity, water accumulation and rainfall resources and facilities, social culture variable and social demographic variable. The selection of alternatives based on multiple criteria’s is considered as a complex problem in decision-making framework. In general, decision makers and administrators are often used linguistic terms to give their opinions. This paper uses fuzzy logic based VIKOR (VIsekriterijumska optimizacija i KOmpromisno Resenje) method to analyze the linguistic terms collected from the decision makers and rank the best alternatives based on multiple criteria’s

IoT for Global Development to Achieve the United Nations Sustainable Development Goals: The New Scenario After the COVID-19 Pandemic

COVID-19 has not affected all countries equally: developing countries have been more disadvantaged by the pandemic. Regarding global development, the COVID-19 pandemic has forced a step back in the path to attaining the Sustainable Development Goals (SDGs). The SDGs most negatively affected by the pandemic are identified here: education, health, and work. Then using the SDGs as a reference, this research explores the new challenges faced by developing countries and the impact of the Internet of Things (IoT) after COVID-19's emergence. IoT solutions carried out in developing countries during the pandemic have been identified and reviewed. Successful Internet of Things for Development (IoT4D) projects, in relation to the SDGs, are highlighted. New social and technical challenges that have emerged for the IoT4D as a consequence of the pandemic are then studied. This work concludes that the future of IoT4D in the wake of COVID-19 should focus on the use of low-cost IoT devices for the SDGs most affected by the pandemic. After an exhaustive study, the Intelligent Internet of Things (IIoT) has been determined to be a key actor in the pandemic's wake, with a leading role in the health sector. The proposed approach includes an extensive study of the new role of the IoT4D for achieving the SDGs in our forever changed world.This work was supported by the European Commission through Urban Innovative Actions of the EPIU Getafe Project under Grant UIA04-212. The work of Ascensión López-Vargas was supported by the University of Jaén, ``Ayudas de la EDUJA para la realización de estancias para la obtención de Mención Internacional,'' in 2019. The work of Agapito Ledezma was supported by the Agencia Estatal de Investigación (AEI) under Grant RTI2018-096036-B-C22/AEI/10.13039/501100011033. The work of Araceli Sanchis was supported by the Agencia Estatal de Investigación (AEI) under Grant PID2019-104793RB-C31/AEI/10.13039/501100011033

Design and Development of Techniques to Ensure Integrity in Fog Computing Based Databases

The advancement of information technology in coming years will bring significant changes to the way sensitive data is processed. But the volume of generated data is rapidly growing worldwide. Technologies such as cloud computing, fog computing, and the Internet of things (IoT) will offer business service providers and consumers opportunities to obtain effective and efficient services as well as enhance their experiences and services; increased availability and higher-quality services via real-time data processing augment the potential for technology to add value to everyday experiences. This improves human life quality and easiness. As promising as these technological innovations, they are prone to security issues such as data integrity and data consistency. However, as with any computer system, these services are not without risks. There is the possibility that systems might be infiltrated by malicious transactions and, as a result, data could be corrupted, which is a cause for concern. Once an attacker damages a set of data items, the damage can spread through the database. When valid transactions read corrupted data, they can update other data items based on the value read. Given the sensitive nature of important data and the critical need to provide real-time access for decision-making, it is vital that any damage done by a malicious transaction and spread by valid transactions must be corrected immediately and accurately. In this research, we develop three different novel models for employing fog computing technology in critical systems such as healthcare, intelligent government system and critical infrastructure systems. In the first model, we present two sub-models for using fog computing in healthcare: an architecture using fog modules with heterogeneous data, and another using fog modules with homogeneous data. We propose a unique approach for each module to assess the damage caused by malicious transactions, so that original data may be recovered and affected transactions may be identified for future investigations. In the second model, we introduced a unique model that uses fog computing in smart cities to manage utility service companies and consumer data. Then we propose a novel technique to assess damage to data caused by an attack. Thus, original data can be recovered, and a database can be returned to its consistent state as no attacking has occurred. The last model focus of designing a novel technique for an intelligent government system that uses fog computing technology to control and manage data. Unique algorithms sustaining the integrity of system data in the event of cyberattack are proposed in this segment of research. These algorithms are designed to maintain the security of systems attacked by malicious transactions or subjected to fog node data modifications. A transaction-dependency graph is implemented in this model to observe and monitor the activities of every transaction. Once an intrusion detection system detects malicious activities, the system will promptly detect all affected transactions. Then we conducted a simulation study to prove the applicability and efficacy of the proposed models. The evaluation rendered this models practicable and effective

Internet of Things for Sustainable Human Health

The sustainable health IoT has the strong potential to bring tremendous improvements in human health and well-being through sensing, and monitoring of health impacts across the whole spectrum of climate change. The sustainable health IoT enables development of a systems approach in the area of human health and ecosystem. It allows integration of broader health sub-areas in a bigger archetype for improving sustainability in health in the realm of social, economic, and environmental sectors. This integration provides a powerful health IoT framework for sustainable health and community goals in the wake of changing climate. In this chapter, a detailed description of climate-related health impacts on human health is provided. The sensing, communications, and monitoring technologies are discussed. The impact of key environmental and human health factors on the development of new IoT technologies also analyzed

INTEGRATION OF INTERNET OF THINGS AND HEALTH RECOMMENDER SYSTEMS

The Internet of Things (IoT) has become a part of our lives and has provided many enhancements to day-to-day living. In this project, IoT in healthcare is reviewed. IoT-based healthcare is utilized in remote health monitoring, observing chronic diseases, individual fitness programs, helping the elderly, and many other healthcare fields. There are three main architectures of smart IoT healthcare: Three-Layer Architecture, Service-Oriented Based Architecture (SoA), and The Middleware-Based IoT Architecture. Depending on the required services, different IoT architecture are being used. In addition, IoT healthcare services, IoT healthcare service enablers, IoT healthcare applications, and IoT healthcare services focusing on Smartwatch are presented in this research. Along with IoT in smart healthcare, Health Recommender Systems integration with IoT is important. Main Recommender Systems including Content-based filtering, Collaborative-based filtering, Knowledge-based filtering, and Hybrid filtering with machine learning algorithms are described for the Health Recommender Systems. In this study, a framework is presented for the IoT-based Health Recommender Systems. Also, a case is investigated on how different algorithms can be used for Recommender Systems and their accuracy levels are presented. Such a framework can help with the health issues, for example, risk of going to see the doctor during pandemic, taking quick actions in any health emergencies, affordability of healthcare services, and enhancing the personal lifestyle using recommendations in non-critical conditions. The proposed framework can necessitate further development of IoT-based Health Recommender Systems so that people can mitigate their medical emergencies and live a healthy life