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

A fog computing solution for context-based privacy leakage detection for android healthcare devices

Intelligent medical service system integrates wireless internet of things (WIoT), including medical sensors, wireless communications, and middleware techniques, so as to collect and analyze patients' data to examine their physical conditions by many personal health devices (PHDs) in real time. However, large amount of malicious codes on the Android system can compromise consumers' privacy, and further threat the hospital management or even the patients' health. Furthermore, this sensor-rich system keeps generating large amounts of data and saturates the middleware system. To address these challenges, we propose a fog computing security and privacy protection solution. Specifically, first, we design the security and privacy protection framework based on the fog computing to improve tele-health and tele-medicine infrastructure. Then, we propose a context-based privacy leakage detection method based on the combination of dynamic and static information. Experimental results show that the proposed method can achieve higher detection accuracy and lower energy consumption compared with other state-of-art methods.This work was supported by the National Natural Science Foundation of China (General Program) under Grant No.61572253, the 13th Five-Year Plan Equipment Pre-Research Projects Fund under Grant No.61402420101HK02001, and the Aviation Science Fund under Grant No. 2016ZC52030

Exploratory Analysis of Internet of Things (IoT) in Healthcare: A Topic Modeling Approach

The rapid integration of the physical and cyber worlds through the Internet of Things, or IoTs, is transforming our lives in ways that we could not have imagined even five years ago. Although they are still in their infancy, IoTs have already made a significant impact, particularly in the healthcare domain. The purpose of this study is to unravel key themes latent in the sparse but growing academic literature on the application of IoTs in healthcare. Specifically, we performed topic modeling and identified five dominant clusters of research, namely, privacy and security, wireless network technologies, applications, data, and smart health and cloud. Our results show that research in healthcare IoT has mainly focused on the technical aspects with little attention to social concerns. In addition to categorizing and discussing the topics identified, the paper provides directions for future researc

PDRL: Multi-Agent based Reinforcement Learning for Predictive Monitoring

Reinforcement learning has been increasingly applied in monitoring applications because of its ability to learn from previous experiences and can make adaptive decisions. However, existing machine learning-based health monitoring applications are mostly supervised learning algorithms, trained on labels and they cannot make adaptive decisions in an uncertain complex environment. This study proposes a novel and generic system, predictive deep reinforcement learning (PDRL) with multiple RL agents in a time series forecasting environment. The proposed generic framework accommodates virtual Deep Q Network (DQN) agents to monitor predicted future states of a complex environment with a well-defined reward policy so that the agent learns existing knowledge while maximizing their rewards. In the evaluation process of the proposed framework, three DRL agents were deployed to monitor a subject's future heart rate, respiration, and temperature predicted using a BiLSTM model. With each iteration, the three agents were able to learn the associated patterns and their cumulative rewards gradually increased. It outperformed the baseline models for all three monitoring agents. The proposed PDRL framework is able to achieve state-of-the-art performance in the time series forecasting process. The proposed DRL agents and deep learning model in the PDRL framework are customized to implement the transfer learning in other forecasting applications like traffic and weather and monitor their states. The PDRL framework is able to learn the future states of the traffic and weather forecasting and the cumulative rewards are gradually increasing over each episode.Comment: This work has been submitted to the Springer for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessibl

Preventing the next Aedes-borne arboviral disease epidemic

Dengue, chikungunya and Zika are infectious diseases transmitted to humans by Aedes species mosquitoes (mainly Aedes aegypti). These arboviruses caused outbreaks in several countries belonging to Africa, America, Asia, the Caribbean, and the Pacific. Besides all recommendations of the World Health Organization (WHO), and novel mosquito control strategies that have been developed, countries are still struggling with preventing and controlling the transmission of Aedes-borne infectious diseases (ABIDs). To overcome the difficulties in ABID control interventions, first, the challenges with regards to Aedes control at the macro-level (health system), meso-level (community), and the micro-level (individuals) of that specific country need to be determined. Therefore, this dissertation's overall aim is to investigate the context and concepts shaping the health system, community and individual prevention and control interventions/ behaviour for ABIDs in Curaçao. We have combined different research methodologies (qualitative and quantitative) and disciplines (e.g., epidemiology, entomology, microbiology, and social and environmental sciences) to study the above-mentioned research topics. Different theoretical frameworks and concepts were used to understand the intricate relationship between macro (health system), meso (social groups) and micro-level (individuals) vis-à-vis ABIDs prevention and control. All studies presented in this thesis provide readers with the information to understand the challenges of Aedes control in Curaçao holistically. Our results highlight the relevance of risk communication, cultural schemas, and heuristics in disease control. This information is useful to policymakers and others concerned with the prevention and control of ABIDs

GeoFog4Health: a fog-based SDI framework for geospatial health big data analysis

Spatial Data Infrastructure (SDI) is an important framework for sharing geospatial big data using the web. Integration of SDI with cloud computing led to emergence of Cloud-SDI as a tool for transmission, processing and analysis of geospatial data. Fog computing is a paradigm where embedded computers are employed to increase the throughput and reduce latency at the edge of the network. In this study, we developed and evaluated a Fog-based SDI framework named GeoFog4Health for mining analytics from geo-health big data. We built prototypes using Intel Edison and Raspberry Pi for studying the comparative performance. We conducted a case study on Malaria vector-borne disease positive maps of Maharastra state in India. The proposed framework had provision of lossless data compression for reduced data transfer. Also, overlay analysis of geospatial data was implemented. In addition, we discussed energy savings, cost analysis and scalability of the proposed framework with respect to efficient data processing. We compared the performance of the proposed framework with the state-of-the-art Cloud-SDI in terms of analysis time. Results and discussions showed the efficacy of the proposed system for enhanced analysis of geo-health big data generated from a variety of sensing frameworks

Remote health monitoring systems for elderly people: a survey

This paper addresses the growing demand for healthcare systems, particularly among the elderly population. The need for these systems arises from the desire to enable patients and seniors to live independently in their homes without relying heavily on their families or caretakers. To achieve substantial improvements in healthcare, it is essential to ensure the continuous development and availability of information technologies tailored explicitly for patients and elderly individuals. The primary objective of this study is to comprehensively review the latest remote health monitoring systems, with a specific focus on those designed for older adults. To facilitate a comprehensive understanding, we categorize these remote monitoring systems and provide an overview of their general architectures. Additionally, we emphasize the standards utilized in their development and highlight the challenges encountered throughout the developmental processes. Moreover, this paper identifies several potential areas for future research, which promise further advancements in remote health monitoring systems. Addressing these research gaps can drive progress and innovation, ultimately enhancing the quality of healthcare services available to elderly individuals. This, in turn, empowers them to lead more independent and fulfilling lives while enjoying the comforts and familiarity of their own homes. By acknowledging the importance of healthcare systems for the elderly and recognizing the role of information technologies, we can address the evolving needs of this population. Through ongoing research and development, we can continue to enhance remote health monitoring systems, ensuring they remain effective, efficient, and responsive to the unique requirements of elderly individuals

Enabling the Internet of Mobile Crowdsourcing Health Things: A Mobile Fog Computing, Blockchain and IoT Based Continuous Glucose Monitoring System for Diabetes Mellitus Research and Care

[Abstract] Diabetes patients suffer from abnormal blood glucose levels, which can cause diverse health disorders that affect their kidneys, heart and vision. Due to these conditions, diabetes patients have traditionally checked blood glucose levels through Self-Monitoring of Blood Glucose (SMBG) techniques, like pricking their fingers multiple times per day. Such techniques involve a number of drawbacks that can be solved by using a device called Continuous Glucose Monitor (CGM), which can measure blood glucose levels continuously throughout the day without having to prick the patient when carrying out every measurement. This article details the design and implementation of a system that enhances commercial CGMs by adding Internet of Things (IoT) capabilities to them that allow for monitoring patients remotely and, thus, warning them about potentially dangerous situations. The proposed system makes use of smartphones to collect blood glucose values from CGMs and then sends them either to a remote cloud or to distributed fog computing nodes. Moreover, in order to exchange reliable, trustworthy and cybersecure data with medical scientists, doctors and caretakers, the system includes the deployment of a decentralized storage system that receives, processes and stores the collected data. Furthermore, in order to motivate users to add new data to the system, an incentive system based on a digital cryptocurrency named GlucoCoin was devised. Such a system makes use of a blockchain that is able to execute smart contracts in order to automate CGM sensor purchases or to reward the users that contribute to the system by providing their own data. Thanks to all the previously mentioned technologies, the proposed system enables patient data crowdsourcing and the development of novel mobile health (mHealth) applications for diagnosing, monitoring, studying and taking public health actions that can help to advance in the control of the disease and raise global awareness on the increasing prevalence of diabetes.Xunta de Galicia; ED431G/01Xunta de Galicia; ED431C 2016-045Agencia Estatal de Investigación de España; TEC2016-75067-C4-1-

Statistical Review of Health Monitoring Models for Real-Time Hospital Scenarios

Health Monitoring System Models (HMSMs) need speed, efficiency, and security to work. Cascading components ensure data collection, storage, communication, retrieval, and privacy in these models. Researchers propose many methods to design such models, varying in scalability, multidomain efficiency, flexibility, usage and deployment, computational complexity, cost of deployment, security level, feature usability, and other performance metrics. Thus, HMSM designers struggle to find the best models for their application-specific deployments. They must test and validate different models, which increases design time and cost, affecting deployment feasibility. This article discusses secure HMSMs' application-specific advantages, feature-specific limitations, context-specific nuances, and deployment-specific future research scopes to reduce model selection ambiguity. The models based on the Internet of Things (IoT), Machine Learning Models (MLMs), Blockchain Models, Hashing Methods, Encryption Methods, Distributed Computing Configurations, and Bioinspired Models have better Quality of Service (QoS) and security than their counterparts. Researchers can find application-specific models. This article compares the above models in deployment cost, attack mitigation performance, scalability, computational complexity, and monitoring applicability. This comparative analysis helps readers choose HMSMs for context-specific application deployments. This article also devises performance measuring metrics called Health Monitoring Model Metrics (HM3) to compare the performance of various models based on accuracy, precision, delay, scalability, computational complexity, energy consumption, and security

Cloud Computing to Fog Computing: A Paradigm Shift

Fog computing scatters the resources throughout the system to provide services close to the edge of the network. This chapter provides an overview of different segments associated with the fog computing paradigm for implementing efficient Internet of Things (IoT) applications. Section 1 provides an overview and motivation behind the provision of healthcare services using cloud and fog computing paradigms. Section 2 provides the literature and research work related to the deployment of healthcare applications using cloud and fog computing architectures. Section 3 provides the architectural design of a fog computing-based remote pain monitoring application. Section 4 provides the simulation parameters and architecture that are arranged for the evaluation of the proposed policy. Finally, Section 5 concludes and discusses the results of simulations obtained on different scales