CONFRONT: Cloud-fog-dew based monitoring framework for COVID-19 management

Innovations Syst Softw Eng

Mukherjee²

2022

Self Cite

The outbreak of 2019 novel coronavirus (COVID-19) has triggered unprecedented challenges and put the whole world in a parlous condition. The impacts of COVID-19 is a matter of grave concern in terms of fatality rate, socio-economical condition, health infrastructure. It is obvious that only pharmaceutical solutions (vaccine) cannot eradicate this pandemic completely, and effective strategies regarding lockdown measures, restricted mobility, emergency services to users-in brief data-driven decision system is of utmost importance. This necessitates an efficient data analytics framework, data infrastructure to store, manage pandemic related information, and distributed computing platform to support such data-driven operations. In the past few decades, Internet of Things-based devices and applications have emerged significantly in various sectors including healthcare and time-critical applications. To be specific, health-sensors help to accumulate health-related parameters at different time-instances of a day, the movement sensors keep track of mobility traces of the user, and helps to assist them in varied conditions. The smartphones are equipped with several such sensors and the ability of low-cost connected sensors to cover large areas makes it the most useful component to combat pandemics such as COVID-19. However, analysing and managing the huge amount of data generated by these sensors is a big challenge. In this paper we have proposed a unified framework which has three major components: (i) Spatial Data Infrastructure to manage, store, analyse and share spatio-temporal information with stakeholders efficiently, (ii) Cloud-Fog-Edge-based hierarchical architecture to support preliminary diagnosis, monitoring patients' mobility, health parameters and activities while they are in quarantine or homebased treatment, and (iii) Assisting users in varied emergency situation leveraging efficient data-driven techniques at lowlatency and energy consumption. The mobility data analytics along with SDI is required to interpret the movement dynamics of the region and correlate with COVID-19 hotspots. Further, Cloud-Fog-Edge-based system architecture is required to provision healthcare services efficiently and in timely manner. The proposed framework yields encouraging results in taking decisions based on the COVID-19 context and assisting users effectively by enhancing accuracy of detecting suspected infected people by ∼24% and reducing delay by ∼55% compared to cloud-only system.

Section: Resultsmentioning

confidence: 99%

STROVE: spatial data infrastructure enabled cloud–fog–edge computing framework for combating COVID-19 pandemic

Innovations Syst Softw Eng

Mukherjee²

2022

Self Cite

“…It gives provides applicability of fog devices and gateways in Healthcare environment for current and future applications. The analysis for the cloud computing, IoT and fog computing is done to provide context-aware services to the users when required [37]. In [36] a cloud-fog-dew based monitoring Framework is proposed for Covid-19 management known as CONFRONT.…”

Section: Cognitive Systems a Survey On Cognitive Computing (Cc) Is Do...mentioning

confidence: 99%

Post-Covid Remote Patient Monitoring using Medical Internet of Things and Machine Learning Analytics

Rahman

Parveen

Sofi

et al. 2023

SCPE

The Covid-19 pandemic disturbed the smooth functioning of healthcare services throughout the world. New practices such as masking, social distancing and so on were followed to prevent the spread. Further, the severity of the problem increases for the elderly people and people having co-morbidities as proper medical care was not possible and as a result many deaths were recorded. Even for those patients who recovered from Covid could not get proper health monitoring in the Post-Covid phase as a result many deaths and severity in health conditions were reported after the Covid recovery i.e., the Post-Covid era. Technical interventions like the Internet of Things (IoT) based remote patient monitoring using Medical Internet of Things (M-IoT) wearables is one of the solutions that could help in the Post-Covid scenarios. The paper discusses a proposed framework where in a variety of IoT sensing devices along with ML algorithms are used for patient monitoring by utilizing aggregated data acquired from the registered Post-Covid patients. Thus, by using M-IoT along with Machine Learning (ML) approaches could help us in monitoring Post-Covid patients with co-morbidities for and immediate medical help.

“…2,3 The combination of these emerging technologies facilitate several time-critical applications, namely, continuous patient monitoring in home, ambulance, and hospital, 4,5 as well as assisting users in emergency situations such as disasters, pandemics. 6,7 In recent times, there is a growing need for analyzing spatio-temporal datasets for extracting meaningful information and providing location-aware services, 8 such as trip-planning, next location prediction from sparse datapoints 9 weather forecasting, and even health management. Notably, one of the significant aspects of epidemiological analysis is retrieving the correlations among the people and disease spread in spatial and temporal dimensions to adapt the countermeasures effectively.…”

Section: Introductionmentioning

confidence: 99%

“…Here, edge and fog nodes extend the cloud computing functionality by processing, analyzing, and storing the information near the end‐user 2,3 . The combination of these emerging technologies facilitate several time‐critical applications, namely, continuous patient monitoring in home, ambulance, and hospital, 4,5 as well as assisting users in emergency situations such as disasters, pandemics 6,7 . In recent times, there is a growing need for analyzing spatio‐temporal datasets for extracting meaningful information and providing location‐aware services, 8 such as trip‐planning, next location prediction from sparse datapoints 9 weather forecasting, and even health management.…”

Section: Introductionmentioning

confidence: 99%

STOPPAGE: Spatio‐temporal data driven cloud‐fog‐edge computing framework for pandemic monitoring and management

Mukherjee²,

et al. 2022

Softw Pract Exp

Several global health incidents and evidences show the increasing likelihood of pandemics (large‐scale outbreaks of infectious disease), which has adversely affected all aspects of human lives. It is essential to develop an analytics framework by extracting and incorporating the knowledge of heterogeneous data‐sources to deliver insights for enhancing preparedness to combat the pandemic. Specifically, human mobility, travel history, and other transport statistics have significantly impact on the spread of any infectious disease. This article proposes a spatio‐temporal knowledge mining framework, named STOPPAGE, to model the impact of human mobility and other contextual information over the large geographic areas in different temporal scales. The framework has two key modules: (i) spatio‐temporal data and computing infrastructure using fog/edge based architecture; and (ii) spatio‐temporal data analytics module to efficiently extract knowledge from heterogeneous data sources. We created a pandemic‐knowledge graph to discover correlations among mobility information and disease spread, a deep learning architecture to predict the next hotspot zones. Further, we provide necessary support in home‐health monitoring utilizing Femtolet and fog/edge based solutions. The experimental evaluations on real‐life datasets related to COVID‐19 in India illustrate the efficacy of the proposed methods. STOPPAGE outperforms the existing works and baseline methods in terms of accuracy by prefix≈$$ \approx $$(18–21)% in predicting hotspots and reduces the power consumption of the smartphone significantly. The scalability study yields that the STOPPAGE framework is flexible enough to analyze a huge amount of spatio‐temporal datasets and reduces the delay in predicting health status compared to the existing studies.