COVID Hotspot Alert System using Geo-Fencing Technique

Covid-19 is an extremely communicable disease. It becomes extremely hard to control once it begins to spread. One of the most important and effective steps to break the chain and keep healthy people from getting infected is social isolation/distancing. When an infected person comes into contact with a healthy person, that person becomes infected as well, and the chain reaction continues. To curb this, COVID alert system using geo-fencing is developed. This system uses a GPS module to create a Geo Fence around the infected area and the healthy area. The live/current GPS location/coordinate is compared with the hotspot co-ordinates. The GSM module with Sim800L will send an alert to healthy people when they come into contact with virus-infected areas. The device comes with a GPS, GSM module with Sim800L and an OLED which displays the alert message. The device can be fit into any public or private transport, so that the healthy person will be prevented from entering the hotspot zones unnecessarily, thereby blocking the virus spread. © 2022 IEEE.

An IoT-Based Wristband for Automatic People Tracking, Contact Tracing and Geofencing for COVID-19.

The coronavirus disease (COVID-19) pandemic has triggered a huge transformation in the use of existing technologies. Many innovations have been made in the field of contact tracing and tracking. However, studies have shown that there is no holistic system that integrates the overall process from data collection to the proper analysis of the data and actions corresponding to the results. It is critical to identify any contact with infected people and to ensure that they do not interact with others. In this research, we propose an IoT-based system that provides automatic tracking and contact tracing of people using radio frequency identification (RFID) and a global positioning system (GPS)-enabled wristband. Additionally, the proposed system defines virtual boundaries for individuals using geofencing technology to effectively monitor and keep track of infected people. Furthermore, the developed system offers robust and modular data collection, authentication through a fingerprint scanner, and real-time database management, and it communicates the health status of the individuals to appropriate authorities. The validation results prove that the proposed system identifies infected people and curbs the spread of the virus inside organizations and workplaces.

Geofencing-based Congestion Control in Workplaces Environment using Sequential Pattern Mining

To control congestion in the workplace environment especially in crises like the COVID-19 pandemic, this requires careful control of highly crowded workplace locations. Therefore, innovative technologies, such as geofencing and sequential pattern mining can be used to estimate people movement pattern and combat the spread of COVID-19. In this paper, the workplace area is divided into a set of geofences by using geofencing technology. Then, the movement profiles of each user are estimated to control the possible congestion in the workplace's enviroment. To accomplish this, the user's historical geofence transitions are used to anticipate the next time the user will leave the current geofence. The Sequential Pattern Discovery using Equivalence classes (CM-SPADE), Succinct BWT-based Sequence prediction model (SuBSeq) and Compact Prediction Tree + (CPT+) algorithms are adopted to predict the user's next geofence. In the CM-SPADE algorithm, a vertical database is obtained from the available database and the frequent sequence is found based on relative support, confidence, and lift measures. Meanwhile, in the training phase of the SuBSeq algorithm, Ferragina and Manzini (FM)-index and Burrows-Wheeler Transform string are generated. Then, in the ready-to-predict phase, the next geofence is anticipated. The CPT+ algorithm is based on generating Prediction Tree (PT), Lookup Table (LT), and Inverted Index (IIdx) for the training data. Then, Frequent Subsequence Compression (FSC) and Simple Branches Compression (SBC) are used to reduce the size of the PT. In addition, the Prediction with improved Noise Reduction (PNR) method is utilized to reduce the execution time. The results show remarkable superiority for SuBSeq algorithm over CM-SPADE and CPT+ with the accuracy greater than 90% withh an average of 8 input geofences to predict the next geofence. © 2022 IEEE.

A Machine Learning Approach for Movement Monitoring in Clustered Workplaces to Control COVID-19 Based on Geofencing and Fusion of Wi-Fi and Magnetic Field Metrics.

The ubiquitous existence of COVID-19 has required the management of congested areas such as workplaces. As a result, the use of a variety of inspiring tools to deal with the spread of COVID-19 has been required, including internet of things, artificial intelligence (AI), machine learning (ML), and geofencing technologies. In this work, an efficient approach based on the use of ML and geofencing technology is proposed to monitor and control the density of persons in workplaces during working hours. In particular, the workplace environment is divided into a number of geofences in which each person is associated with a set of geofences that make up their own cluster using a dynamic user-centric clustering scheme. Different metrics are used to generate a unique geofence digital signature (GDS) such as Wi-Fi basic service set identifier, Wi-Fi received signal strength indication, and magnetic field data, which can be collected using the person's smartphone. Then, these metrics are utilized by different ML techniques to generate the GDS for each indoor geofence and each building geofence as well as to detect whether the person is in their cluster. In addition, a Layered-Architecture Geofence Division method is considered to reduce the processing overhead at the person's smartphone. Our experimental results demonstrate that the proposed approach can perform well in a real workplace environment. The results show that the system accuracy is about 98.25% in indoor geofences and 76% in building geofences.

Dynamic User-Centric Clustered Workplaces for COVID-19 Control Measures Based on Geofencing and Deep Learning

Control measures have been applied in recent years due to the COVID-19 pandemic. Different technologies including artificial intelligence (AI) and geofencing are required to be exploited for developing efficient techniques to deal with this crisis. Workplaces are the most dangerous areas that can lead to the infection of the pandemic. This is due to the increased density of people and transactions in limited places. In this paper, an efficient approach is proposed to monitor and impose COVID-19 control measures in workplaces. The workplace environment is clustered based on a dynamic user-centric clustering scheme, where each person in the workplace is assigned to a set of associated geofences that form its cluster. For each geofence, different wireless and network metrics are used for generating its digital signature. An efficient technique based on deep learning is proposed to generate the geofence digital signature and detect whether the person is inside his associated cluster or not. Experimental results show the effectiveness of the proposed technique for different locations in a real workplace. Specifically, an accuracy of 92.86% is achieved in a workplace environment by the proposed approach. © 2022 ACM.

Mobile geo-fencing triggers for alerting entries into COVID-19 containment zones using IoT

In a thickly populated nation like India, it is hard to forecast community transmission of COVID-19. Hence, a number of containment zones had been recognized all over the country separated into red, orange, and green zones, individually. People are restricted to move into these containment zones. This chapter focuses on informing the public about the containment zone when they are in travel and also sends an alert to the police when a person enters the containment zone without permission using the containment zone alert system. This chapter suggests a containment zone alert system by means of geo-fencing technology to identify the movement of public, deliver info about the danger to the public in travel and also send an alert to the police when there is an entry or exit detected in the containment zone by the use of location-based services (LBS). By creating a fence virtually called geo-fence at the containment zones established based on the government info, this system monitors public movements like entry and exit to fence. (PsycInfo Database Record (c) 2022 APA, all rights reserved)

Internet of Things Application in Controlling the Coronavirus Disease Spread in Hajj Season

Coronavirus Disease 2019 (COVID-19), caused by the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is currently a threat to the global human population. Infectious viruses, such as SARS-CoV-2, are easily transmitted from person to person and spread very quickly. These viruses are likely to spread anywhere there are massive crowds in confined spaces-and the Hajj pilgrimage to Makkah, Saudi Arabia is no exception. This work aims to prevent the spread of infection in the early stages of an outbreak. This paper explores the various methods for monitoring and controlling infectious disease during the Hajj including strengthening disease control and methods for providing the Saudi Ministry of Health (MOH) insights to enable them to plan for the specific challenges of controlling Coronavirus disease during the Hajj. This paper proposes a model, based on the Radio Frequency Identification Devices (RFID), Global Positioning System (GPS), wearable watch technology, and cloud computing infrastructure, which detects and monitors infected pilgrims and also aids in the identification of those pilgrims exposed to sources of a virus. Disciplinary: Information System, Technology, and Application, Healthcare Management. (C) 2022 INT TRANS J ENG MANAG SCI TECH.

IoT Enabled Geofencing-Based System for Monitoring and Enforcing COVID-19 Control Measures in Workplaces, Service Areas and Distributed Home Quarantine

Due to the outbreak of COVID-19, the whole world is thinking of new mechanisms, preventive measures to protect human life from the widespread of the pandemic. Many countries imposed COVID-19 control measures to limit further infection spread. However, controlling the spread of COVID-19 without taking severe measures (e.g. lockdown and full quarantine) that have undesirable economic effects becomes a major challenge. By exploiting the modern and available information and communication technologies, innovative solutions may emerge to face and deal with this crisis. In this paper, an innovative and flexible solution based on the exploitation and integration of geofencing, and Internet of Things (IoT) technologies is proposed to enhance the crisis management framework in response to the COVID-19 pandemic. The proposed solution is designed to monitor and impose COVID-19 control measures in different environments such as distributed home quarantine, workplaces, service areas, and COVID-19 infected zones. © 2022 IEEE.