Detecting coronavirus contact using internet of things

Author(s):  
Thangamani M. ◽  
Ganthimathi M. ◽  
Sridhar S.R. ◽  
Akila M. ◽  
Keerthana R. ◽  
...  

Purpose The purpose of this paper is to identify coronavirus contact using internet of things. The disease is said to be highly contagious with the contact of infected persons. Feared to be air-borne, droplets of body fluids can transmit the disease in a matter of hours. The predominant symptoms of the COVID-19 are high fever, cough, breathing problem, etc. Recent studies have demonstrated the evolution of the disease to hide its symptoms. As it is highly transmissible, this disease might spread at an exponential rate costing the lives of thousands of people. The chain of transmission has to be detected with utmost priority through early detection and isolation of infected people. Automated internet of things (IoT) devices can be used in design and implementation of a prediction scheme for reporting the health-care risks of the patients with various parameters such as temperature, humidity and blood pressure. Design/methodology/approach IoT is a configuration of multiple autonomous and embedded wireless devices for serving a purpose. Every object possesses an individual identity and will serve to register critical events as entries for future learning and decisions. IoT plays an inevitable role in medical industries, detection of vital signs of diseases and monitoring. Among other life-threatening diseases, a new pandemic is on rise among world nations. COVID-19, a novel severe acute respiratory syndrome virus originated from animals in December 2019 and is becoming a serious menace to Governments, despite serious measures of lockdowns. Findings In this paper, the authors defined an architecture of an IoT system to predict the Covid-19 disease by getting the data from the human through sensors and send the data to the doctor using mobile, computer, etc. The main goal is early health surveillance by predicting COVID-19. Accordingly, the authors are able to identify both symptomatic and asymptomatic patients, which will help in the early prediction of disease. Originality/value Using the proposed method, the authors can save the time of both patient and doctor by ensuring timely medical treatment and contribute toward breaking the transmission chain. In so doing, the method also contributes toward avoiding unnecessary expenses and saving human lives.

2017 ◽  
Vol 21 (1) ◽  
pp. 57-70 ◽  
Author(s):  
Lorna Uden ◽  
Wu He

Purpose Current knowledge management (KM) systems cannot be used effectively for decision-making because of the lack of real-time data. This study aims to discuss how KM can benefit by embedding Internet of Things (IoT). Design/methodology/approach The paper discusses how IoT can help KM to capture data and convert data into knowledge to improve the parking service in transportation using a case study. Findings This case study related to intelligent parking service supported by IoT devices of vehicles shows that KM can play a role in turning the incoming big data collected from IoT devices into useful knowledge more quickly and effectively. Originality/value The literature review shows that there are few papers discussing how KM can benefit by embedding IoT and processing incoming big data collected from IoT devices. The case study developed in this study provides evidence to explain how IoT can help KM to capture big data and convert big data into knowledge to improve the parking service in transportation.


Subject IoT ecosystem. Significance The market for the Internet of Things (IoT) or connected devices is expanding rapidly, with no manufacturer currently forecast to dominate the supply chain. This has fragmented the emerging IoT ecosystem, triggering questions about interoperability and cybersecurity of IoT devices. Impacts Firms in manufacturing, transportation and logistics and utilities are expected to see the highest IoT spending in coming years. The pace of IoT adoption is inextricably linked to that of related technologies such as 5G, artificial intelligence and cloud computing. Data privacy and security will be the greatest constraint to IoT adoption.


2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
J Aruna Santhi ◽  
G Vijaya Saradhi

PurposeThis paper tactics to implement the attack detection in medical Internet of things (IoT) devices using improved deep learning architecture for accomplishing the concept bring your own device (BYOD). Here, a simulation-based hospital environment is modeled where many IoT devices or medical equipment are communicated with each other. The node or the device, which is creating the attack are recognized with the support of attribute collection. The dataset pertaining to the attack detection in medical IoT is gathered from each node that is considered as features. These features are subjected to a deep belief network (DBN), which is a part of deep learning algorithm. Despite the existing DBN, the number of hidden neurons of DBN is tuned or optimized correctly with the help of a hybrid meta-heuristic algorithm by merging grasshopper optimization algorithm (GOA) and spider monkey optimization (SMO) in order to enhance the accuracy of detection. The hybrid algorithm is termed as local leader phase-based GOA (LLP-GOA). The DBN is used to train the nodes by creating the data library with attack details, thus maintaining accurate detection during testing.Design/methodology/approachThis paper has presented novel attack detection in medical IoT devices using improved deep learning architecture as BYOD. With this, this paper aims to show the high convergence and better performance in detecting attacks in the hospital network.FindingsFrom the analysis, the overall performance analysis of the proposed LLP-GOA-based DBN in terms of accuracy was 0.25% better than particle swarm optimization (PSO)-DBN, 0.15% enhanced than grey wolf algorithm (GWO)-DBN, 0.26% enhanced than SMO-DBN and 0.43% enhanced than GOA-DBN. Similarly, the accuracy of the proposed LLP-GOA-DBN model was 13% better than support vector machine (SVM), 5.4% enhanced than k-nearest neighbor (KNN), 8.7% finer than neural network (NN) and 3.5% enhanced than DBN.Originality/valueThis paper adopts a hybrid algorithm termed as LLP-GOA for the accurate detection of attacks in medical IoT for improving the enhanced security in healthcare sector using the optimized deep learning. This is the first work which utilizes LLP-GOA algorithm for improving the performance of DBN for enhancing the security in the healthcare sector.


2021 ◽  
Vol 23 (12) ◽  
pp. 249-261
Author(s):  
Monish Chandradhara ◽  
◽  
Ashlin George ◽  
Mohammed Faraaz ◽  
Aryan Saraf ◽  
...  

The Internet of Things (IoT) is used to describe a network of physical objects which are connected to each other with the help of embedded sensors, transceivers and software code to communicate and share data with each other. The Internet of Things, which first took shape back in the 1980s through an ARPA-NET connected Coca-Cola Vending machine in Carnegie Mellon University has come a long way since then. In today’s world a growing proportion of new home appliances are coming with IoT features embedded in them. These include, but are not limited to Television Sets, Refrigerators, Microwave Ovens, Fitness trackers, Smart watches and even IoT enabled light bulbs. IoT devices are not only used in Homes but also in Industries where they can play a pivotal role in bringing down operating costs and helping companies make better business decisions to align their companies in the right direction. With the advent of Industrial Internet of Things (IIoT) and Industry 4.0, a new standard has been set that organizations follow to gain a lot of key advantages such as improving productivity, customer experience, driving down costs and taking better informed decisions. IoT devices have also entered the healthcare industry where they play a paramount role in monitoring vital signs of patients. These devices continuously monitor all the vital signs such as heart rate, blood pressure and oxygen saturation.


2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Himmet Karadal ◽  
A. Mohammed Abubakar

PurposeThe authors’ understanding of the Internet of things (IoT) skills and needs satisfaction for IoT devices and generational cohorts' variations remains limited as commentaries are often oversimplified and generalized. This research fills a gap in the literature by highlighting the dynamics between the IoT skills and needs satisfaction for IoT devices and seeks to expound on the variations among generational cohorts using the self-determination theory.Design/methodology/approachSurvey data were obtained from 1,245 residents and IoT device users in Aksaray, Turkey. The obtained data were analyzed with variance-based structural equation modeling and the analysis of variance technique.FindingsThe results demonstrate that IoT skills determine the needs satisfaction for IoT devices. Generation Xers, Generation Yers and Generation Zers are distinct cohorts with respect to the IoT skills and needs satisfaction for IoT devices.Originality/valueCollectively, this study provides empirical evidence that informs the debate about the contributions of IoT skills and generational cohorts on needs satisfaction for IoT devices. The implications and several avenues for future theory-building research are discussed.Peer reviewThe peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-04-2020-0144


Subject Healthcare IoT. Significance As healthcare Internet of Things (IoT) devices become widespread through wearable technology such as ‘smartwatches’, companies are looking to the next advance. With long-term intentions including wireless interfacing with brain implants, security and privacy issues will take centre-stage in IoT design. Impacts Regulators may pressure companies to educate users on privacy. Cybersecurity firms will be able to leverage limited understanding of security among existing medical IoT manufacturers to win new clients. Medical IoT device manufacturers will need to priotise safety to avoid losing market share and falling foul of regulators.


2020 ◽  
Vol 30 (2) ◽  
pp. 201-220 ◽  
Author(s):  
Monica Maceli

Purpose Cultural heritage archives rely on environmental monitoring devices, such as dataloggers or more complex networked systems, to ensure collection preservation through collecting temperature, humidity, light and/or air quality measures. Existing systems are often costly, inflexible and do not use a modern, internet of things (IoT) approach. This paper aims to determine the suitability of currently popular general-purpose IoT devices, standards and technologies to the environmental monitoring needs of archivists, as well as identify any challenges. Design/methodology/approach This paper describes an exploratory study detailing the design, construction and usability testing of a do-it-yourself datalogger and data dashboard system, which seeks to manage previously identified trade-offs in cost, required technical skill and maintainability. Findings The environmental monitoring system presented met archivists’ needs well and was generally noted to be easy-to-use, efficient and an improvement on existing systems. This suggests that an IoT approach can support archivists’ needs in this area. Research limitations/implications Potential limitations of this study include lack of archival staff with sufficient technical training to maintain such a system and the rapid pace of IoT evolution yielding unstable and constantly changing technologies. Practical implications The system design presented in this work provides a blueprint for cultural heritage organizations desiring a fuller-featured, lower cost environmental monitoring system for archival collections. Originality/value This research takes a novel user-centered, open-source, IoT approach to construct an environmental monitoring system that is designed directly from archivists’ requirements and is extensible for future needs.


Author(s):  
Ifeoma V. Ngonadi

The Internet of Things (IoT) is a system of interrelated computing devices, mechanical and digital machines, objects, animals or people that are provided with unique identifiers and the ability to transfer data over a network without requiring human-to-human or human-to-computer interaction. Remote patient monitoring enables the monitoring of patients’ vital signs outside the conventional clinical settings which may increase access to care and decrease healthcare delivery costs. This paper focuses on implementing internet of things in a remote patient medical monitoring system. This was achieved by writing two computer applications in java in which one simulates a mobile phone called the Intelligent Personal Digital Assistant (IPDA) which uses a data structure that includes age, smoking habits and alcohol intake to simulate readings for blood pressure, pulse rate and mean arterial pressure continuously every twenty five which it sends to the server. The second java application protects the patients’ medical records as they travel through the networks by employing a symmetric key encryption algorithm which encrypts the patients’ medical records as they are generated and can only be decrypted in the server only by authorized personnel. The result of this research work is the implementation of internet of things in a remote patient medical monitoring system where patients’ vital signs are generated and transferred to the server continuously without human intervention.


2017 ◽  
Author(s):  
JOSEPH YIU

The increasing need for security in microcontrollers Security has long been a significant challenge in microcontroller applications(MCUs). Traditionally, many microcontroller systems did not have strong security measures against remote attacks as most of them are not connected to the Internet, and many microcontrollers are deemed to be cheap and simple. With the growth of IoT (Internet of Things), security in low cost microcontrollers moved toward the spotlight and the security requirements of these IoT devices are now just as critical as high-end systems due to:


Impact ◽  
2019 ◽  
Vol 2019 (10) ◽  
pp. 61-63 ◽  
Author(s):  
Akihiro Fujii

The Internet of Things (IoT) is a term that describes a system of computing devices, digital machines, objects, animals or people that are interrelated. Each of the interrelated 'things' are given a unique identifier and the ability to transfer data over a network that does not require human-to-human or human-to-computer interaction. Examples of IoT in practice include a human with a heart monitor implant, an animal with a biochip transponder (an electronic device inserted under the skin that gives the animal a unique identification number) and a car that has built-in sensors which can alert the driver about any problems, such as when the type pressure is low. The concept of a network of devices was established as early as 1982, although the term 'Internet of Things' was almost certainly first coined by Kevin Ashton in 1999. Since then, IoT devices have become ubiquitous, certainly in some parts of the world. Although there have been significant developments in the technology associated with IoT, the concept is far from being fully realised. Indeed, the potential for the reach of IoT extends to areas which some would find surprising. Researchers at the Faculty of Science and Engineering, Hosei University in Japan, are exploring using IoT in the agricultural sector, with some specific work on the production of melons. For the advancement of IoT in agriculture, difficult and important issues are implementation of subtle activities into computers procedure. The researchers challenges are going on.


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