Pub Date : 2020-09-16DOI: 10.1108/IJPCC-03-2020-0009
Benjula Anbu Malar Manickam Bernard, P. Jayagopal
Purpose This paper aims to discuss the Silver and Golden ticket exploits that usually exists in the existing systems. To overcome these challenges, the data is first encrypted and then the ticket is granted to the validated user. The users are validated using the user privileges. The security levels of the proposed model are compared with the existing models and provide a better performance using the Key Distribution Centre (KDC). The number of authentication and authorization levels present in the existing and proposed model is also evaluated. Design/methodology/approach The methodology designed in this paper is discussed in this section. The existing models are designed in such a way that the client ID first asked to send an authorization request to the Authentication Server. The server looks up the user in its database and then sends back a ticket generated by it to the client to obtain services for the Service center. Numerous models have some additional features to these systems where the theme of KDC was introduced. The Key Distribution Centre (KDC), which is a set of nodes in a network where the data could be distributed and stored, such that any kind of attack on a single KDC will not impact other KDC and the data stored in it. The nodes other than the KDC in the network are termed as the slave nodes. The slave nodes communicate with each other within the network depending on the topology of the entire network. In this paper, the authors have used the Kerberos protocol for adding more security functions in the entire network. The system developed consists of a client, server and a set of nodes connected to each other in a ring fashion. Findings The proposed model provides security to the information being used by making use of the Kerberos protocol. Additional features and algorithms such as the use of the ticket-granting approach have been added at the protocol to make it more secure than the existing models. The ticket generation is done at the server-side that makes the user have proper authentication to make use of the services available from the server-side. The model is designed in such a way that it could remain operational even during the time of denial of service. As future work, use of machine learning and deep learning could be used to predict the attack on the network well before it is being misused. Originality/value The paper discusses the Silver and Golden ticket exploits that usually exists in the existing systems. To overcome these challenges, the data is first encrypted and then the ticket is granted to the validated user. The users are validated using the user privileges. The security levels of the proposed model are compared with the existing models and provide a better performance using the Key Distribution Centre (KDC). The number of authentication and authorization levels present in the existing and proposed model is also evaluated.
{"title":"Trust based authentication scheme (tbas) for cloud computing environment with Kerberos protocol using distributed controller and prevention attack","authors":"Benjula Anbu Malar Manickam Bernard, P. Jayagopal","doi":"10.1108/IJPCC-03-2020-0009","DOIUrl":"https://doi.org/10.1108/IJPCC-03-2020-0009","url":null,"abstract":"\u0000Purpose\u0000This paper aims to discuss the Silver and Golden ticket exploits that usually exists in the existing systems. To overcome these challenges, the data is first encrypted and then the ticket is granted to the validated user. The users are validated using the user privileges. The security levels of the proposed model are compared with the existing models and provide a better performance using the Key Distribution Centre (KDC). The number of authentication and authorization levels present in the existing and proposed model is also evaluated.\u0000\u0000\u0000Design/methodology/approach\u0000The methodology designed in this paper is discussed in this section. The existing models are designed in such a way that the client ID first asked to send an authorization request to the Authentication Server. The server looks up the user in its database and then sends back a ticket generated by it to the client to obtain services for the Service center. Numerous models have some additional features to these systems where the theme of KDC was introduced. The Key Distribution Centre (KDC), which is a set of nodes in a network where the data could be distributed and stored, such that any kind of attack on a single KDC will not impact other KDC and the data stored in it. The nodes other than the KDC in the network are termed as the slave nodes. The slave nodes communicate with each other within the network depending on the topology of the entire network. In this paper, the authors have used the Kerberos protocol for adding more security functions in the entire network. The system developed consists of a client, server and a set of nodes connected to each other in a ring fashion.\u0000\u0000\u0000Findings\u0000The proposed model provides security to the information being used by making use of the Kerberos protocol. Additional features and algorithms such as the use of the ticket-granting approach have been added at the protocol to make it more secure than the existing models. The ticket generation is done at the server-side that makes the user have proper authentication to make use of the services available from the server-side. The model is designed in such a way that it could remain operational even during the time of denial of service. As future work, use of machine learning and deep learning could be used to predict the attack on the network well before it is being misused.\u0000\u0000\u0000Originality/value\u0000The paper discusses the Silver and Golden ticket exploits that usually exists in the existing systems. To overcome these challenges, the data is first encrypted and then the ticket is granted to the validated user. The users are validated using the user privileges. The security levels of the proposed model are compared with the existing models and provide a better performance using the Key Distribution Centre (KDC). The number of authentication and authorization levels present in the existing and proposed model is also evaluated.\u0000","PeriodicalId":210948,"journal":{"name":"Int. J. Pervasive Comput. Commun.","volume":"61 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126176388","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-09-15DOI: 10.1108/IJPCC-08-2020-0110
A. Khan, M. Chishti
Purpose The purpose of this study is to exploit the lowest common ancestor technique in an m-ary data aggregation tree in the fog computing-enhanced IoT to assist in contact tracing in COVID-19. One of the promising characteristics of the Internet of Things (IoT) that can be used to save the world from the current crisis of COVID-19 pandemic is data aggregation. As the number of patients infected by the disease is already huge, the data related to the different attributes of patients such as patient thermal image record and the previous health record of the patient is going to be gigantic. The authors used the technique of data aggregation to efficiently aggregate the sensed data from the patients and analyse it. Among the various inferences drawn from the aggregated data, one of the most important is contact tracing. Contact tracing in COVID-19 deals with finding out a person or a group of persons who have infected or were infected by the disease. Design/methodology/approach The authors propose to exploit the technique of lowest common ancestor in an m-ary data aggregation tree in the Fog-Computing enhanced IoT to help the health-care experts in contact tracing in a particular region or community. In this research, the authors argue the current scenario of COVID-19 pandemic, finding the person or a group of persons who has/have infected a group of people is of extreme importance. Finding the individuals who have been infected or are infecting others can stop the pandemic from worsening by stopping the community transfer. In a community where the outbreak has spiked, the samples from either all the persons or the patients showing the symptoms are collected and stored in an m-ary tree-based structure sorted over time. Findings Contact tracing in COVID-19 deals with finding out a person or a group of persons who have infected or were infected by the disease. The authors exploited the technique of lowest common ancestor in an m-ary data aggregation tree in the fog-computing-enhanced IoT to help the health-care experts in contact tracing in a particular region or community. The simulations were carried randomly on a set of individuals. The proposed algorithm given in Algorithm 1 is executed on the samples collected at level-0 of the simulation model, and to aggregate the data and transmit the data, the authors implement Algorithm 2 at the level-1. It is found from the results that a carrier can be easily identified from the samples collected using the approach designed in the paper. Practical implications The work presented in the paper can aid the health-care experts fighting the COVID-19 pandemic by reducing the community transfer with efficient contact tracing mechanism proposed in the paper. Social implications Fighting COVID-19 efficiently and saving the humans from the pandemic has huge social implications in the current times of crisis. Originality/value To the best of the authors’ knowledge, the lowest common ancestor technique in
{"title":"On contact tracing in COVID-19 (SARS-CoV-2) pandemic using lowest common ancestor in m-ary data aggregation tree in the fog-computing enhanced internet of things","authors":"A. Khan, M. Chishti","doi":"10.1108/IJPCC-08-2020-0110","DOIUrl":"https://doi.org/10.1108/IJPCC-08-2020-0110","url":null,"abstract":"\u0000Purpose\u0000The purpose of this study is to exploit the lowest common ancestor technique in an m-ary data aggregation tree in the fog computing-enhanced IoT to assist in contact tracing in COVID-19. One of the promising characteristics of the Internet of Things (IoT) that can be used to save the world from the current crisis of COVID-19 pandemic is data aggregation. As the number of patients infected by the disease is already huge, the data related to the different attributes of patients such as patient thermal image record and the previous health record of the patient is going to be gigantic. The authors used the technique of data aggregation to efficiently aggregate the sensed data from the patients and analyse it. Among the various inferences drawn from the aggregated data, one of the most important is contact tracing. Contact tracing in COVID-19 deals with finding out a person or a group of persons who have infected or were infected by the disease.\u0000\u0000\u0000Design/methodology/approach\u0000The authors propose to exploit the technique of lowest common ancestor in an m-ary data aggregation tree in the Fog-Computing enhanced IoT to help the health-care experts in contact tracing in a particular region or community. In this research, the authors argue the current scenario of COVID-19 pandemic, finding the person or a group of persons who has/have infected a group of people is of extreme importance. Finding the individuals who have been infected or are infecting others can stop the pandemic from worsening by stopping the community transfer. In a community where the outbreak has spiked, the samples from either all the persons or the patients showing the symptoms are collected and stored in an m-ary tree-based structure sorted over time.\u0000\u0000\u0000Findings\u0000Contact tracing in COVID-19 deals with finding out a person or a group of persons who have infected or were infected by the disease. The authors exploited the technique of lowest common ancestor in an m-ary data aggregation tree in the fog-computing-enhanced IoT to help the health-care experts in contact tracing in a particular region or community. The simulations were carried randomly on a set of individuals. The proposed algorithm given in Algorithm 1 is executed on the samples collected at level-0 of the simulation model, and to aggregate the data and transmit the data, the authors implement Algorithm 2 at the level-1. It is found from the results that a carrier can be easily identified from the samples collected using the approach designed in the paper.\u0000\u0000\u0000Practical implications\u0000The work presented in the paper can aid the health-care experts fighting the COVID-19 pandemic by reducing the community transfer with efficient contact tracing mechanism proposed in the paper.\u0000\u0000\u0000Social implications\u0000Fighting COVID-19 efficiently and saving the humans from the pandemic has huge social implications in the current times of crisis.\u0000\u0000\u0000Originality/value\u0000To the best of the authors’ knowledge, the lowest common ancestor technique in ","PeriodicalId":210948,"journal":{"name":"Int. J. Pervasive Comput. Commun.","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128213846","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-08-31DOI: 10.1108/ijpcc-07-2020-0088
Kumar K.R., I. M, Nivedithaa V.R, S. Magesh, G. Magesh, Shanmugasundaram Marappan
Purpose This paper has used the well-known machine learning (ML) computational algorithm with Internet of Things (IoT) devices to predict the COVID-19 disease and to analyze the peak rate of the disease in the world. ML is the best tool to analyze and predict the object in reasonable time with great level of accuracy. The Purpose of this paper is to develop a model to predict the coronavirus by considering majorly related symptoms, attributes and also to predict and analyze the peak rate of the disease. Design/methodology/approach COVID-19 or coronavirus disease threatens the human lives in various ways, which leads to deaths in most of the cases. It affects the respiratory organs slowly and this penetration leads to multiple organ failure, which causes death in some cases having poor immunity system. In recent times, it has drawn the international attention because of the pandemic threat that is harder to control the spreading of infection around the world. Findings This proposed model is implemented by support vector machine classifier and Bayesian network algorithm, which yields high accuracy. The K-means algorithm has been applied for clustering the data set models. For data collection, IoT devices and related sensors were used in the identified hotspots. The data sets were collected from the selected hotspots, which are placed on the regions selected by the government agencies. The proposed COVID-19 prediction models improve the accuracy of the prediction and peak accuracy ratio. This model is also tested with best, worst and average cases of data set to achieve the better prediction rate. Originality/value From that hotspots, the IoT devices were fixed and accessed through wireless sensors (802.11) to transfer the data to the authors’ database, which is dedicated in data collection server. The data set and the proposed model yield good results and perform well with expected accuracy rate in the analysis and monitoring of the recovery rate of COVID-19.
{"title":"Monitoring and analysis of the recovery rate of Covid-19 positive cases to prevent dangerous stage using IoT and sensors","authors":"Kumar K.R., I. M, Nivedithaa V.R, S. Magesh, G. Magesh, Shanmugasundaram Marappan","doi":"10.1108/ijpcc-07-2020-0088","DOIUrl":"https://doi.org/10.1108/ijpcc-07-2020-0088","url":null,"abstract":"\u0000Purpose\u0000This paper has used the well-known machine learning (ML) computational algorithm with Internet of Things (IoT) devices to predict the COVID-19 disease and to analyze the peak rate of the disease in the world. ML is the best tool to analyze and predict the object in reasonable time with great level of accuracy. The Purpose of this paper is to develop a model to predict the coronavirus by considering majorly related symptoms, attributes and also to predict and analyze the peak rate of the disease.\u0000\u0000\u0000Design/methodology/approach\u0000COVID-19 or coronavirus disease threatens the human lives in various ways, which leads to deaths in most of the cases. It affects the respiratory organs slowly and this penetration leads to multiple organ failure, which causes death in some cases having poor immunity system. In recent times, it has drawn the international attention because of the pandemic threat that is harder to control the spreading of infection around the world.\u0000\u0000\u0000Findings\u0000This proposed model is implemented by support vector machine classifier and Bayesian network algorithm, which yields high accuracy. The K-means algorithm has been applied for clustering the data set models. For data collection, IoT devices and related sensors were used in the identified hotspots. The data sets were collected from the selected hotspots, which are placed on the regions selected by the government agencies. The proposed COVID-19 prediction models improve the accuracy of the prediction and peak accuracy ratio. This model is also tested with best, worst and average cases of data set to achieve the better prediction rate.\u0000\u0000\u0000Originality/value\u0000From that hotspots, the IoT devices were fixed and accessed through wireless sensors (802.11) to transfer the data to the authors’ database, which is dedicated in data collection server. The data set and the proposed model yield good results and perform well with expected accuracy rate in the analysis and monitoring of the recovery rate of COVID-19.\u0000","PeriodicalId":210948,"journal":{"name":"Int. J. Pervasive Comput. Commun.","volume":"439 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132942138","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-08-31DOI: 10.1108/ijpcc-07-2020-0091
Sharnil Pandya, Anirban Sur, K. Kotecha
Purpose The purpose of the presented IoT based sensor-fusion assistive technology for COVID-19 disinfection termed as “Smart epidemic tunnel” is to protect an individual using an automatic sanitizer spray system equipped with a sanitizer sensing unit based on individual using an automatic sanitizer spray system equipped with a sanitizer sensing unit based on human motion detection. Design/methodology/approach The presented research work discusses a smart epidemic tunnel that can assist an individual in immediate disinfection from COVID-19 infections. The authors have presented a sensor-fusion-based automatic sanitizer tunnel that detects a human using an ultrasonic sensor from the height of 1.5 feet and disinfects him/her using the spread of a sanitizer spray. The presented smart tunnel operates using a solar cell during the day time and switched to a solar power-bank power mode during night timings using a light-dependent register sensing unit. Findings The investigation results validate the performance evaluation of the presented smart epidemic tunnel mechanism. The presented smart tunnel can prevent or disinfect an outsider who is entering a particular building or a premise from COVID-19 infection possibilities. Furthermore, it has also been observed that the presented sensor-fusion-based mechanism can disinfect a person in a time of span of just 10 s. The presented smart epidemic tunnel is embedded with an intelligent sanitizer sensing unit which stores the essential information in a cloud platform such as Google Fire-base. Thus, the proposed system favours society by saving time and helps in lowering the spread of coronavirus. It also provides daily, weekly and monthly reports of the counts of individuals, along with in-out timestamps and power usage reports. Practical implications The presented system has been designed and developed after the lock-down period to disinfect an individual from the possibility of COVID-19 infections. Social implications The presented smart epidemic tunnel reduced the possibility by disinfecting an outside individual/COVID-19 suspect from spreading the COVID-19 infections in a particular building or a premise. Originality/value The presented system is an original work done by all the authors which have been installed at the Symbiosis Institute of Technology premise and have undergone rigorous experimentation and testing by the authors and end-users.
{"title":"Smart epidemic tunnel: IoT-based sensor-fusion assistive technology for COVID-19 disinfection","authors":"Sharnil Pandya, Anirban Sur, K. Kotecha","doi":"10.1108/ijpcc-07-2020-0091","DOIUrl":"https://doi.org/10.1108/ijpcc-07-2020-0091","url":null,"abstract":"\u0000Purpose\u0000The purpose of the presented IoT based sensor-fusion assistive technology for COVID-19 disinfection termed as “Smart epidemic tunnel” is to protect an individual using an automatic sanitizer spray system equipped with a sanitizer sensing unit based on individual using an automatic sanitizer spray system equipped with a sanitizer sensing unit based on human motion detection.\u0000\u0000\u0000Design/methodology/approach\u0000The presented research work discusses a smart epidemic tunnel that can assist an individual in immediate disinfection from COVID-19 infections. The authors have presented a sensor-fusion-based automatic sanitizer tunnel that detects a human using an ultrasonic sensor from the height of 1.5 feet and disinfects him/her using the spread of a sanitizer spray. The presented smart tunnel operates using a solar cell during the day time and switched to a solar power-bank power mode during night timings using a light-dependent register sensing unit.\u0000\u0000\u0000Findings\u0000The investigation results validate the performance evaluation of the presented smart epidemic tunnel mechanism. The presented smart tunnel can prevent or disinfect an outsider who is entering a particular building or a premise from COVID-19 infection possibilities. Furthermore, it has also been observed that the presented sensor-fusion-based mechanism can disinfect a person in a time of span of just 10 s. The presented smart epidemic tunnel is embedded with an intelligent sanitizer sensing unit which stores the essential information in a cloud platform such as Google Fire-base. Thus, the proposed system favours society by saving time and helps in lowering the spread of coronavirus. It also provides daily, weekly and monthly reports of the counts of individuals, along with in-out timestamps and power usage reports.\u0000\u0000\u0000Practical implications\u0000The presented system has been designed and developed after the lock-down period to disinfect an individual from the possibility of COVID-19 infections.\u0000\u0000\u0000Social implications\u0000The presented smart epidemic tunnel reduced the possibility by disinfecting an outside individual/COVID-19 suspect from spreading the COVID-19 infections in a particular building or a premise.\u0000\u0000\u0000Originality/value\u0000The presented system is an original work done by all the authors which have been installed at the Symbiosis Institute of Technology premise and have undergone rigorous experimentation and testing by the authors and end-users.\u0000","PeriodicalId":210948,"journal":{"name":"Int. J. Pervasive Comput. Commun.","volume":"100 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128126556","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-08-28DOI: 10.1108/ijpcc-07-2020-0086
S. Prabu, B. Velan, S. C. Nelson, V. JayasudhaF., P. Visu, K. Janarthanan
Purpose The purpose of this paper is to review the techniques for versatile advancements in contact tracing for the coronavirus disease 2019 (COVID-19) positive cases in this pandemic and to introduce the way of using the mobile location information collected within the country India. As the method, an exploratory review of current measures was conducted for confirmed COVID-19 contact tracing after understanding the current situation of the world. This paper has examined the way of using free locational information in an innovative way to reduce the spread of COVID-19 spread. Design/methodology/approach COVID-19 pandemic is the utmost global economic and health challenge of the century. One powerful and consistent procedure to slow down the spread and decrease the effect of COVID-19 is to track the essential and auxiliary contacts of confirmed COVID-19 positive cases by using contact-tracing innovation. Findings Although it takes the information from various clients, there are numerous odds in the information. The sincere measures were taken by the authors to avoid the abuse of information by any kind. A portion of the tips for keeping information from getting abused is on the whole, the information ought to be with just higher specialists, and they ought not to have the authorization to impart information to anybody. Originality/value This paper helps to track the COVID-19 positive cases as of now by using the field information assortment and outbreak examination stages. At the same time, mobile location information used inside the current guideline, rules for information handlers must incorporate measures to reduce the abusing of information.
{"title":"Mobile technologies for contact tracing and prevention of COVID-19 positive cases: a cross- sectional study","authors":"S. Prabu, B. Velan, S. C. Nelson, V. JayasudhaF., P. Visu, K. Janarthanan","doi":"10.1108/ijpcc-07-2020-0086","DOIUrl":"https://doi.org/10.1108/ijpcc-07-2020-0086","url":null,"abstract":"\u0000Purpose\u0000The purpose of this paper is to review the techniques for versatile advancements in contact tracing for the coronavirus disease 2019 (COVID-19) positive cases in this pandemic and to introduce the way of using the mobile location information collected within the country India. As the method, an exploratory review of current measures was conducted for confirmed COVID-19 contact tracing after understanding the current situation of the world. This paper has examined the way of using free locational information in an innovative way to reduce the spread of COVID-19 spread.\u0000\u0000\u0000Design/methodology/approach\u0000COVID-19 pandemic is the utmost global economic and health challenge of the century. One powerful and consistent procedure to slow down the spread and decrease the effect of COVID-19 is to track the essential and auxiliary contacts of confirmed COVID-19 positive cases by using contact-tracing innovation.\u0000\u0000\u0000Findings\u0000Although it takes the information from various clients, there are numerous odds in the information. The sincere measures were taken by the authors to avoid the abuse of information by any kind. A portion of the tips for keeping information from getting abused is on the whole, the information ought to be with just higher specialists, and they ought not to have the authorization to impart information to anybody.\u0000\u0000\u0000Originality/value\u0000This paper helps to track the COVID-19 positive cases as of now by using the field information assortment and outbreak examination stages. At the same time, mobile location information used inside the current guideline, rules for information handlers must incorporate measures to reduce the abusing of information.\u0000","PeriodicalId":210948,"journal":{"name":"Int. J. Pervasive Comput. Commun.","volume":"106 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116919475","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-08-13DOI: 10.1108/ijpcc-07-2020-0083
Divya Madnani, Semila Fernandes, Nidhi Madnani
The outbreak of COVID-19 saw a robust increase in viewership of over-the-top (OTT) media platforms. This study aims to investigate the impact of COVID-19 on OTT platforms in India, as it has led to reshaping consumer content preferences.,The authors have conducted primary research by doing a survey and focus group discussion. The first study has focused on the impact of various factors such as time, content, convenience, satisfaction and work from home (WFH) on OTT platforms during the COVID-19 crisis and the second study has focused on change in behavior of people before and during lockdown using visual representation.,The findings of this study show that lockdown has played a major role in the increase in viewership of OTT platforms, as people working from home are also using OTT platforms more. The average hours spent on OTT have increased from 0–2 to 2–5 h and average spending that users are willing to make on OTT platforms is Rs 100–300 (per month). The satisfaction level of customers is directly related to space to watch with family, time to use OTT platforms, the quality of content on OTT platforms and preference of OTT platform over television. Also, factors such as age group, occupation, city and income groups also determine the usage of the OTT platform.,The main contribution of this paper is to analyze the customer needs that impact their satisfaction level.
{"title":"Analysing the impact of COVID-19 on over-the-top media platforms in India","authors":"Divya Madnani, Semila Fernandes, Nidhi Madnani","doi":"10.1108/ijpcc-07-2020-0083","DOIUrl":"https://doi.org/10.1108/ijpcc-07-2020-0083","url":null,"abstract":"The outbreak of COVID-19 saw a robust increase in viewership of over-the-top (OTT) media platforms. This study aims to investigate the impact of COVID-19 on OTT platforms in India, as it has led to reshaping consumer content preferences.,The authors have conducted primary research by doing a survey and focus group discussion. The first study has focused on the impact of various factors such as time, content, convenience, satisfaction and work from home (WFH) on OTT platforms during the COVID-19 crisis and the second study has focused on change in behavior of people before and during lockdown using visual representation.,The findings of this study show that lockdown has played a major role in the increase in viewership of OTT platforms, as people working from home are also using OTT platforms more. The average hours spent on OTT have increased from 0–2 to 2–5 h and average spending that users are willing to make on OTT platforms is Rs 100–300 (per month). The satisfaction level of customers is directly related to space to watch with family, time to use OTT platforms, the quality of content on OTT platforms and preference of OTT platform over television. Also, factors such as age group, occupation, city and income groups also determine the usage of the OTT platform.,The main contribution of this paper is to analyze the customer needs that impact their satisfaction level.","PeriodicalId":210948,"journal":{"name":"Int. J. Pervasive Comput. Commun.","volume":"72 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126257686","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-08-12DOI: 10.1108/ijpcc-07-2020-0087
M. Sbai, Hajer Taktak, Faouzi Moussa
Purpose In view of the intensive spread of Coronavirus disease 2019 (COVID-19) and in order to reduce the rate of spread of this disease; the objective of this article is to propose an approach to detect in real time suspect person of Coronavirus disease 2019 (COVID-19). Design/methodology/approach The ubiquitous computing offers a new opportunity to reshape the form of conventional solutions for personalized services according to the contextual situations of each environment. The health system is seen as a key part of ubiquitous computing, which means that health services are available anytime, anywhere to monitor patients based on their context. This paper aims to design and validate a contextual model for ubiquitous health systems designed to detect in real time suspect person of COVID-19, to reduce the propagation of this infectious disease and to take the necessary instructions. Findings This paper presents the performance results of the COVID-19 detection approach. Thus, the reduction of the COVID-19 propagation rate thanks to the real-time intervention of the system. Originality/value Following the COVID-19 pandemic spread, the authors tried to find a solution to detect the disease in real time. In this paper, a real-time COVID-19 detection system based on the ontological description supported by Semantic Web Rule Language (SWRL) rules was developed. The proposed ontology contains all relevant concepts related to COVID-19, including personal information, location, symptoms, risk factors, laboratory test results and treatment planning. The SWRL rules are constructed from medical recommendations.
{"title":"Towards a ubiquitous real-time COVID-19 detection system","authors":"M. Sbai, Hajer Taktak, Faouzi Moussa","doi":"10.1108/ijpcc-07-2020-0087","DOIUrl":"https://doi.org/10.1108/ijpcc-07-2020-0087","url":null,"abstract":"\u0000Purpose\u0000In view of the intensive spread of Coronavirus disease 2019 (COVID-19) and in order to reduce the rate of spread of this disease; the objective of this article is to propose an approach to detect in real time suspect person of Coronavirus disease 2019 (COVID-19).\u0000\u0000\u0000Design/methodology/approach\u0000The ubiquitous computing offers a new opportunity to reshape the form of conventional solutions for personalized services according to the contextual situations of each environment. The health system is seen as a key part of ubiquitous computing, which means that health services are available anytime, anywhere to monitor patients based on their context. This paper aims to design and validate a contextual model for ubiquitous health systems designed to detect in real time suspect person of COVID-19, to reduce the propagation of this infectious disease and to take the necessary instructions.\u0000\u0000\u0000Findings\u0000This paper presents the performance results of the COVID-19 detection approach. Thus, the reduction of the COVID-19 propagation rate thanks to the real-time intervention of the system.\u0000\u0000\u0000Originality/value\u0000Following the COVID-19 pandemic spread, the authors tried to find a solution to detect the disease in real time. In this paper, a real-time COVID-19 detection system based on the ontological description supported by Semantic Web Rule Language (SWRL) rules was developed. The proposed ontology contains all relevant concepts related to COVID-19, including personal information, location, symptoms, risk factors, laboratory test results and treatment planning. The SWRL rules are constructed from medical recommendations.\u0000","PeriodicalId":210948,"journal":{"name":"Int. J. Pervasive Comput. Commun.","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115296119","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-08-06DOI: 10.1108/ijpcc-07-2020-0081
Mark Ryan
Purpose The media has even been very critical of some East Asian countries’ use of digital contact-tracing to control Covid-19. For example, South Korea has been criticised for its use of privacy-infringing digital contact-tracing. However, whether their type of digital contact-tracing was unnecessarily harmful to the human rights of Korean citizens is open for debate. The purpose of this paper is to examine this criticism to see if Korea’s digital contact-tracing is ethically justifiable. Design/methodology/approach This paper will evaluate Korea’s digital contact-tracing through the lens of the four human rights principles to determine if their response is ethically justifiable. These four principles were originally outlined in the European Court of Human Rights, namely, necessary, proportional, scientifically valid and time-bounded (European Court of Human Rights 1950). Findings The paper will propose that while the use of Korea’s digital contact-tracing was scientifically valid and proportionate (albeit, in need for improvements), it meets the necessity requirement, but is too vague to meet the time-boundedness requirement. Originality/value The Covid-19 pandemic has proven to be one of the worst threats to human health and the global economy in the past century. There have been many different strategies to tackle the pandemic, from somewhat laissez-faire approaches, herd immunity, to strict draconian measures. Analysis of the approaches taken in the response to the pandemic is of high scientific value and this paper is one of the first to critically engage with one of these methods – digital contact-tracing in South Korea.
{"title":"In defence of digital contact-tracing: human rights, South Korea and Covid-19","authors":"Mark Ryan","doi":"10.1108/ijpcc-07-2020-0081","DOIUrl":"https://doi.org/10.1108/ijpcc-07-2020-0081","url":null,"abstract":"\u0000Purpose\u0000The media has even been very critical of some East Asian countries’ use of digital contact-tracing to control Covid-19. For example, South Korea has been criticised for its use of privacy-infringing digital contact-tracing. However, whether their type of digital contact-tracing was unnecessarily harmful to the human rights of Korean citizens is open for debate. The purpose of this paper is to examine this criticism to see if Korea’s digital contact-tracing is ethically justifiable.\u0000\u0000\u0000Design/methodology/approach\u0000This paper will evaluate Korea’s digital contact-tracing through the lens of the four human rights principles to determine if their response is ethically justifiable. These four principles were originally outlined in the European Court of Human Rights, namely, necessary, proportional, scientifically valid and time-bounded (European Court of Human Rights 1950).\u0000\u0000\u0000Findings\u0000The paper will propose that while the use of Korea’s digital contact-tracing was scientifically valid and proportionate (albeit, in need for improvements), it meets the necessity requirement, but is too vague to meet the time-boundedness requirement.\u0000\u0000\u0000Originality/value\u0000The Covid-19 pandemic has proven to be one of the worst threats to human health and the global economy in the past century. There have been many different strategies to tackle the pandemic, from somewhat laissez-faire approaches, herd immunity, to strict draconian measures. Analysis of the approaches taken in the response to the pandemic is of high scientific value and this paper is one of the first to critically engage with one of these methods – digital contact-tracing in South Korea.\u0000","PeriodicalId":210948,"journal":{"name":"Int. J. Pervasive Comput. Commun.","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126998221","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-08-06DOI: 10.1108/ijpcc-07-2020-0082
S. Magesh, R. NivedithaV., S. RajakumarP., S. RadhaRamMohan, L. Natrayan
Purpose The current and on-going coronavirus (COVID-19) has disrupted many human lives all over the world and seems very difficult to confront this global crisis as the infection is transmitted by physical contact. As no vaccine or medical treatment made available till date, the only solution is to detect the COVID-19 cases, block the transmission, isolate the infected and protect the susceptible population. In this scenario, the pervasive computing becomes essential, as it is environment-centric and data acquisition via smart devices provides better way for analysing diseases with various parameters. Design/methodology/approach For data collection, Infrared Thermometer, Hikvision’s Thermographic Camera and Acoustic device are deployed. Data-imputation is carried out by principal component analysis. A mathematical model susceptible, infected and recovered (SIR) is implemented for classifying COVID-19 cases. The recurrent neural network (RNN) with long-term short memory is enacted to predict the COVID-19 disease. Findings Machine learning models are very efficient in predicting diseases. In the proposed research work, besides contribution of smart devices, Artificial Intelligence detector is deployed to reduce false alarms. A mathematical model SIR is integrated with machine learning techniques for better classification. Implementation of RNN with Long Short Term Memory (LSTM) model furnishes better prediction holding the previous history. Originality/value The proposed research collected COVID −19 data using three types of sensors for temperature sensing and detecting the respiratory rate. After pre-processing, 300 instances are taken for experimental results considering the demographic features: Sex, Patient Age, Temperature, Finding and Clinical Trials. Classification is performed using SIR mode and finally predicted 188 confirmed cases using RNN with LSTM model.
{"title":"Pervasive computing in the context of COVID-19 prediction with AI-based algorithms","authors":"S. Magesh, R. NivedithaV., S. RajakumarP., S. RadhaRamMohan, L. Natrayan","doi":"10.1108/ijpcc-07-2020-0082","DOIUrl":"https://doi.org/10.1108/ijpcc-07-2020-0082","url":null,"abstract":"\u0000Purpose\u0000The current and on-going coronavirus (COVID-19) has disrupted many human lives all over the world and seems very difficult to confront this global crisis as the infection is transmitted by physical contact. As no vaccine or medical treatment made available till date, the only solution is to detect the COVID-19 cases, block the transmission, isolate the infected and protect the susceptible population. In this scenario, the pervasive computing becomes essential, as it is environment-centric and data acquisition via smart devices provides better way for analysing diseases with various parameters.\u0000\u0000\u0000Design/methodology/approach\u0000For data collection, Infrared Thermometer, Hikvision’s Thermographic Camera and Acoustic device are deployed. Data-imputation is carried out by principal component analysis. A mathematical model susceptible, infected and recovered (SIR) is implemented for classifying COVID-19 cases. The recurrent neural network (RNN) with long-term short memory is enacted to predict the COVID-19 disease.\u0000\u0000\u0000Findings\u0000Machine learning models are very efficient in predicting diseases. In the proposed research work, besides contribution of smart devices, Artificial Intelligence detector is deployed to reduce false alarms. A mathematical model SIR is integrated with machine learning techniques for better classification. Implementation of RNN with Long Short Term Memory (LSTM) model furnishes better prediction holding the previous history.\u0000\u0000\u0000Originality/value\u0000The proposed research collected COVID −19 data using three types of sensors for temperature sensing and detecting the respiratory rate. After pre-processing, 300 instances are taken for experimental results considering the demographic features: Sex, Patient Age, Temperature, Finding and Clinical Trials. Classification is performed using SIR mode and finally predicted 188 confirmed cases using RNN with LSTM model.\u0000","PeriodicalId":210948,"journal":{"name":"Int. J. Pervasive Comput. Commun.","volume":"149 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116584757","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-08-05DOI: 10.1108/ijpcc-06-2020-0061
S.Veera pandi, H. PradeepReddyC.
Inclusion of mobile nodes (MNs) in Internet of Things (IoT) further increases the challenges such as frequent network disconnection and intermittent connectivity because of high mobility rate of nodes. This paper aims to propose a proactive mobility and congestion aware route prediction mechanism (PMCAR) to find the congestion free route from leaf to destination oriented directed acyclic graph root (DODAG-ROOT) which considers number of MNs connected to a static node. This paper compares the proposed technique (PMCAR) with RPL (OF0) which considers the HOP-COUNT to determine the path from leaf to DODAG-ROOT. The authors performed a simulation with the proposed technique in MATLAB to present the benefits in terms of packet loss and energy consumption.,In this pandemic situation, mobile and IoT play major role in predicting and preventing the CoronaVirus Disease of 2019 (COVID-19). Huge amount of computations is happening with the data generated in this pandemic with the help of mobile devices. To route the data to remote locations through the network, it is necessary to have proper routing mechanism without congestion. In this paper, PMCAR mechanism is introduced to achieve the same. Internet of mobile Things (IoMT) is an extension of IoT that consists of static embedded devices and sensors. IoMT includes MNs which sense data and transfer it to the DODAG-ROOT. The nodes in the IoMT are characterised by low power, low memory, low computing power and low bandwidth support. Several challenges are encountered by routing protocols defined for IPV6 over low power wireless personal area networks to ensure reduced packet loss, less delay, less energy consumption and guaranteed quality of service.,The results obtained shows a significant improvement compared to the existing approach such as RPL (OF0). The proposed route prediction mechanism can be applied largely to medical applications which are delay sensitive, particularly in pandemic situations where the number of patients involved and the data gathered from them flows towards a central root for analysis. Support of data transmission from the patients to the doctors without much delay and packet loss will make the response or decisions available more quickly which is a vital part of medical applications.,The computational technologies in this COVID-19 pandemic situation needs timely data for computation without delay. IoMT is enabled with various devices such as mobile, sensors and wearable devices. These devices are dedicated for collecting the data from the patients or any objects from different geographical location based on the predetermined time intervals. Timely delivery of data is essential for accurate computation. So, it is necessary to have a routing mechanism without delay and congestion to handle this pandemic situation. The proposed PMCAR mechanism ensures the reliable delivery of data for immediate computation which can be used to make decisions in preventing and prediction.
{"title":"PMCAR: proactive mobility and congestion aware route prediction mechanism in IoMT for delay sensitive medical applications to ensure reliability in COVID-19 pandemic situation","authors":"S.Veera pandi, H. PradeepReddyC.","doi":"10.1108/ijpcc-06-2020-0061","DOIUrl":"https://doi.org/10.1108/ijpcc-06-2020-0061","url":null,"abstract":"Inclusion of mobile nodes (MNs) in Internet of Things (IoT) further increases the challenges such as frequent network disconnection and intermittent connectivity because of high mobility rate of nodes. This paper aims to propose a proactive mobility and congestion aware route prediction mechanism (PMCAR) to find the congestion free route from leaf to destination oriented directed acyclic graph root (DODAG-ROOT) which considers number of MNs connected to a static node. This paper compares the proposed technique (PMCAR) with RPL (OF0) which considers the HOP-COUNT to determine the path from leaf to DODAG-ROOT. The authors performed a simulation with the proposed technique in MATLAB to present the benefits in terms of packet loss and energy consumption.,In this pandemic situation, mobile and IoT play major role in predicting and preventing the CoronaVirus Disease of 2019 (COVID-19). Huge amount of computations is happening with the data generated in this pandemic with the help of mobile devices. To route the data to remote locations through the network, it is necessary to have proper routing mechanism without congestion. In this paper, PMCAR mechanism is introduced to achieve the same. Internet of mobile Things (IoMT) is an extension of IoT that consists of static embedded devices and sensors. IoMT includes MNs which sense data and transfer it to the DODAG-ROOT. The nodes in the IoMT are characterised by low power, low memory, low computing power and low bandwidth support. Several challenges are encountered by routing protocols defined for IPV6 over low power wireless personal area networks to ensure reduced packet loss, less delay, less energy consumption and guaranteed quality of service.,The results obtained shows a significant improvement compared to the existing approach such as RPL (OF0). The proposed route prediction mechanism can be applied largely to medical applications which are delay sensitive, particularly in pandemic situations where the number of patients involved and the data gathered from them flows towards a central root for analysis. Support of data transmission from the patients to the doctors without much delay and packet loss will make the response or decisions available more quickly which is a vital part of medical applications.,The computational technologies in this COVID-19 pandemic situation needs timely data for computation without delay. IoMT is enabled with various devices such as mobile, sensors and wearable devices. These devices are dedicated for collecting the data from the patients or any objects from different geographical location based on the predetermined time intervals. Timely delivery of data is essential for accurate computation. So, it is necessary to have a routing mechanism without delay and congestion to handle this pandemic situation. The proposed PMCAR mechanism ensures the reliable delivery of data for immediate computation which can be used to make decisions in preventing and prediction.","PeriodicalId":210948,"journal":{"name":"Int. J. Pervasive Comput. Commun.","volume":"91 3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121503318","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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