Pub Date : 2021-05-10DOI: 10.1109/AIIoT52608.2021.9454222
Douglas Ellman, Pratiksha Shukla, Yuanzhang Xiao, M. Iskander, Kevin L. Davies
Increasing use of renewable and distributed power generation creates opportunities for customer resources to support power system operations by adjusting power consumption and generation to address grid needs, based on system-wide and local grid conditions. We present an integrated Energy Internet of Things (E-IoT) testbed including (1) distributed Advanced Realtime Grid Energy Monitor Systems (ARGEMS) with sensing, communication, and control capabilities, (2) distributed smart home sites, including smart home hubs for monitoring and control of physical and simulated Internet of Things (IoT) distributed energy resources (DERs) such as solar systems, home batteries, and smart appliances, and (3) control algorithms based on artificial intelligence and optimization, which manage customer DERs to respond to power grid conditions while serving customer needs. The integration of these three components enables demonstration and assessment of a variety of advanced DER monitoring and control strategies for improved power grid operations and customer benefits. We validate the functionality of this E- IoT testbed by demonstrating control of a simulated home battery by a neural network imitation learning algorithm running on a physical smart home hub, where the controller responds to grid services events triggered by an ARGEMS device based on local power system measurements and simulated bulk power system conditions. The developed neural network controller imitates the performance of a model predictive control optimization algorithm, but requires nearly 20,000 times less computational time and can run on small distributed computers.
{"title":"Integrated Energy Monitoring and Control IoT System and Validation Results from Neural Network Control Demonstration","authors":"Douglas Ellman, Pratiksha Shukla, Yuanzhang Xiao, M. Iskander, Kevin L. Davies","doi":"10.1109/AIIoT52608.2021.9454222","DOIUrl":"https://doi.org/10.1109/AIIoT52608.2021.9454222","url":null,"abstract":"Increasing use of renewable and distributed power generation creates opportunities for customer resources to support power system operations by adjusting power consumption and generation to address grid needs, based on system-wide and local grid conditions. We present an integrated Energy Internet of Things (E-IoT) testbed including (1) distributed Advanced Realtime Grid Energy Monitor Systems (ARGEMS) with sensing, communication, and control capabilities, (2) distributed smart home sites, including smart home hubs for monitoring and control of physical and simulated Internet of Things (IoT) distributed energy resources (DERs) such as solar systems, home batteries, and smart appliances, and (3) control algorithms based on artificial intelligence and optimization, which manage customer DERs to respond to power grid conditions while serving customer needs. The integration of these three components enables demonstration and assessment of a variety of advanced DER monitoring and control strategies for improved power grid operations and customer benefits. We validate the functionality of this E- IoT testbed by demonstrating control of a simulated home battery by a neural network imitation learning algorithm running on a physical smart home hub, where the controller responds to grid services events triggered by an ARGEMS device based on local power system measurements and simulated bulk power system conditions. The developed neural network controller imitates the performance of a model predictive control optimization algorithm, but requires nearly 20,000 times less computational time and can run on small distributed computers.","PeriodicalId":443405,"journal":{"name":"2021 IEEE World AI IoT Congress (AIIoT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124278469","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 : 2021-05-10DOI: 10.1109/AIIoT52608.2021.9454211
Beatriz Dias, A. Glória, P. Sebastião
This paper introduces a study done to evaluate the use of machine learning regression techniques to predict the link quality of communications done by IoT nodes. The proposed methodology is able to predict the link quality of the most typical cloud communication protocols, such as cellular, Wi-Fi, SigFox and LoRaWAN, based on the node location. To discover the best model to achieve this, a set of machine learning techniques were implemented, including Linear Regression, Decision Tree, Random Forest and Neural Networks, being the results compared. Results showed that Decisions Trees achieve the best efficiency, with a margin of error of 7.172 dBm, after cross-validation. This paper includes a detailed description of the methodology, its implementation and the experimental results.
{"title":"Prediction of Link Quality for IoT Cloud Communications supported by Machine Learning","authors":"Beatriz Dias, A. Glória, P. Sebastião","doi":"10.1109/AIIoT52608.2021.9454211","DOIUrl":"https://doi.org/10.1109/AIIoT52608.2021.9454211","url":null,"abstract":"This paper introduces a study done to evaluate the use of machine learning regression techniques to predict the link quality of communications done by IoT nodes. The proposed methodology is able to predict the link quality of the most typical cloud communication protocols, such as cellular, Wi-Fi, SigFox and LoRaWAN, based on the node location. To discover the best model to achieve this, a set of machine learning techniques were implemented, including Linear Regression, Decision Tree, Random Forest and Neural Networks, being the results compared. Results showed that Decisions Trees achieve the best efficiency, with a margin of error of 7.172 dBm, after cross-validation. This paper includes a detailed description of the methodology, its implementation and the experimental results.","PeriodicalId":443405,"journal":{"name":"2021 IEEE World AI IoT Congress (AIIoT)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134237037","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 : 2021-05-10DOI: 10.1109/AIIoT52608.2021.9454246
Yoshemart Amador-Salgado, J. Padilla-Medina, F. Pérez-Pinal, A. Barranco-Gutiérrez, M. Rodríguez-Licea, Juan J. Martinez-Nolasco
Security and safeness are constant topics to be solved in cities around the world, finding ways to detect weapons threatening human beings is an important challenge. This paper presents an approach for solving knife detection in Close Circuit Television (CCTV) cameras videos. In this sense, knife detection is the goal in this work and through a combination of color and invariant moments techniques, the system presented reaches the objective and turns reliable in indoor environments, with or without environmental illumination. The work has been proved under white and infrared lighting, with a range between 0.5 to 4.0 meters of distance from camera to knives with positive qualities of detection. Supporting this work, videos were uploaded on web. The reported error in them runs from 0% to 1.192%. This system may be useful at convenience stores, banks, theatres and some others public places with commercial surveillance cameras from a relatively long distance. Results offer a simple classification because to important features found. For instance, when the system may be executed by parallel processors or in pipeline method it could be detecting more than one knife on scene.
{"title":"Knife Detection using Indoor Surveillance Camera","authors":"Yoshemart Amador-Salgado, J. Padilla-Medina, F. Pérez-Pinal, A. Barranco-Gutiérrez, M. Rodríguez-Licea, Juan J. Martinez-Nolasco","doi":"10.1109/AIIoT52608.2021.9454246","DOIUrl":"https://doi.org/10.1109/AIIoT52608.2021.9454246","url":null,"abstract":"Security and safeness are constant topics to be solved in cities around the world, finding ways to detect weapons threatening human beings is an important challenge. This paper presents an approach for solving knife detection in Close Circuit Television (CCTV) cameras videos. In this sense, knife detection is the goal in this work and through a combination of color and invariant moments techniques, the system presented reaches the objective and turns reliable in indoor environments, with or without environmental illumination. The work has been proved under white and infrared lighting, with a range between 0.5 to 4.0 meters of distance from camera to knives with positive qualities of detection. Supporting this work, videos were uploaded on web. The reported error in them runs from 0% to 1.192%. This system may be useful at convenience stores, banks, theatres and some others public places with commercial surveillance cameras from a relatively long distance. Results offer a simple classification because to important features found. For instance, when the system may be executed by parallel processors or in pipeline method it could be detecting more than one knife on scene.","PeriodicalId":443405,"journal":{"name":"2021 IEEE World AI IoT Congress (AIIoT)","volume":"88 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124745745","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 : 2021-05-10DOI: 10.1109/AIIoT52608.2021.9454224
Zexi Xing, Zhengxin Chen
In this research we focus on how to prevent Double Spending Attack (also called 51 % hash rate attack), a particular security issue related to blockchain technology in the current cryptocurrency world. We describe the main idea of our proposed Block Access Restriction (BAR) mechanism, which controls the actual block requests and detects malicious behaviors while transactions have been recorded into a specific block, to protect general miner's privileges and provide fairness in the blockchain network environment. We propose an effective way to prevent this to happen (with detailed steps), discuss how to deploy BAR switch into blockchain networks and how the BAR switch can prevent DSA while the hacker bypasses it. We also present general idea of implementing BAR switch, and point out the importance of dealing with security threat at post-quantum computing era.
{"title":"A Protecting Mechanism Against Double Spending Attack in Blockchain Systems","authors":"Zexi Xing, Zhengxin Chen","doi":"10.1109/AIIoT52608.2021.9454224","DOIUrl":"https://doi.org/10.1109/AIIoT52608.2021.9454224","url":null,"abstract":"In this research we focus on how to prevent Double Spending Attack (also called 51 % hash rate attack), a particular security issue related to blockchain technology in the current cryptocurrency world. We describe the main idea of our proposed Block Access Restriction (BAR) mechanism, which controls the actual block requests and detects malicious behaviors while transactions have been recorded into a specific block, to protect general miner's privileges and provide fairness in the blockchain network environment. We propose an effective way to prevent this to happen (with detailed steps), discuss how to deploy BAR switch into blockchain networks and how the BAR switch can prevent DSA while the hacker bypasses it. We also present general idea of implementing BAR switch, and point out the importance of dealing with security threat at post-quantum computing era.","PeriodicalId":443405,"journal":{"name":"2021 IEEE World AI IoT Congress (AIIoT)","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124841527","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 : 2021-05-10DOI: 10.1109/AIIoT52608.2021.9454195
Maha Kanan Almutairi, Shameek Bhattacharjee
There has been a huge growth in the number of mobile devices in the last few years. These mobile devices are equipped with various hardware that allows them to sense and receive different types of wireless signals such as Global Positioning System (GPS), Wi-Fi, Bluetooth, and others. Today, a mobile device is capable of obtaining its location in various ways. Location-enabled mobile devices enable applications to utilize and verify users' location to allow them to access location-based resources, which is known as Location-based access control (LBAC). The implementation of LBAC has grown rapidly with the increase in the number of mobile devices and location-based services (LBS) and is expected to grow more in the future. LBAC verifies the user's location is required. User location can be obtained using different localization techniques. In this paper, we survey the localization techniques used in LBAC. Consequently, we examine and review the strength and weaknesses of each of the localization techniques. Finally, we discuss the nature of applications that suits each localization technique the most.
{"title":"A Survey in Localization Techniques Used in Location-based Access Control","authors":"Maha Kanan Almutairi, Shameek Bhattacharjee","doi":"10.1109/AIIoT52608.2021.9454195","DOIUrl":"https://doi.org/10.1109/AIIoT52608.2021.9454195","url":null,"abstract":"There has been a huge growth in the number of mobile devices in the last few years. These mobile devices are equipped with various hardware that allows them to sense and receive different types of wireless signals such as Global Positioning System (GPS), Wi-Fi, Bluetooth, and others. Today, a mobile device is capable of obtaining its location in various ways. Location-enabled mobile devices enable applications to utilize and verify users' location to allow them to access location-based resources, which is known as Location-based access control (LBAC). The implementation of LBAC has grown rapidly with the increase in the number of mobile devices and location-based services (LBS) and is expected to grow more in the future. LBAC verifies the user's location is required. User location can be obtained using different localization techniques. In this paper, we survey the localization techniques used in LBAC. Consequently, we examine and review the strength and weaknesses of each of the localization techniques. Finally, we discuss the nature of applications that suits each localization technique the most.","PeriodicalId":443405,"journal":{"name":"2021 IEEE World AI IoT Congress (AIIoT)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125240643","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 : 2021-05-10DOI: 10.1109/AIIoT52608.2021.9454178
Sumaiya Binte Akther, Md Anik Hasan, N. Tasneem, Mohammad Monirujjaman Khan
In this current world, technology innovation is developing day by day which makes people's life easier and more comfortable. Nowadays, nearly every work of a computer and many features are presently empowered in a mobile application. To move in the city, it is quite expensive when people use transport privately but if it is possible to share, then the cost of transportation will decrease at least half or less than half. This paper presents an android application that works collectively using Google APIs and maps. This is a ride-sharing app. Users with the same destination will be able to share rides with others who are of the same place to reach. The application will calculate their fares. A database is used to store the records of registered users. By using the android based platform, application would be optimized for any usage. For the applications, efficient offline and online algorithms have been presented. An algorithm with theoretical analysis and trace-driven simulations under practical settings has been verified.
{"title":"An Interactive Android Application to Share Rides With NSUers","authors":"Sumaiya Binte Akther, Md Anik Hasan, N. Tasneem, Mohammad Monirujjaman Khan","doi":"10.1109/AIIoT52608.2021.9454178","DOIUrl":"https://doi.org/10.1109/AIIoT52608.2021.9454178","url":null,"abstract":"In this current world, technology innovation is developing day by day which makes people's life easier and more comfortable. Nowadays, nearly every work of a computer and many features are presently empowered in a mobile application. To move in the city, it is quite expensive when people use transport privately but if it is possible to share, then the cost of transportation will decrease at least half or less than half. This paper presents an android application that works collectively using Google APIs and maps. This is a ride-sharing app. Users with the same destination will be able to share rides with others who are of the same place to reach. The application will calculate their fares. A database is used to store the records of registered users. By using the android based platform, application would be optimized for any usage. For the applications, efficient offline and online algorithms have been presented. An algorithm with theoretical analysis and trace-driven simulations under practical settings has been verified.","PeriodicalId":443405,"journal":{"name":"2021 IEEE World AI IoT Congress (AIIoT)","volume":"64 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121252354","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 : 2021-05-10DOI: 10.1109/AIIoT52608.2021.9454208
A. Ornatelli, A. Tortorelli, F. Liberati
This paper tackles the power control problem in the context of wireless networks. The development of intelligent services based on widespread smart devices with limited energy storage capabilities and high interference sensitivity is heavily bounded by the energy consumption required for communication. For addressing this issue, a decentralized control approach based on multi-agent reinforcement learning has been developed. The most interesting feature of the proposed solution consists in its scalability and low complexity. As a consequence, the proposed approach can be deployed in presence of sensor nodes with low processing and communication capabilities. Simulations are presented to validate the proposed solution.
{"title":"A Distributed Reinforcement Learning approach for Power Control in Wireless Networks","authors":"A. Ornatelli, A. Tortorelli, F. Liberati","doi":"10.1109/AIIoT52608.2021.9454208","DOIUrl":"https://doi.org/10.1109/AIIoT52608.2021.9454208","url":null,"abstract":"This paper tackles the power control problem in the context of wireless networks. The development of intelligent services based on widespread smart devices with limited energy storage capabilities and high interference sensitivity is heavily bounded by the energy consumption required for communication. For addressing this issue, a decentralized control approach based on multi-agent reinforcement learning has been developed. The most interesting feature of the proposed solution consists in its scalability and low complexity. As a consequence, the proposed approach can be deployed in presence of sensor nodes with low processing and communication capabilities. Simulations are presented to validate the proposed solution.","PeriodicalId":443405,"journal":{"name":"2021 IEEE World AI IoT Congress (AIIoT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125804690","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 : 2021-05-10DOI: 10.1109/AIIoT52608.2021.9454242
Joseph M. Carmack, Scott Kuzdeba
Waveform representation, manipulation, and synthesis are challenging problems in the RF domain traditionally demanding expert knowledge to produce transparent and efficient solutions. In this work we present a low-complexity neural network architecture for waveform representation, manipulation, and synthesis. We demonstrate this architecture's performance by training it to represent Wi-Fi 802.11a/g waveforms and modify them with the objective of enhancing waveform distinguishability for RF fingerprint classification. We further present analysis of the network waveforms' latent representation to discover time and frequency properties of the learned transform. We discuss these properties in the context of traditional signals processing transforms to increase understanding and transparency of the algorithm and inspire future research into this domain. Although we target RF domain applications, we expect this architecture's performance and benefits to have high transferability to other domains.
{"title":"RiftNet Reconstruction Model for Radio Frequency Domain Waveform Representation and Synthesis","authors":"Joseph M. Carmack, Scott Kuzdeba","doi":"10.1109/AIIoT52608.2021.9454242","DOIUrl":"https://doi.org/10.1109/AIIoT52608.2021.9454242","url":null,"abstract":"Waveform representation, manipulation, and synthesis are challenging problems in the RF domain traditionally demanding expert knowledge to produce transparent and efficient solutions. In this work we present a low-complexity neural network architecture for waveform representation, manipulation, and synthesis. We demonstrate this architecture's performance by training it to represent Wi-Fi 802.11a/g waveforms and modify them with the objective of enhancing waveform distinguishability for RF fingerprint classification. We further present analysis of the network waveforms' latent representation to discover time and frequency properties of the learned transform. We discuss these properties in the context of traditional signals processing transforms to increase understanding and transparency of the algorithm and inspire future research into this domain. Although we target RF domain applications, we expect this architecture's performance and benefits to have high transferability to other domains.","PeriodicalId":443405,"journal":{"name":"2021 IEEE World AI IoT Congress (AIIoT)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125861601","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 : 2021-05-10DOI: 10.1109/AIIoT52608.2021.9454245
Faria Soroni, Md. Amdadul Bari, Mohammad Monirujjaman Khan
E-business has accelerated its developing position due to the world-wide fight against Covid-19.E-commerce app and website is no more an exceptional case in Bangladesh. ‘Geram Bazar’ which is an android and website interface an e- commerce system has been developed for the rising e-business platform. It has marked user eases at operating and surfing. It is a tool which can cut though the stiffs in climbing the way to make pure and affordable purchase of non- branded products. Initially, itenablescustomerstobuygoodswithafreshgradefromfarmers who produce those in their backyards with humble care. Further, the farmers would be able to avail a standard payment for each product. This feature also ensures delivery before the products freshness expires according to customer's favor. And finally due to the fact that the system would run under a brand name, it would minimize corruption in this sector which arises from hand- to-hand exchanging. This paper discusses the illustrated features and attributes of the application and website ‘Geramllazar’.
{"title":"GERAM BAZAR, A Mobile Application and Website Interface E-commerce","authors":"Faria Soroni, Md. Amdadul Bari, Mohammad Monirujjaman Khan","doi":"10.1109/AIIoT52608.2021.9454245","DOIUrl":"https://doi.org/10.1109/AIIoT52608.2021.9454245","url":null,"abstract":"E-business has accelerated its developing position due to the world-wide fight against Covid-19.E-commerce app and website is no more an exceptional case in Bangladesh. ‘Geram Bazar’ which is an android and website interface an e- commerce system has been developed for the rising e-business platform. It has marked user eases at operating and surfing. It is a tool which can cut though the stiffs in climbing the way to make pure and affordable purchase of non- branded products. Initially, itenablescustomerstobuygoodswithafreshgradefromfarmers who produce those in their backyards with humble care. Further, the farmers would be able to avail a standard payment for each product. This feature also ensures delivery before the products freshness expires according to customer's favor. And finally due to the fact that the system would run under a brand name, it would minimize corruption in this sector which arises from hand- to-hand exchanging. This paper discusses the illustrated features and attributes of the application and website ‘Geramllazar’.","PeriodicalId":443405,"journal":{"name":"2021 IEEE World AI IoT Congress (AIIoT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128415750","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 : 2021-05-10DOI: 10.1109/AIIoT52608.2021.9454166
Amber Honnef, Emily Sawall, Mohamed Mohamed, A. A. AlQahtani, Thamraa Alshayeb
Throughout the COVID-19 pandemic, one of the largest goals has been to social distance while still finding ways to continue our daily lives in a somewhat normal manner. Many businesses and institutions need ways to account for their attendees on a daily basis, but COVID-19 has created a rift in some of the normal ways that this can be done while abiding by social distancing rules and maintaining proper sanitation of objects and devices. In this study, we propose a zero-effort and zero-interaction approach, especially for being in the midst of a pandemic, as well as a probable solution for well beyond the pandemic due to this system's ease of use. This paper utilizes a Wi-Fi-enabled device (e.g., smartphone) and access points to calculate a user's location within a building and account for its activeness to consider a user's social distancing or not. The proposed scheme completely eliminates the necessity to have face-to-face interaction or physical contact with a person or a device.
{"title":"Zero-Effort Indoor Continuous Social Distancing Monitoring System","authors":"Amber Honnef, Emily Sawall, Mohamed Mohamed, A. A. AlQahtani, Thamraa Alshayeb","doi":"10.1109/AIIoT52608.2021.9454166","DOIUrl":"https://doi.org/10.1109/AIIoT52608.2021.9454166","url":null,"abstract":"Throughout the COVID-19 pandemic, one of the largest goals has been to social distance while still finding ways to continue our daily lives in a somewhat normal manner. Many businesses and institutions need ways to account for their attendees on a daily basis, but COVID-19 has created a rift in some of the normal ways that this can be done while abiding by social distancing rules and maintaining proper sanitation of objects and devices. In this study, we propose a zero-effort and zero-interaction approach, especially for being in the midst of a pandemic, as well as a probable solution for well beyond the pandemic due to this system's ease of use. This paper utilizes a Wi-Fi-enabled device (e.g., smartphone) and access points to calculate a user's location within a building and account for its activeness to consider a user's social distancing or not. The proposed scheme completely eliminates the necessity to have face-to-face interaction or physical contact with a person or a device.","PeriodicalId":443405,"journal":{"name":"2021 IEEE World AI IoT Congress (AIIoT)","volume":"108 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128609563","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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