Pub Date : 2021-06-14DOI: 10.1109/wf-iot51360.2021.9595896
{"title":"[Career and Professional Events]","authors":"","doi":"10.1109/wf-iot51360.2021.9595896","DOIUrl":"https://doi.org/10.1109/wf-iot51360.2021.9595896","url":null,"abstract":"","PeriodicalId":184138,"journal":{"name":"2021 IEEE 7th World Forum on Internet of Things (WF-IoT)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131636298","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 : 2021-06-14DOI: 10.1109/WF-IoT51360.2021.9594932
Hasibul Jamil, Ning Yang, N. Weng
Modern home network has traditional Ether-net/WiFi traffic along with emerging low power cross platform IoT traffic, which makes traditional Network Intrusion Detection Systems (IDS) approaches ineffective. This paper presents a deep neural network approach with a split architecture of Intrusion Detection System (IDS) specially suitable for home networks. The split architecture consists of multiple ML models and trained on two separate dataset for heterogeneous traffic. We also compare our model performance with reported different ML algorithms and found superiority of our model. The proposed model achieves 0.9694, 0.9625 and 0.9651 in precision, recall, and F1-score, respectively, for NSL-KDD dataset. Another interesting finding is that tree-based method and ensemble methods outperform our model in case the training dataset is unbalanced. An analysis of run-time implementation performance of the proposed IDS model is also discussed.
{"title":"Securing Home IoT Network with Machine Learning Based Classifiers","authors":"Hasibul Jamil, Ning Yang, N. Weng","doi":"10.1109/WF-IoT51360.2021.9594932","DOIUrl":"https://doi.org/10.1109/WF-IoT51360.2021.9594932","url":null,"abstract":"Modern home network has traditional Ether-net/WiFi traffic along with emerging low power cross platform IoT traffic, which makes traditional Network Intrusion Detection Systems (IDS) approaches ineffective. This paper presents a deep neural network approach with a split architecture of Intrusion Detection System (IDS) specially suitable for home networks. The split architecture consists of multiple ML models and trained on two separate dataset for heterogeneous traffic. We also compare our model performance with reported different ML algorithms and found superiority of our model. The proposed model achieves 0.9694, 0.9625 and 0.9651 in precision, recall, and F1-score, respectively, for NSL-KDD dataset. Another interesting finding is that tree-based method and ensemble methods outperform our model in case the training dataset is unbalanced. An analysis of run-time implementation performance of the proposed IDS model is also discussed.","PeriodicalId":184138,"journal":{"name":"2021 IEEE 7th World Forum on Internet of Things (WF-IoT)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126497797","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 : 2021-06-14DOI: 10.1109/WF-IoT51360.2021.9595819
Mert Nakıp, E. Gelenbe
A challenge of IoT networks is to offer Quality of Service (QoS) and meet deadline requirements when packets from numerous IoT devices must be forwarded. Thus this paper introduces the Randomization of flow Generation Times (RGT) that smooths incoming IoT traffic so that QoS improves and packet loss is avoided. When the “Earliest Deadline First” (EDF) or “Priority based on Average Load” (PAL) scheduling algorithms are used, simulation results show that RGT provides significantly better performance, for a small additional computational cost at each device, providing a useful performance improvement for IoT networks.
{"title":"Randomization of Data Generation Times Improves Performance of Predictive IoT Networks","authors":"Mert Nakıp, E. Gelenbe","doi":"10.1109/WF-IoT51360.2021.9595819","DOIUrl":"https://doi.org/10.1109/WF-IoT51360.2021.9595819","url":null,"abstract":"A challenge of IoT networks is to offer Quality of Service (QoS) and meet deadline requirements when packets from numerous IoT devices must be forwarded. Thus this paper introduces the Randomization of flow Generation Times (RGT) that smooths incoming IoT traffic so that QoS improves and packet loss is avoided. When the “Earliest Deadline First” (EDF) or “Priority based on Average Load” (PAL) scheduling algorithms are used, simulation results show that RGT provides significantly better performance, for a small additional computational cost at each device, providing a useful performance improvement for IoT networks.","PeriodicalId":184138,"journal":{"name":"2021 IEEE 7th World Forum on Internet of Things (WF-IoT)","volume":"142 1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116598670","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 : 2021-06-14DOI: 10.1109/WF-IoT51360.2021.9595051
Mohit Taneja, Nikita Jalodia, P. Malone, E. Misha
In the IoT era, the devices along the things-to-cloud continuum, present a unique opportunity to additionally serve as computing hubs. Termed Fog computing, this paradigm can be used to host applications and process data closer to the source. In this article, we present a methodical analysis of our fog enabled software system in an IoT enabled smart dairy farm. The developed software system uses locomotion data generated by wearables on cows’ feet to detect anomalies in their behaviour. We analyze the benefits of using a fog computing assisted approach for developing such IoT solutions. We use resource utilization as the performance metric for analyzing the benefits of leveraging the fog computing paradigm compared to the traditional cloud centric approach. The results suggest that a fog enabled software system brings benefits such as efficient utilization of computing resources, improved QoS etc. The evaluation indicates that there will be need of special design (including both low-level and high-level system design) re-configurations and also re-engineering of some components to provide higher scalability using less computational resources.
{"title":"Methodical Analysis of a Fog Computing Assisted Animal-Welfare Software System in a Real-World Smart Dairy Farm IoT Deployment","authors":"Mohit Taneja, Nikita Jalodia, P. Malone, E. Misha","doi":"10.1109/WF-IoT51360.2021.9595051","DOIUrl":"https://doi.org/10.1109/WF-IoT51360.2021.9595051","url":null,"abstract":"In the IoT era, the devices along the things-to-cloud continuum, present a unique opportunity to additionally serve as computing hubs. Termed Fog computing, this paradigm can be used to host applications and process data closer to the source. In this article, we present a methodical analysis of our fog enabled software system in an IoT enabled smart dairy farm. The developed software system uses locomotion data generated by wearables on cows’ feet to detect anomalies in their behaviour. We analyze the benefits of using a fog computing assisted approach for developing such IoT solutions. We use resource utilization as the performance metric for analyzing the benefits of leveraging the fog computing paradigm compared to the traditional cloud centric approach. The results suggest that a fog enabled software system brings benefits such as efficient utilization of computing resources, improved QoS etc. The evaluation indicates that there will be need of special design (including both low-level and high-level system design) re-configurations and also re-engineering of some components to provide higher scalability using less computational resources.","PeriodicalId":184138,"journal":{"name":"2021 IEEE 7th World Forum on Internet of Things (WF-IoT)","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123345802","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 : 2021-06-14DOI: 10.1109/WF-IoT51360.2021.9595004
Sebastián Echeverría, G. Lewis, Craig Mazzotta, Kyle O'Meara, Keegan Williams, Marc Novakouski, Amit Vasudevan, Matthew McCormack, V. Sekar
Internet of Things (IoT) security remains a challenge due to device vulnerabilities and untrusted supply chains, often limiting the benefits that organizations can obtain from integrating novel IoT devices to support business goals and enhance user experience. To that effect we developed KalKi: an IoT security platform that uses software-defined networking (SDN) concepts and constructs to create per-device defenses that enable integration of untrusted, off-the-shelf IoT devices. However, KalKi had limitations related to performance, scalability, and usability. This paper presents KalKi++, an evolution of KalKi that improves the performance, scalability and usability of the platform by orders of magnitude, with the added benefit of now being able to run on resource-limited hardware and support a larger number of use cases. We present the new architecture, enhanced threat model, and evaluation results for the new platform.
{"title":"KalKi++: A Scalable and Extensible IoT Security Platform","authors":"Sebastián Echeverría, G. Lewis, Craig Mazzotta, Kyle O'Meara, Keegan Williams, Marc Novakouski, Amit Vasudevan, Matthew McCormack, V. Sekar","doi":"10.1109/WF-IoT51360.2021.9595004","DOIUrl":"https://doi.org/10.1109/WF-IoT51360.2021.9595004","url":null,"abstract":"Internet of Things (IoT) security remains a challenge due to device vulnerabilities and untrusted supply chains, often limiting the benefits that organizations can obtain from integrating novel IoT devices to support business goals and enhance user experience. To that effect we developed KalKi: an IoT security platform that uses software-defined networking (SDN) concepts and constructs to create per-device defenses that enable integration of untrusted, off-the-shelf IoT devices. However, KalKi had limitations related to performance, scalability, and usability. This paper presents KalKi++, an evolution of KalKi that improves the performance, scalability and usability of the platform by orders of magnitude, with the added benefit of now being able to run on resource-limited hardware and support a larger number of use cases. We present the new architecture, enhanced threat model, and evaluation results for the new platform.","PeriodicalId":184138,"journal":{"name":"2021 IEEE 7th World Forum on Internet of Things (WF-IoT)","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130961514","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 : 2021-06-14DOI: 10.1109/WF-IoT51360.2021.9596026
Felipe Rocha, Everton Cavalcante, T. Batista, Daniel Araújo
The integration of Machine Learning (ML) with the Internet of Things (IoT) allows effectively analyzing the huge amount of gathered data, thus making them more meaningful and helping to accurately identify anomalies and potential problems. This paper presents the use of ML techniques in the analysis of data gathered from Smart Place, a real-world IoT-based smart building system that automatically controls air conditioners aiming at saving energy. A predictive agent uses these techniques to determine the actual state of air conditioners based on data about temperature, humidity, and the presence of people in the monitored environments. Four well-known ML classifiers (namely k-Nearest Neighbors, Multi-Layer Perception neural networks, Random Forest, and Support Vector Machines) were considered in an empirical study aimed to evaluate their suitability with respect to accuracy, resource utilization, and execution time. Obtained results showed a maximum average accuracy of 96.5% in the prediction of the state of air conditioners, besides the feasibility of using alternative models in compliance with resource constraints related to the IoT scenario.
{"title":"Evaluating Machine Learning Classifiers for Prediction in an IoT-based Smart Building System","authors":"Felipe Rocha, Everton Cavalcante, T. Batista, Daniel Araújo","doi":"10.1109/WF-IoT51360.2021.9596026","DOIUrl":"https://doi.org/10.1109/WF-IoT51360.2021.9596026","url":null,"abstract":"The integration of Machine Learning (ML) with the Internet of Things (IoT) allows effectively analyzing the huge amount of gathered data, thus making them more meaningful and helping to accurately identify anomalies and potential problems. This paper presents the use of ML techniques in the analysis of data gathered from Smart Place, a real-world IoT-based smart building system that automatically controls air conditioners aiming at saving energy. A predictive agent uses these techniques to determine the actual state of air conditioners based on data about temperature, humidity, and the presence of people in the monitored environments. Four well-known ML classifiers (namely k-Nearest Neighbors, Multi-Layer Perception neural networks, Random Forest, and Support Vector Machines) were considered in an empirical study aimed to evaluate their suitability with respect to accuracy, resource utilization, and execution time. Obtained results showed a maximum average accuracy of 96.5% in the prediction of the state of air conditioners, besides the feasibility of using alternative models in compliance with resource constraints related to the IoT scenario.","PeriodicalId":184138,"journal":{"name":"2021 IEEE 7th World Forum on Internet of Things (WF-IoT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114708359","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 : 2021-06-14DOI: 10.1109/WF-IoT51360.2021.9596033
S. Azumah, Nelly Elsayed, Victor Adewopo, Zaghloul Saad Zaghloul, Chengcheng Li
The technological advancement of the Internet of Things (IoT) devices in our world today has become beneficial to many users but has security issues that are left unattended. IoT devices have the ability to connect to other devices on the internet to transmit and share data from anywhere. Hence, the need to secure these devices as they improve the quality of life comfort. Research shows that about 70% of IoT devices are easy to hack. Therefore, an efficient mechanism is highly needed to safeguard these devices, especially in smart homes. This paper proposes a novel deep learning-based anomaly detection approach to predict cyberattacks on smart home IoT network devices and learn new outliers as they occur over time using IoT network intrusion datasets. The proposed model is based on long-term memory architecture, which achieves a significant accuracy improvement compared to the existing state-of-the-art anomaly detection models for IoT networks in smart homes.
{"title":"A Deep LSTM based Approach for Intrusion Detection IoT Devices Network in Smart Home","authors":"S. Azumah, Nelly Elsayed, Victor Adewopo, Zaghloul Saad Zaghloul, Chengcheng Li","doi":"10.1109/WF-IoT51360.2021.9596033","DOIUrl":"https://doi.org/10.1109/WF-IoT51360.2021.9596033","url":null,"abstract":"The technological advancement of the Internet of Things (IoT) devices in our world today has become beneficial to many users but has security issues that are left unattended. IoT devices have the ability to connect to other devices on the internet to transmit and share data from anywhere. Hence, the need to secure these devices as they improve the quality of life comfort. Research shows that about 70% of IoT devices are easy to hack. Therefore, an efficient mechanism is highly needed to safeguard these devices, especially in smart homes. This paper proposes a novel deep learning-based anomaly detection approach to predict cyberattacks on smart home IoT network devices and learn new outliers as they occur over time using IoT network intrusion datasets. The proposed model is based on long-term memory architecture, which achieves a significant accuracy improvement compared to the existing state-of-the-art anomaly detection models for IoT networks in smart homes.","PeriodicalId":184138,"journal":{"name":"2021 IEEE 7th World Forum on Internet of Things (WF-IoT)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134101312","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 : 2021-06-14DOI: 10.1109/WF-IoT51360.2021.9595688
Gangavarapu Vigneswara Ihita, K. S. Viswanadh, Y. Sudhansh, S. Chaudhari, S. Gaur
The surge in the development and adoption of Internet of Things (IoT)-enabled smart city technologies has brought with it a diverse set of critical security challenges. In this paper, protocol and network security threats pertaining to a large-scale IoT-enabled pollution monitoring sensor network, AirIoT, deployed in and around an educational campus in the Indian city of Hyderabad, have been explored. Using the STRIDE methodology, the paper assesses various threat vectors for the deployment. As solutions, the paper proposes an approach for end-to-end encryption, protocol and dashboard security, and a proof of concept deauthentication detector. This baseline threat analysis and risk assessment can provide a foundation for securing Wi-Fi and mobile network-based large-scale IoT deployments.
{"title":"Security Analysis of Large Scale IoT Network for Pollution Monitoring in Urban India","authors":"Gangavarapu Vigneswara Ihita, K. S. Viswanadh, Y. Sudhansh, S. Chaudhari, S. Gaur","doi":"10.1109/WF-IoT51360.2021.9595688","DOIUrl":"https://doi.org/10.1109/WF-IoT51360.2021.9595688","url":null,"abstract":"The surge in the development and adoption of Internet of Things (IoT)-enabled smart city technologies has brought with it a diverse set of critical security challenges. In this paper, protocol and network security threats pertaining to a large-scale IoT-enabled pollution monitoring sensor network, AirIoT, deployed in and around an educational campus in the Indian city of Hyderabad, have been explored. Using the STRIDE methodology, the paper assesses various threat vectors for the deployment. As solutions, the paper proposes an approach for end-to-end encryption, protocol and dashboard security, and a proof of concept deauthentication detector. This baseline threat analysis and risk assessment can provide a foundation for securing Wi-Fi and mobile network-based large-scale IoT deployments.","PeriodicalId":184138,"journal":{"name":"2021 IEEE 7th World Forum on Internet of Things (WF-IoT)","volume":"256 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132726257","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 : 2021-06-14DOI: 10.1109/WF-IoT51360.2021.9594956
Somdip Dey, Suman Saha, A. Singh, K. Mcdonald-Maier
Mapping resources to tasks accurately in order to gain performance, energy efficiency, reduction in peak temperature, etc. on an embedded/Edge device is a big challenge. Machine learning has proven to be effective in learning heuristics based resource mapping approaches, but its success is bound by the quality of feature extraction. Additionally, feature extraction in such approaches not just requires expert domain knowledge and human effort, but at the same time requires the application (tasks) to be profiled for such processes. Therefore, the efficacy of such resource mapping methodologies depends on expertise, skills, profiling time and architecture of the system. In this paper, we propose a novel methodology, Asynchronous Hybrid Deep Learning (AHDL), which sets a new paradigm of using Deep Learning approaches to map resources to application (tasks). In our approach, we leverage task profiling methodologies to achieve accurate mapping in order to achieve greater reward from the system, but at the same time is able to allocate resources to unprofiled application (tasks) at the same time without the need of manual feature extraction by domain experts. Our proposed methodology is able to achieve competitive results in comparison with the state-of- the-art without the usual associated challenges such as manual feature extraction.
{"title":"Asynchronous Hybrid Deep Learning (AHDL): A Deep Learning Based Resource Mapping in DVFS Enabled Mobile MPSoCs","authors":"Somdip Dey, Suman Saha, A. Singh, K. Mcdonald-Maier","doi":"10.1109/WF-IoT51360.2021.9594956","DOIUrl":"https://doi.org/10.1109/WF-IoT51360.2021.9594956","url":null,"abstract":"Mapping resources to tasks accurately in order to gain performance, energy efficiency, reduction in peak temperature, etc. on an embedded/Edge device is a big challenge. Machine learning has proven to be effective in learning heuristics based resource mapping approaches, but its success is bound by the quality of feature extraction. Additionally, feature extraction in such approaches not just requires expert domain knowledge and human effort, but at the same time requires the application (tasks) to be profiled for such processes. Therefore, the efficacy of such resource mapping methodologies depends on expertise, skills, profiling time and architecture of the system. In this paper, we propose a novel methodology, Asynchronous Hybrid Deep Learning (AHDL), which sets a new paradigm of using Deep Learning approaches to map resources to application (tasks). In our approach, we leverage task profiling methodologies to achieve accurate mapping in order to achieve greater reward from the system, but at the same time is able to allocate resources to unprofiled application (tasks) at the same time without the need of manual feature extraction by domain experts. Our proposed methodology is able to achieve competitive results in comparison with the state-of- the-art without the usual associated challenges such as manual feature extraction.","PeriodicalId":184138,"journal":{"name":"2021 IEEE 7th World Forum on Internet of Things (WF-IoT)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126043952","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 : 2021-06-14DOI: 10.1109/WF-IoT51360.2021.9595440
Fathi H. Amsaad, Selçuk Köse
Next generation smart power grid offers advanced features to enhance the traditional power grid by enabling faster and more user-friendly two-way communications between utility centers and the consumers for a faster, greener, safer, more reliable, and increasingly efficient power delivery. The energy storage units and smart charging stations have become the essential components of a smart power grid. An efficient authentication and key management scheme is proposed in this work to realize a secure and trusted smart charging coordination system using a low-cost data encryption standard (DES) design and a lightweight physical unclonable function. The proposed scheme is implemented and tested on a re-programmable platform using Artix-7 FPGA device. The experimental results demonstrate that the proposed scheme can be efficiently realized on a off-the-shelf hardware, preserve the privacy of energy storage unit owners, and provide low-cost authentication for different NIST security levels.
{"title":"A Lightweight Hardware-based Authentication for Secure Smart Grid Energy Storage Units","authors":"Fathi H. Amsaad, Selçuk Köse","doi":"10.1109/WF-IoT51360.2021.9595440","DOIUrl":"https://doi.org/10.1109/WF-IoT51360.2021.9595440","url":null,"abstract":"Next generation smart power grid offers advanced features to enhance the traditional power grid by enabling faster and more user-friendly two-way communications between utility centers and the consumers for a faster, greener, safer, more reliable, and increasingly efficient power delivery. The energy storage units and smart charging stations have become the essential components of a smart power grid. An efficient authentication and key management scheme is proposed in this work to realize a secure and trusted smart charging coordination system using a low-cost data encryption standard (DES) design and a lightweight physical unclonable function. The proposed scheme is implemented and tested on a re-programmable platform using Artix-7 FPGA device. The experimental results demonstrate that the proposed scheme can be efficiently realized on a off-the-shelf hardware, preserve the privacy of energy storage unit owners, and provide low-cost authentication for different NIST security levels.","PeriodicalId":184138,"journal":{"name":"2021 IEEE 7th World Forum on Internet of Things (WF-IoT)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123673240","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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