Pub Date : 2024-01-06DOI: 10.1109/CCNC51664.2024.10454708
Supriya Kumari, Shwetha Vittal, Antony Franklin A
Providing resilient and sustained service is quite challenging in the Service Based Architecture of distributed 5G Core (5GC) as multiple Network Functions (NFs) are involved to help serve the various User Service Requests (USRs) arriving in the control plane. In this regard, the continuous monitoring of individual NFs in a Closed Loop Automation (CLA) is a need of hour to keep up the robust and resilient functioning of the 5GC overall. Any unforeseen situations like the sudden failure, overload, or congestion of the NFs of the 5GC can drop the critical USRs unnecessarily. This paper proposes the proactive monitoring of the NFs of the 5GC in the control plane and utilizes it to intelligently schedule and serve the frequently arriving USRs and prioritize the critical slice service requests. Specifically, the Ford-Fulkerson algorithm popularly known as the Max-Flow problem solver is leveraged to proactively assess the NFs' performance and availability and use it effectively to serve critical service requests arriving during unexpected situations of failure and overloads. Our experiments based on the 3GPP-compliant 5G testbed show that, with the proposed solution, the native 5GC can serve 20% more predominant USRs, and the slice-supportive 5GC can serve 33% more massive Machine Type Communications (mMTC) slice USRs, and 47% more ultra Reliable Low Latency Communications (uRLLC) slice USRs while handling their respective peak traffic.
{"title":"Resource-Aware Service Prioritization in a Slice-Supportive 5G Core Control Plane for Improved Resilience and Sustenance","authors":"Supriya Kumari, Shwetha Vittal, Antony Franklin A","doi":"10.1109/CCNC51664.2024.10454708","DOIUrl":"https://doi.org/10.1109/CCNC51664.2024.10454708","url":null,"abstract":"Providing resilient and sustained service is quite challenging in the Service Based Architecture of distributed 5G Core (5GC) as multiple Network Functions (NFs) are involved to help serve the various User Service Requests (USRs) arriving in the control plane. In this regard, the continuous monitoring of individual NFs in a Closed Loop Automation (CLA) is a need of hour to keep up the robust and resilient functioning of the 5GC overall. Any unforeseen situations like the sudden failure, overload, or congestion of the NFs of the 5GC can drop the critical USRs unnecessarily. This paper proposes the proactive monitoring of the NFs of the 5GC in the control plane and utilizes it to intelligently schedule and serve the frequently arriving USRs and prioritize the critical slice service requests. Specifically, the Ford-Fulkerson algorithm popularly known as the Max-Flow problem solver is leveraged to proactively assess the NFs' performance and availability and use it effectively to serve critical service requests arriving during unexpected situations of failure and overloads. Our experiments based on the 3GPP-compliant 5G testbed show that, with the proposed solution, the native 5GC can serve 20% more predominant USRs, and the slice-supportive 5GC can serve 33% more massive Machine Type Communications (mMTC) slice USRs, and 47% more ultra Reliable Low Latency Communications (uRLLC) slice USRs while handling their respective peak traffic.","PeriodicalId":518411,"journal":{"name":"2024 IEEE 21st Consumer Communications & Networking Conference (CCNC)","volume":"3 6","pages":"113-120"},"PeriodicalIF":0.0,"publicationDate":"2024-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140531631","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 : 2024-01-06DOI: 10.1109/CCNC51664.2024.10454877
Kentaro Morise, Tokimasa Toyohara, Hiroaki Nishi
IoT applications require secure communication methods that protect personal information contained in communication data. This study focuses on MQTT, a low-cost protocol used for IoT communication, and proposes a mechanism to anonymize communication data between IoT and clients. MQTT is a publish-subscribe model of communication where a broker handles many-to-many communications among clients. Due to the concentration of communications on the broker, it is efficient to anonymize data there. Therefore, the proposed mechanism performs differential privacy anonymization of communication data on the MQTT broker. We also propose a mechanism to anonymize data according to anonymization criteria required by senders and receivers using topic names and user properties, which are features of MQTT. We implemented the proposed mechanism in an FPGA-based MQTT broker and confirmed that it achieves the same throughput and low latency as regular MQTT communication and satisfies IoT applications such as power control and automated driving that require sub-millisecond latency.
{"title":"Proposal of Differential Privacy Anonymization for IoT Applications Using MQTT Broker","authors":"Kentaro Morise, Tokimasa Toyohara, Hiroaki Nishi","doi":"10.1109/CCNC51664.2024.10454877","DOIUrl":"https://doi.org/10.1109/CCNC51664.2024.10454877","url":null,"abstract":"IoT applications require secure communication methods that protect personal information contained in communication data. This study focuses on MQTT, a low-cost protocol used for IoT communication, and proposes a mechanism to anonymize communication data between IoT and clients. MQTT is a publish-subscribe model of communication where a broker handles many-to-many communications among clients. Due to the concentration of communications on the broker, it is efficient to anonymize data there. Therefore, the proposed mechanism performs differential privacy anonymization of communication data on the MQTT broker. We also propose a mechanism to anonymize data according to anonymization criteria required by senders and receivers using topic names and user properties, which are features of MQTT. We implemented the proposed mechanism in an FPGA-based MQTT broker and confirmed that it achieves the same throughput and low latency as regular MQTT communication and satisfies IoT applications such as power control and automated driving that require sub-millisecond latency.","PeriodicalId":518411,"journal":{"name":"2024 IEEE 21st Consumer Communications & Networking Conference (CCNC)","volume":"94 7","pages":"634-635"},"PeriodicalIF":0.0,"publicationDate":"2024-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140531653","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 : 2024-01-06DOI: 10.1109/CCNC51664.2024.10454678
Brent Anderson, Razib Iqbal
Voice-enabled virtual assistants have received widespread popularity in smart homes. Adding a context detection feature in voice conversations with virtual assistants can offer a more personalized experience in smart homes such that it maintains awareness of the ongoing conversation and responds appropriately. In this paper, we present a novel word embedding with emotionally relevant keyword search (WERKS) approach for context detection. This WERKS approach makes use of a combination of emotion detection, keyword search, and word embedding for context detection from voice commands and short conversations with virtual assistants. The TPOT classifier was applied over RAVDESS and a custom data set to obtain experimental results, which demonstrated a 15 and 12 percent increase in prediction accuracy of our defined contexts.
{"title":"Word Embedding with Emotionally Relevant Keyword Search for Context Detection from Smart Home Voice Commands","authors":"Brent Anderson, Razib Iqbal","doi":"10.1109/CCNC51664.2024.10454678","DOIUrl":"https://doi.org/10.1109/CCNC51664.2024.10454678","url":null,"abstract":"Voice-enabled virtual assistants have received widespread popularity in smart homes. Adding a context detection feature in voice conversations with virtual assistants can offer a more personalized experience in smart homes such that it maintains awareness of the ongoing conversation and responds appropriately. In this paper, we present a novel word embedding with emotionally relevant keyword search (WERKS) approach for context detection. This WERKS approach makes use of a combination of emotion detection, keyword search, and word embedding for context detection from voice commands and short conversations with virtual assistants. The TPOT classifier was applied over RAVDESS and a custom data set to obtain experimental results, which demonstrated a 15 and 12 percent increase in prediction accuracy of our defined contexts.","PeriodicalId":518411,"journal":{"name":"2024 IEEE 21st Consumer Communications & Networking Conference (CCNC)","volume":"64 9","pages":"594-595"},"PeriodicalIF":0.0,"publicationDate":"2024-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140531826","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 : 2024-01-06DOI: 10.1109/CCNC51664.2024.10454739
Xing-fa Liu, Wei Yu, Cheng Qian, David W. Griffith, N. Golmie
In this paper, we address the issue of Channel State Information (CSI) prediction of the Internet of Vehicles (loV) system, which is a highly dynamic network environment. We propose a deep reinforcement learning-based approach to predict CSI with historical data and video footage captured by smart cameras. Specifically, we use a Conventional Neural Network (CNN) to extract unique environmental characteristics, which will be sent to a Recurrent Neural Network (RNN)-based learning model so that the future CSI can be predicted. Our approach also considers the heterogeneous nature of IoV communication environments by adopting transfer learning to reduce the training cost when applying our approach to different IoV scenarios. We assess the efficacy of our proposed approach using our designed IoV simulation platform. The experimental results confirm that our approach can accurately predict CSI by using historically generated data.
{"title":"Deep Reinforcement Learning for Channel State Information Prediction in Internet of Vehicles","authors":"Xing-fa Liu, Wei Yu, Cheng Qian, David W. Griffith, N. Golmie","doi":"10.1109/CCNC51664.2024.10454739","DOIUrl":"https://doi.org/10.1109/CCNC51664.2024.10454739","url":null,"abstract":"In this paper, we address the issue of Channel State Information (CSI) prediction of the Internet of Vehicles (loV) system, which is a highly dynamic network environment. We propose a deep reinforcement learning-based approach to predict CSI with historical data and video footage captured by smart cameras. Specifically, we use a Conventional Neural Network (CNN) to extract unique environmental characteristics, which will be sent to a Recurrent Neural Network (RNN)-based learning model so that the future CSI can be predicted. Our approach also considers the heterogeneous nature of IoV communication environments by adopting transfer learning to reduce the training cost when applying our approach to different IoV scenarios. We assess the efficacy of our proposed approach using our designed IoV simulation platform. The experimental results confirm that our approach can accurately predict CSI by using historically generated data.","PeriodicalId":518411,"journal":{"name":"2024 IEEE 21st Consumer Communications & Networking Conference (CCNC)","volume":"66 7","pages":"388-391"},"PeriodicalIF":0.0,"publicationDate":"2024-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140531969","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 : 2024-01-06DOI: 10.1109/CCNC51664.2024.10454865
Rei Nakagawa, S. Ohzahata, Ryo Yamamoto
Today, information centric networking enables adaptive video streaming clients to further improve QoE by applying flexible content-based control. However, an adaptive bitrate algorithm makes a client occupy the bottleneck link at excessively high bitrate, reducing the QoE fairness to other clients sharing the bottleneck link. Then, we propose fairAccel, a method of accelerating bitrate-based feedback control for achieving QoE fairness. fairAccel assigns more bandwidth to clients selecting the lower bitrate while suppressing content requests from clients selecting the highest bitrate on the bottleneck link. In addition, to further improve QoE fairness, fairAccel exploits the symmetric routing of ICN content request / response and applies bidirectional feedback control to the content request / response path. Thus, fairAccel accelerates feedback control by mitigating router queues under control of suppressing content requests before excessive traffic is delivered to the response path. Through simulation experiments, fairAccel improves the average bitrate and further improves QoE fairness for representative ABR algorithms.
{"title":"Accelerating Feedback Control for QoE Fairness in Adaptive Video Streaming Over ICN","authors":"Rei Nakagawa, S. Ohzahata, Ryo Yamamoto","doi":"10.1109/CCNC51664.2024.10454865","DOIUrl":"https://doi.org/10.1109/CCNC51664.2024.10454865","url":null,"abstract":"Today, information centric networking enables adaptive video streaming clients to further improve QoE by applying flexible content-based control. However, an adaptive bitrate algorithm makes a client occupy the bottleneck link at excessively high bitrate, reducing the QoE fairness to other clients sharing the bottleneck link. Then, we propose fairAccel, a method of accelerating bitrate-based feedback control for achieving QoE fairness. fairAccel assigns more bandwidth to clients selecting the lower bitrate while suppressing content requests from clients selecting the highest bitrate on the bottleneck link. In addition, to further improve QoE fairness, fairAccel exploits the symmetric routing of ICN content request / response and applies bidirectional feedback control to the content request / response path. Thus, fairAccel accelerates feedback control by mitigating router queues under control of suppressing content requests before excessive traffic is delivered to the response path. Through simulation experiments, fairAccel improves the average bitrate and further improves QoE fairness for representative ABR algorithms.","PeriodicalId":518411,"journal":{"name":"2024 IEEE 21st Consumer Communications & Networking Conference (CCNC)","volume":"87 11","pages":"98-106"},"PeriodicalIF":0.0,"publicationDate":"2024-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140531662","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 : 2024-01-06DOI: 10.1109/CCNC51664.2024.10454777
Masahiro Takigawa, Ryochi Kataoka, I. Kanno, Yoji Kishi
This paper proposes an antenna design suitable for a mobile analog repeater with frequency-to-spatial multiplexing do-main conversion (FSMDC) among access and backhaul link. The relaying scheme with FSMDC, which we had proposed, converts wider band frequency multiplexing in access link into spatial multiplexing for the backhaul link only with analog circuits, and it achieves low latency and high capacity in millimeter wave spectrum. However, the typical scenario where the millimeter wave repeater is operated is LoS environment, and spatial multiplexing (i.e. LoS-MIMO) gain is not secured due to its dependency to the communication distance. For the robustness, the proposed antenna design is optimized by applying cost functions, that can achieve better channel capacity of FSMDC at any communication distance, as the fitness in genetic algorithm. The simulation results show its robustness to the communication distance of the LoS MIMO Links.
本文提出了一种适用于移动模拟直放站的天线设计，该直放站在接入链路和回程链路之间采用频率-空间多路复用主转换（FSMDC）技术。我们提出的带 FSMDC 的中继方案仅使用模拟电路将接入链路中的宽带频率复用转换为回程链路的空间复用，在毫米波频谱中实现了低延迟和高容量。然而，毫米波中继器运行的典型场景是 LoS 环境，由于空间多路复用（即 LoS-MIMO）增益与通信距离有关，因此无法保证其增益。为了提高鲁棒性，我们采用成本函数优化了拟议的天线设计，该函数在任何通信距离下都能实现更好的 FSMDC 信道容量，并将其作为遗传算法中的适应度。仿真结果表明，它对 LoS MIMO 链路的通信距离具有鲁棒性。
{"title":"Antenna Design for Robust Millimeter Wave LoS-MIMO Link in Mobile Analog Repeater Achieving Low Latency and High Capacity","authors":"Masahiro Takigawa, Ryochi Kataoka, I. Kanno, Yoji Kishi","doi":"10.1109/CCNC51664.2024.10454777","DOIUrl":"https://doi.org/10.1109/CCNC51664.2024.10454777","url":null,"abstract":"This paper proposes an antenna design suitable for a mobile analog repeater with frequency-to-spatial multiplexing do-main conversion (FSMDC) among access and backhaul link. The relaying scheme with FSMDC, which we had proposed, converts wider band frequency multiplexing in access link into spatial multiplexing for the backhaul link only with analog circuits, and it achieves low latency and high capacity in millimeter wave spectrum. However, the typical scenario where the millimeter wave repeater is operated is LoS environment, and spatial multiplexing (i.e. LoS-MIMO) gain is not secured due to its dependency to the communication distance. For the robustness, the proposed antenna design is optimized by applying cost functions, that can achieve better channel capacity of FSMDC at any communication distance, as the fitness in genetic algorithm. The simulation results show its robustness to the communication distance of the LoS MIMO Links.","PeriodicalId":518411,"journal":{"name":"2024 IEEE 21st Consumer Communications & Networking Conference (CCNC)","volume":"87 6","pages":"912-917"},"PeriodicalIF":0.0,"publicationDate":"2024-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140531881","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 : 2024-01-06DOI: 10.1109/CCNC51664.2024.10454769
S. Bouk, Babatunji Omoniwa, Sachin Shetty
5G networks are expected to provide high-speed, low-latency, and reliable connectivity to support various applications such as autonomous vehicles, smart cities, and the Internet of Things (IoT). However, the performance of 5G networks can be affected by several factors such as interference, congestion, signal attenuation, or attacks, which can lead to packet loss and retransmissions. Retransmissions in the network may be seen as an essential measure to improve network reliability, but a high retransmission rate may indicate issues that can help network operators mitigate possible service disruptions or threats to network users. A deep learning-based approach has been proposed to predict downlink retransmissions in 5G networks, achieving as much as 5%- 15% improvement over traditional prediction algorithms.
{"title":"Predicting Downlink Retransmissions in 5G Networks Using Deep Learning","authors":"S. Bouk, Babatunji Omoniwa, Sachin Shetty","doi":"10.1109/CCNC51664.2024.10454769","DOIUrl":"https://doi.org/10.1109/CCNC51664.2024.10454769","url":null,"abstract":"5G networks are expected to provide high-speed, low-latency, and reliable connectivity to support various applications such as autonomous vehicles, smart cities, and the Internet of Things (IoT). However, the performance of 5G networks can be affected by several factors such as interference, congestion, signal attenuation, or attacks, which can lead to packet loss and retransmissions. Retransmissions in the network may be seen as an essential measure to improve network reliability, but a high retransmission rate may indicate issues that can help network operators mitigate possible service disruptions or threats to network users. A deep learning-based approach has been proposed to predict downlink retransmissions in 5G networks, achieving as much as 5%- 15% improvement over traditional prediction algorithms.","PeriodicalId":518411,"journal":{"name":"2024 IEEE 21st Consumer Communications & Networking Conference (CCNC)","volume":"79 11","pages":"1056-1057"},"PeriodicalIF":0.0,"publicationDate":"2024-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140531894","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}
We propose a user mobility-driven federated learning method, which integrates learning models from different regions, leveraging user mobility. This method aims to improve performance of learning models in specific regions by merging them with models from other areas. In regions with less user mobility, our method creates unique regional models, while in areas with high mobility, it integrates models for enhanced performance. Evaluation results indicate that accuracy improved with additional training, although it temporarily decreased after model integration.
{"title":"Experimental Evaluation Toward Mobility-Driven Model Integration Between Edges","authors":"Shota Ono, Taku Yamazaki, Takumi Miyoshi, Akihito Taya, Yuuki Nishiyama, K. Sezaki","doi":"10.1109/CCNC51664.2024.10454772","DOIUrl":"https://doi.org/10.1109/CCNC51664.2024.10454772","url":null,"abstract":"We propose a user mobility-driven federated learning method, which integrates learning models from different regions, leveraging user mobility. This method aims to improve performance of learning models in specific regions by merging them with models from other areas. In regions with less user mobility, our method creates unique regional models, while in areas with high mobility, it integrates models for enhanced performance. Evaluation results indicate that accuracy improved with additional training, although it temporarily decreased after model integration.","PeriodicalId":518411,"journal":{"name":"2024 IEEE 21st Consumer Communications & Networking Conference (CCNC)","volume":"103 10","pages":"610-611"},"PeriodicalIF":0.0,"publicationDate":"2024-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140531810","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 : 2024-01-06DOI: 10.1109/CCNC51664.2024.10454876
Ei Tanaka, Y. Kawamoto, Nei Kato, Masashi Iwabuchi, Riku Ohmiya, T. Murakami
Intelligent reflecting surface (IRS) is a device that can reflect radio waves in any direction by setting the phase shift of the reflecting elements. It is expected to solve the problems of high-frequency band communications, such as vulnerability to obstacles, and to realize super-multiplex connections in the high-frequency band. Since the reflective elements of IRS can only be time-division controlled and can basically support only one user per time slot, it is highly likely that a large number of resource blocks will be allocated to a single user to perform communications. However, in such a case, the frequency efficiency is reduced due to the effect of beam squint. In this paper, we show the effectiveness of a method to increase frequency efficiency by optimizing the reflection direction through resource allocation and IRS phase control.
{"title":"Frequency Resource Allocation for IRS-Aided Communication Using Beam Squint Approach","authors":"Ei Tanaka, Y. Kawamoto, Nei Kato, Masashi Iwabuchi, Riku Ohmiya, T. Murakami","doi":"10.1109/CCNC51664.2024.10454876","DOIUrl":"https://doi.org/10.1109/CCNC51664.2024.10454876","url":null,"abstract":"Intelligent reflecting surface (IRS) is a device that can reflect radio waves in any direction by setting the phase shift of the reflecting elements. It is expected to solve the problems of high-frequency band communications, such as vulnerability to obstacles, and to realize super-multiplex connections in the high-frequency band. Since the reflective elements of IRS can only be time-division controlled and can basically support only one user per time slot, it is highly likely that a large number of resource blocks will be allocated to a single user to perform communications. However, in such a case, the frequency efficiency is reduced due to the effect of beam squint. In this paper, we show the effectiveness of a method to increase frequency efficiency by optimizing the reflection direction through resource allocation and IRS phase control.","PeriodicalId":518411,"journal":{"name":"2024 IEEE 21st Consumer Communications & Networking Conference (CCNC)","volume":"63 8","pages":"1064-1065"},"PeriodicalIF":0.0,"publicationDate":"2024-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140531830","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 : 2024-01-06DOI: 10.1109/CCNC51664.2024.10454888
Uttam Ghosh, Debashis Das, Sourav Banerjee, S. Mohanty
The proliferation of interconnected devices in the era of the Internet of Things (IoT) has given rise to the need for robust device identity management and authentication mechanisms in cyber-physical systems (CPSs). Traditional centralized approaches to identity management face challenges of security, scalability, and privacy. Therefore, the paper provides an innovative approach by fusing Self-Sovereign Identity (SSI) with blockchain technology to revolutionize device identity management within CPS environments. In this paper, devices autonomously initiate their identity-creation processes. Each device generates a cryptographic key pair comprising a public key for openly identifying the device and a closely guarded private key used for authentication and decryption purposes. The research also introduces an innovative authentication algorithm within CPS environments that employs secure tokens to validate the authenticity of devices. The proposed framework reduces the risk of unauthorized access and data breaches while empowering devices with control over their identities. Overall, the proposed approach not only enhances security, privacy, and resilience within CPSs but also provides a transformative solution for identity management in dynamic and autonomous device environments.
{"title":"Blockchain-Based Device Identity Management and Authentication in Cyber-Physical Systems","authors":"Uttam Ghosh, Debashis Das, Sourav Banerjee, S. Mohanty","doi":"10.1109/CCNC51664.2024.10454888","DOIUrl":"https://doi.org/10.1109/CCNC51664.2024.10454888","url":null,"abstract":"The proliferation of interconnected devices in the era of the Internet of Things (IoT) has given rise to the need for robust device identity management and authentication mechanisms in cyber-physical systems (CPSs). Traditional centralized approaches to identity management face challenges of security, scalability, and privacy. Therefore, the paper provides an innovative approach by fusing Self-Sovereign Identity (SSI) with blockchain technology to revolutionize device identity management within CPS environments. In this paper, devices autonomously initiate their identity-creation processes. Each device generates a cryptographic key pair comprising a public key for openly identifying the device and a closely guarded private key used for authentication and decryption purposes. The research also introduces an innovative authentication algorithm within CPS environments that employs secure tokens to validate the authenticity of devices. The proposed framework reduces the risk of unauthorized access and data breaches while empowering devices with control over their identities. Overall, the proposed approach not only enhances security, privacy, and resilience within CPSs but also provides a transformative solution for identity management in dynamic and autonomous device environments.","PeriodicalId":518411,"journal":{"name":"2024 IEEE 21st Consumer Communications & Networking Conference (CCNC)","volume":"69 5","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2024-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140531930","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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