Pub Date : 2024-11-25DOI: 10.1109/OJCOMS.2024.3506214
Mumin Adam;Uthman Baroudi
The rapid advancement of Internet of Things (IoT) technology has transformed the digital landscape, enabling unprecedented connectivity between devices, people, and services. Traditionally, IoT-generated data was processed through centralized, cloud-based machine learning (ML) systems, raising significant privacy, security, and network bandwidth concerns. Federated Learning (FL) presents a viable alternative by transmitting only model parameters while preserving local data privacy. Despite the growing body of research, there remains a gap in comprehensive studies on FL-enabled IoT systems. This review provides an in-depth examination of the integration of FL with IoT, highlighting how FL enhances the efficiency, robustness, and adaptability of IoT systems. The paper introduces the foundational principles of FL, followed by an exploration of its key benefits in decentralized IoT applications. It presents a comparative analysis of FL-IoT architectures using quantitative metrics and proposes a taxonomy that clarifies the complexities and variations in FL-enabled IoT systems. The challenges of deploying FL in IoT environments are discussed, along with current trends and solutions aimed at overcoming these hurdles. Furthermore, the review explores the integration of FL with emerging technologies, including foundational models (FMs), green and sustainable 6th-generation (6G) IoT networks, and deep reinforcement learning (DRL), emphasizing their role in enhancing FL’s efficiency and resilience. It also covers FL frameworks and benchmarks, providing a valuable resource for researchers and practitioners in the field The article concludes by identifying promising research directions that are expected to drive future advancements in this dynamic and expanding field.
{"title":"Federated Learning for IoT: Applications, Trends, Taxonomy, Challenges, Current Solutions, and Future Directions","authors":"Mumin Adam;Uthman Baroudi","doi":"10.1109/OJCOMS.2024.3506214","DOIUrl":"https://doi.org/10.1109/OJCOMS.2024.3506214","url":null,"abstract":"The rapid advancement of Internet of Things (IoT) technology has transformed the digital landscape, enabling unprecedented connectivity between devices, people, and services. Traditionally, IoT-generated data was processed through centralized, cloud-based machine learning (ML) systems, raising significant privacy, security, and network bandwidth concerns. Federated Learning (FL) presents a viable alternative by transmitting only model parameters while preserving local data privacy. Despite the growing body of research, there remains a gap in comprehensive studies on FL-enabled IoT systems. This review provides an in-depth examination of the integration of FL with IoT, highlighting how FL enhances the efficiency, robustness, and adaptability of IoT systems. The paper introduces the foundational principles of FL, followed by an exploration of its key benefits in decentralized IoT applications. It presents a comparative analysis of FL-IoT architectures using quantitative metrics and proposes a taxonomy that clarifies the complexities and variations in FL-enabled IoT systems. The challenges of deploying FL in IoT environments are discussed, along with current trends and solutions aimed at overcoming these hurdles. Furthermore, the review explores the integration of FL with emerging technologies, including foundational models (FMs), green and sustainable 6th-generation (6G) IoT networks, and deep reinforcement learning (DRL), emphasizing their role in enhancing FL’s efficiency and resilience. It also covers FL frameworks and benchmarks, providing a valuable resource for researchers and practitioners in the field The article concludes by identifying promising research directions that are expected to drive future advancements in this dynamic and expanding field.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":"5 ","pages":"7842-7877"},"PeriodicalIF":6.3,"publicationDate":"2024-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10767353","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142858735","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-25DOI: 10.1109/OJCOMS.2024.3506481
Alexander James Fernandes;Ioannis N. Psaromiligkos
Channel estimation is one of the main challenges for reconfigurable intelligent surface (RIS) assisted communication systems with passive reflective elements due to the high number of parameters to estimate. In this paper, we consider channel estimation for a MIMO FD RIS-assisted wireless communication system and use tensor (multidimensional array) signal modelling techniques to estimate all channel state information (CSI) involving the self-interference, direct-path, and the RIS assisted channel links. We model the received signal as a tensor composed of two CANDECOMP/PARAFAC (CP) decomposition terms for the non-RIS and the RIS assisted links. Based on this model we extend the alternating least squares algorithm to jointly estimate all channels, then derive the corresponding Cramér-Rao Bounds (CRB). Numerical results show that compared to recent previous works which estimate the non-RIS and RIS links during separate training stages, our method provides a more accurate estimate by efficiently using all pilots transmitted throughout the full training duration without turning the RIS off when comparing the same number of total pilots transmitted. For a sufficient number of transmitted pilots, the proposed method’s accuracy comes close to the CRB for the RIS channels and attains the CRB for the direct-path and self-interference channels.
{"title":"Tensor Signal Modeling and Channel Estimation for Reconfigurable Intelligent Surface-Assisted Full-Duplex MIMO","authors":"Alexander James Fernandes;Ioannis N. Psaromiligkos","doi":"10.1109/OJCOMS.2024.3506481","DOIUrl":"https://doi.org/10.1109/OJCOMS.2024.3506481","url":null,"abstract":"Channel estimation is one of the main challenges for reconfigurable intelligent surface (RIS) assisted communication systems with passive reflective elements due to the high number of parameters to estimate. In this paper, we consider channel estimation for a MIMO FD RIS-assisted wireless communication system and use tensor (multidimensional array) signal modelling techniques to estimate all channel state information (CSI) involving the self-interference, direct-path, and the RIS assisted channel links. We model the received signal as a tensor composed of two CANDECOMP/PARAFAC (CP) decomposition terms for the non-RIS and the RIS assisted links. Based on this model we extend the alternating least squares algorithm to jointly estimate all channels, then derive the corresponding Cramér-Rao Bounds (CRB). Numerical results show that compared to recent previous works which estimate the non-RIS and RIS links during separate training stages, our method provides a more accurate estimate by efficiently using all pilots transmitted throughout the full training duration without turning the RIS off when comparing the same number of total pilots transmitted. For a sufficient number of transmitted pilots, the proposed method’s accuracy comes close to the CRB for the RIS channels and attains the CRB for the direct-path and self-interference channels.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":"5 ","pages":"7668-7684"},"PeriodicalIF":6.3,"publicationDate":"2024-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10767385","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142825838","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-25DOI: 10.1109/OJCOMS.2024.3506033
Prasoon Raghuwanshi;Onel Luis Alcaraz López;Neelesh B. Mehta;Hirley Alves;Matti Latva-Aho
Efficient Random Access (RA) is critical for enabling reliable communication in Industrial Internet of Things (IIoT) networks. Herein, we propose a deep reinforcement learning-based distributed RA scheme, entitled Neural Network-Based Bandit (NNBB), for the IIoT alarm scenario. In such a scenario, devices may detect a common critical event, and the goal is to ensure the alarm information is delivered successfully from at least one device. The proposed NNBB scheme is implemented at each device, where it trains itself online and establishes implicit inter-device coordination to achieve the common goal. We devise a procedure for acquiring a valuable context for NNBB, which then uses a deep neural network to process this context and let devices determine their action. Each possible transmission pattern, i.e., transmit channel(s) allocation, constitutes a feasible action. Our simulation results show that as the number of devices in the network increases, so does the performance gain of the NNBB compared to the Multi-Armed Bandit (MAB) RA benchmark. For instance, NNBB experiences a 7% success rate drop when there are four channels and the number of devices increases from 10 to 60, while MAB faces a 25% drop.
{"title":"Neural Network-Based Bandit: A Medium Access Control for the IIoT Alarm Scenario","authors":"Prasoon Raghuwanshi;Onel Luis Alcaraz López;Neelesh B. Mehta;Hirley Alves;Matti Latva-Aho","doi":"10.1109/OJCOMS.2024.3506033","DOIUrl":"https://doi.org/10.1109/OJCOMS.2024.3506033","url":null,"abstract":"Efficient Random Access (RA) is critical for enabling reliable communication in Industrial Internet of Things (IIoT) networks. Herein, we propose a deep reinforcement learning-based distributed RA scheme, entitled Neural Network-Based Bandit (NNBB), for the IIoT alarm scenario. In such a scenario, devices may detect a common critical event, and the goal is to ensure the alarm information is delivered successfully from at least one device. The proposed NNBB scheme is implemented at each device, where it trains itself online and establishes implicit inter-device coordination to achieve the common goal. We devise a procedure for acquiring a valuable context for NNBB, which then uses a deep neural network to process this context and let devices determine their action. Each possible transmission pattern, i.e., transmit channel(s) allocation, constitutes a feasible action. Our simulation results show that as the number of devices in the network increases, so does the performance gain of the NNBB compared to the Multi-Armed Bandit (MAB) RA benchmark. For instance, NNBB experiences a 7% success rate drop when there are four channels and the number of devices increases from 10 to 60, while MAB faces a 25% drop.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":"5 ","pages":"7511-7524"},"PeriodicalIF":6.3,"publicationDate":"2024-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10767384","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142777748","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-25DOI: 10.1109/OJCOMS.2024.3506219
Thakshanth Uthayakumar;Xianbin Wang
The perpetual efforts in supporting ever-growing QoS requirements have brought new challenges in 5G advanced and 6G networks such as increased spatial channel correlation, large delaydoppler spread, higher carrier frequency offset, and more complex multi-path signals. As a result, the orthogonality among spatial, time-frequency, and delay-doppler domain radio resources is often destroyed leading to non-orthogonal radio resources a common paradigm for modulation in next-generation networks. Such non-orthogonality degrees have become both transmitter-receiver pair specific and domain specific due to diverse channel conditions perceived by the UE involved. Furthermore, the operational cost of restoring orthogonality and demodulation varies across domains due to different synchronization and interference cancelation capabilities of receiver in different domains. To tackle these issues, we propose user-centric multi-dimensional modulation (UC-MDM) aiming to minimize receiver costs while supporting necessary data rates. Our situation-aware, cost-conscious UC-MDM optimizes resource separation across spatial, time-frequency, and delay-doppler domains, utilizing optimal resource combinations through multidimensional modulation in either spatial-time-frequency or spatial-delay-doppler domains. Simulation results under simultaneously varied domain specific non-orthogonality degrees validate that UC-MDM achieves required data rate with less operational cost from user-device compared to MIMO-OFDM and MIMO-OTFS systems.
{"title":"User-Centric Multi-Dimensional Modulation for Receiver Operational Cost Minimization in Non-Orthogonal Domains","authors":"Thakshanth Uthayakumar;Xianbin Wang","doi":"10.1109/OJCOMS.2024.3506219","DOIUrl":"https://doi.org/10.1109/OJCOMS.2024.3506219","url":null,"abstract":"The perpetual efforts in supporting ever-growing QoS requirements have brought new challenges in 5G advanced and 6G networks such as increased spatial channel correlation, large delaydoppler spread, higher carrier frequency offset, and more complex multi-path signals. As a result, the orthogonality among spatial, time-frequency, and delay-doppler domain radio resources is often destroyed leading to non-orthogonal radio resources a common paradigm for modulation in next-generation networks. Such non-orthogonality degrees have become both transmitter-receiver pair specific and domain specific due to diverse channel conditions perceived by the UE involved. Furthermore, the operational cost of restoring orthogonality and demodulation varies across domains due to different synchronization and interference cancelation capabilities of receiver in different domains. To tackle these issues, we propose user-centric multi-dimensional modulation (UC-MDM) aiming to minimize receiver costs while supporting necessary data rates. Our situation-aware, cost-conscious UC-MDM optimizes resource separation across spatial, time-frequency, and delay-doppler domains, utilizing optimal resource combinations through multidimensional modulation in either spatial-time-frequency or spatial-delay-doppler domains. Simulation results under simultaneously varied domain specific non-orthogonality degrees validate that UC-MDM achieves required data rate with less operational cost from user-device compared to MIMO-OFDM and MIMO-OTFS systems.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":"5 ","pages":"7628-7640"},"PeriodicalIF":6.3,"publicationDate":"2024-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10767382","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142825936","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-22DOI: 10.1109/OJCOMS.2024.3504852
Mudassar Liaq;Waleed Ejaz
The Internet of Things (IoT) applications generate large volumes of data, which needs to be processed securely, reliably, and promptly for effective decision-making. However, the limited processing capability of IoT devices is a significant bottleneck in processing these datasets. In scenarios like forest fire surveillance, flash flood alert systems, or wildlife activity tracking, where IoT devices are deployed in remote locations and only need coverage for a few weeks a year, thus deploying permanent base stations is not a feasible solution. One potential solution to overcome this challenge is to use Federated learning (FL) with unmanned aerial vehicle (UAV) as mobile edge computing (MEC) servers. FL enables collaborative model training across decentralized IoT devices by keeping data local, eliminating the need for centralized data collection. This approach is especially effective when IoT devices generate large volumes of data, making FL an ideal solution for data-sensitive, resource-constrained environments. In this paper, we propose a UAV-aided FL framework that utilizes the computation capacity of UAV-MEC to process some portion of the datasets from the straggling devices (devices which are unable to process their dataset in reasonable time and are lagging, increasing delay in the whole system). We also incorporate an IoT device importance and selection scheme to further improve system performance. We formulate an optimization problem to minimize system delay, considering UAV-MEC’s computation power, computation and communication power of IoT devices, and quality of service constraints. To solve the problem, we transform the proposed problem by introducing auxiliary variables and epigraph form. We then use the concurrent deterministic simplex with root relaxation algorithm. We also propose a deep reinforcement learning (DRL)-based solution to improve runtime complexity. Simulation results show the effectiveness of the proposed framework compared to existing approaches.
{"title":"Minimizing Delay in UAV-Aided Federated Learning for IoT Applications With Straggling Devices","authors":"Mudassar Liaq;Waleed Ejaz","doi":"10.1109/OJCOMS.2024.3504852","DOIUrl":"https://doi.org/10.1109/OJCOMS.2024.3504852","url":null,"abstract":"The Internet of Things (IoT) applications generate large volumes of data, which needs to be processed securely, reliably, and promptly for effective decision-making. However, the limited processing capability of IoT devices is a significant bottleneck in processing these datasets. In scenarios like forest fire surveillance, flash flood alert systems, or wildlife activity tracking, where IoT devices are deployed in remote locations and only need coverage for a few weeks a year, thus deploying permanent base stations is not a feasible solution. One potential solution to overcome this challenge is to use Federated learning (FL) with unmanned aerial vehicle (UAV) as mobile edge computing (MEC) servers. FL enables collaborative model training across decentralized IoT devices by keeping data local, eliminating the need for centralized data collection. This approach is especially effective when IoT devices generate large volumes of data, making FL an ideal solution for data-sensitive, resource-constrained environments. In this paper, we propose a UAV-aided FL framework that utilizes the computation capacity of UAV-MEC to process some portion of the datasets from the straggling devices (devices which are unable to process their dataset in reasonable time and are lagging, increasing delay in the whole system). We also incorporate an IoT device importance and selection scheme to further improve system performance. We formulate an optimization problem to minimize system delay, considering UAV-MEC’s computation power, computation and communication power of IoT devices, and quality of service constraints. To solve the problem, we transform the proposed problem by introducing auxiliary variables and epigraph form. We then use the concurrent deterministic simplex with root relaxation algorithm. We also propose a deep reinforcement learning (DRL)-based solution to improve runtime complexity. Simulation results show the effectiveness of the proposed framework compared to existing approaches.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":"5 ","pages":"7653-7667"},"PeriodicalIF":6.3,"publicationDate":"2024-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10764791","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142825886","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Despite the numerous advantages of aerial base stations (ABSs), including their relatively ease of deployment and inherent flexibility for relocation to adapt to highly dynamic networks, their service endurance is constrained by the limited capacity of their onboard batteries. To address this limiting factor, we explore the use of robotic aerial base stations (RABSs) that are equipped with grasping end-effectors able to anchor onto tall urban landforms such as lampposts. Energy-neutral anchoring conserves energy consumption by eliminating the need for hovering or flying during service time, thereby massively improving communication service endurance. In this paper, a joint RABS deployment and wireless backhauling scheme with the aim of maximizing served traffic is proposed to support future dynamic and densified wireless networks experiencing unprecedented data traffic growth. To meet this significant increase in traffic demand, which requires substantial bandwidth for both access and backhaul, we employ sub-Terahertz (sub-THz) band communication due to its ultra-wide spectrum resources. Given the sub-THz band’s susceptibility to blockages and severe propagation losses due to absorption, we propose a multi-hop wireless scheme to extend network coverage. The optimization interplay between RABS grasping locations, route flow control, and sub-band allocation to ensure link capacity, is framed as a robust optimization problem aimed at maximizing served traffic with a cardinality-constrained uncertainty set. Since the grasping locations are determined from all candidate locations, the number of corresponding candidate routes can significantly increase with the network size in this multi-hop enabled network. In this work, we propose a column generation (CG) based algorithm to tackle the curse of dimensionality due to the exponentially increased number of candidate routes. To this end, a near-optimal decision making is proposed with significantly reduced computational complexity. A wide set of numerical investigations demonstrates the superiority of the proposed network scheme over baseline schemes. For instance, the aggregated served traffic demand improved by 125% compared to a network with fixed small cell deployment which could be considered as the nominal use case and a common deployment option for increasing network capacity.
{"title":"Unlocking Sub-THz by Robotic Aerial Base Stations: Joint Deployment and Wireless Backhaul Routing","authors":"Wen Shang;Yuan Liao;Vasilis Friderikos;Halim Yanikomeroglu","doi":"10.1109/OJCOMS.2024.3505435","DOIUrl":"https://doi.org/10.1109/OJCOMS.2024.3505435","url":null,"abstract":"Despite the numerous advantages of aerial base stations (ABSs), including their relatively ease of deployment and inherent flexibility for relocation to adapt to highly dynamic networks, their service endurance is constrained by the limited capacity of their onboard batteries. To address this limiting factor, we explore the use of robotic aerial base stations (RABSs) that are equipped with grasping end-effectors able to anchor onto tall urban landforms such as lampposts. Energy-neutral anchoring conserves energy consumption by eliminating the need for hovering or flying during service time, thereby massively improving communication service endurance. In this paper, a joint RABS deployment and wireless backhauling scheme with the aim of maximizing served traffic is proposed to support future dynamic and densified wireless networks experiencing unprecedented data traffic growth. To meet this significant increase in traffic demand, which requires substantial bandwidth for both access and backhaul, we employ sub-Terahertz (sub-THz) band communication due to its ultra-wide spectrum resources. Given the sub-THz band’s susceptibility to blockages and severe propagation losses due to absorption, we propose a multi-hop wireless scheme to extend network coverage. The optimization interplay between RABS grasping locations, route flow control, and sub-band allocation to ensure link capacity, is framed as a robust optimization problem aimed at maximizing served traffic with a cardinality-constrained uncertainty set. Since the grasping locations are determined from all candidate locations, the number of corresponding candidate routes can significantly increase with the network size in this multi-hop enabled network. In this work, we propose a column generation (CG) based algorithm to tackle the curse of dimensionality due to the exponentially increased number of candidate routes. To this end, a near-optimal decision making is proposed with significantly reduced computational complexity. A wide set of numerical investigations demonstrates the superiority of the proposed network scheme over baseline schemes. For instance, the aggregated served traffic demand improved by 125% compared to a network with fixed small cell deployment which could be considered as the nominal use case and a common deployment option for increasing network capacity.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":"5 ","pages":"7582-7597"},"PeriodicalIF":6.3,"publicationDate":"2024-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10766414","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142825904","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-21DOI: 10.1109/OJCOMS.2024.3504403
Yong Zhou;Hong Lei;Zijian Bao
Charity donations are a critical mechanism for social resource distribution. However, traditional donation systems, typically centralized, are prone to issues such as data redundancy, vulnerability to single-point failures, and a deficiency in transparency and traceability. Although blockchain-based donation programs have emerged to address trust issues inherent in centralized models, they often neglect critical security concerns like privacy protection and identity authentication. This paper introduces Eisdspa, a blockchain-based donation system designed to offer identity authentication, auditability, and privacy protection. Specifically, we introduce an identity credential system that facilitates anonymous donations, shielding the identities of both donors and donees through the use of BBS+ signatures and zero-knowledge proofs of knowledge (ZKPoKs). Additionally, we ensure the integrity of goods donations by offering robust auditability and protecting user privacy with Pedersen commitments and ZKPoKs. We formally define the privacy aspects of Eisdspa and conduct a security analysis of the system under the random oracle model. A prototype implementation of the scheme, along with a comparative analysis with existing solutions, highlights the benefits of Eisdspa. Moreover, we assess the computational efficiency of Eisdspa, with experimental results indicating its high performance in computational overhead.
{"title":"Eisdspa: An Efficient and Secure Blockchain-Based Donation Scheme With Privacy Protection and Auditability","authors":"Yong Zhou;Hong Lei;Zijian Bao","doi":"10.1109/OJCOMS.2024.3504403","DOIUrl":"https://doi.org/10.1109/OJCOMS.2024.3504403","url":null,"abstract":"Charity donations are a critical mechanism for social resource distribution. However, traditional donation systems, typically centralized, are prone to issues such as data redundancy, vulnerability to single-point failures, and a deficiency in transparency and traceability. Although blockchain-based donation programs have emerged to address trust issues inherent in centralized models, they often neglect critical security concerns like privacy protection and identity authentication. This paper introduces Eisdspa, a blockchain-based donation system designed to offer identity authentication, auditability, and privacy protection. Specifically, we introduce an identity credential system that facilitates anonymous donations, shielding the identities of both donors and donees through the use of BBS+ signatures and zero-knowledge proofs of knowledge (ZKPoKs). Additionally, we ensure the integrity of goods donations by offering robust auditability and protecting user privacy with Pedersen commitments and ZKPoKs. We formally define the privacy aspects of Eisdspa and conduct a security analysis of the system under the random oracle model. A prototype implementation of the scheme, along with a comparative analysis with existing solutions, highlights the benefits of Eisdspa. Moreover, we assess the computational efficiency of Eisdspa, with experimental results indicating its high performance in computational overhead.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":"5 ","pages":"7498-7510"},"PeriodicalIF":6.3,"publicationDate":"2024-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10759694","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142777749","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The integration of Positioning, Navigation, and Timing (PNT) services within the 5G non-terrestrial networks (NTN) infrastructure is necessary to eliminate the need for a GNSS receiver in the user terminal. Using the positioning reference signal (PRS) in an NTN scenario presents significant challenges, such as interference analysis from the transmission of multiple PRS signals. This study provides a stochastic model for the interference generated by PRS transmissions in a 5G NTN scenario. This model has been derived empirically from a Monte Carlo simulator designed specifically for this purpose, showing that the distribution that best fits the interference is a Generalized Extreme Value (GEV) distribution. The parameters of this distribution are also modeled based on the PRS configuration. Therefore, a designer can use this model to evaluate the probability of encountering certain levels of interference.
{"title":"Interference Analysis and Modeling of Positioning Reference Signals in 5G NTN","authors":"Alejandro Gonzalez-Garrido;Jorge Querol;Henk Wymeersch;Symeon Chatzinotas","doi":"10.1109/OJCOMS.2024.3503692","DOIUrl":"https://doi.org/10.1109/OJCOMS.2024.3503692","url":null,"abstract":"The integration of Positioning, Navigation, and Timing (PNT) services within the 5G non-terrestrial networks (NTN) infrastructure is necessary to eliminate the need for a GNSS receiver in the user terminal. Using the positioning reference signal (PRS) in an NTN scenario presents significant challenges, such as interference analysis from the transmission of multiple PRS signals. This study provides a stochastic model for the interference generated by PRS transmissions in a 5G NTN scenario. This model has been derived empirically from a Monte Carlo simulator designed specifically for this purpose, showing that the distribution that best fits the interference is a Generalized Extreme Value (GEV) distribution. The parameters of this distribution are also modeled based on the PRS configuration. Therefore, a designer can use this model to evaluate the probability of encountering certain levels of interference.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":"5 ","pages":"7567-7581"},"PeriodicalIF":6.3,"publicationDate":"2024-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10759698","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142825800","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-20DOI: 10.1109/OJCOMS.2024.3504353
Eman Abouelkheir
Unmanned aerial vehicles (UAVs) have gained significant attention in robotics research during the past decade, despite their presence dating back to 1915. Unmanned Aerial Vehicles (UAVs) are capable of efficiently and successfully carrying out a range of tasks. As a result, the use of many UAVs to fulfill a specific mission has grown into a popular area of research. Researchers have conducted investigations on the use of numerous UAVs in various fields such as remote sensing, disaster relief, force protection, military warfare, and surveillance. Efficiency and robustness are crucial factors for carrying out key operations. Multiple groups of UAVs, through appropriate interaction and concerted procedures, can achieve these objectives. The unpredictable features of UAVs and their reliance on unprotected and widely available wireless networks create challenges in establishing secure communication between a private edge cloud and a UAV. Consequently, secret UAV networks that utilize edge computing necessitate supplementary precautions to safeguard their networks. This research paper talks about a simple, lightweight, certificate-free, heterogeneous online/offline aggregate signing scheme called CL-PFASC. It comes from the discrete logarithm problem. The concert scheme enables UAVs to communicate with a GS without the need for a bilinear coupling operation. We classify the UAVs as identity-based cryptography (IBC) and the ground station GS as public-key infrastructure (PKI). We verify the security features of the suggested scheme using a formal security evaluation method, the random oracle model, under confidentiality and unforgeability. We also evaluate its communication and computation costs and compare them to those of similar existing schemes. The performance and security study indicate that the suggested approach improves both efficiency and security.
{"title":"Securing Unmanned Aerial Vehicles Networks Using Pairing Free Aggregate Signcryption Scheme","authors":"Eman Abouelkheir","doi":"10.1109/OJCOMS.2024.3504353","DOIUrl":"https://doi.org/10.1109/OJCOMS.2024.3504353","url":null,"abstract":"Unmanned aerial vehicles (UAVs) have gained significant attention in robotics research during the past decade, despite their presence dating back to 1915. Unmanned Aerial Vehicles (UAVs) are capable of efficiently and successfully carrying out a range of tasks. As a result, the use of many UAVs to fulfill a specific mission has grown into a popular area of research. Researchers have conducted investigations on the use of numerous UAVs in various fields such as remote sensing, disaster relief, force protection, military warfare, and surveillance. Efficiency and robustness are crucial factors for carrying out key operations. Multiple groups of UAVs, through appropriate interaction and concerted procedures, can achieve these objectives. The unpredictable features of UAVs and their reliance on unprotected and widely available wireless networks create challenges in establishing secure communication between a private edge cloud and a UAV. Consequently, secret UAV networks that utilize edge computing necessitate supplementary precautions to safeguard their networks. This research paper talks about a simple, lightweight, certificate-free, heterogeneous online/offline aggregate signing scheme called CL-PFASC. It comes from the discrete logarithm problem. The concert scheme enables UAVs to communicate with a GS without the need for a bilinear coupling operation. We classify the UAVs as identity-based cryptography (IBC) and the ground station GS as public-key infrastructure (PKI). We verify the security features of the suggested scheme using a formal security evaluation method, the random oracle model, under confidentiality and unforgeability. We also evaluate its communication and computation costs and compare them to those of similar existing schemes. The performance and security study indicate that the suggested approach improves both efficiency and security.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":"5 ","pages":"7552-7566"},"PeriodicalIF":6.3,"publicationDate":"2024-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10759680","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142825938","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-19DOI: 10.1109/OJCOMS.2024.3501322
Sunjae Kim;Wonjun Lee
Container orchestrators like Kubernetes utilize packet encapsulation to construct container overlay networks, allowing for transparent communications among containers. While providing flexible connectivity with a minimal requirement for host machines, the inadvertent interplay with encapsulationinduced extra in-host hops and TCP’s default loss detection algorithm, Recent Acknowledgment (RACK), causes irregular in-sender reordering and spurious retransmissions (SRs). For a deeper understanding of the problem, we examine the behavior of RACK loss detection algorithm in the context of the packet datapath of the container overlay networks, which is not presumed by the RACK standard. Then we quantify the SRs of TCP using RACK in the production-level container overlay networks. Based on the in-depth analysis of the root causes of the SRs in container overlay networking, we derive a SR model induced by in-sender reordering and present a compensation mechanism for in-sender reordering. Our prototype implementation, centered around eBPF running in the Linux kernel, validates that the proposed compensation mechanism reduces SRs by up to 98.6% while maintaining the latency and throughput overhead below 2.3%.
{"title":"In-Sender Reordering Compensation for RACK in Container Overlay Networks","authors":"Sunjae Kim;Wonjun Lee","doi":"10.1109/OJCOMS.2024.3501322","DOIUrl":"https://doi.org/10.1109/OJCOMS.2024.3501322","url":null,"abstract":"Container orchestrators like Kubernetes utilize packet encapsulation to construct container overlay networks, allowing for transparent communications among containers. While providing flexible connectivity with a minimal requirement for host machines, the inadvertent interplay with encapsulationinduced extra in-host hops and TCP’s default loss detection algorithm, Recent Acknowledgment (RACK), causes irregular in-sender reordering and spurious retransmissions (SRs). For a deeper understanding of the problem, we examine the behavior of RACK loss detection algorithm in the context of the packet datapath of the container overlay networks, which is not presumed by the RACK standard. Then we quantify the SRs of TCP using RACK in the production-level container overlay networks. Based on the in-depth analysis of the root causes of the SRs in container overlay networking, we derive a SR model induced by in-sender reordering and present a compensation mechanism for in-sender reordering. Our prototype implementation, centered around eBPF running in the Linux kernel, validates that the proposed compensation mechanism reduces SRs by up to 98.6% while maintaining the latency and throughput overhead below 2.3%.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":"5 ","pages":"7467-7482"},"PeriodicalIF":6.3,"publicationDate":"2024-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10758302","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142777746","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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