TNSM Searchable Index

Author(s)	Title	Year	Publication	Keywords
Chaofeng Lin, Jinchuan Tang, Shuping Dang, Gaojie Chen	Priority-Based Blockchain Packing for Dependent Industrial IoT Transactions	2025	Early Access	Time factors Industrial Internet of Things Blockchains	Blockchain plays a key role in establishing secure and decentralized Industrial Internet of Things (IIoT) systems. Currently, the dependent transactions generated by IIoT devices require a packing process to select a set of non-conflicted transactions, which results in significant delay and deviation of the transaction response time. In this paper, we propose a novel transaction packing algorithm named Priority-Pack to address the above issue. Firstly, we use directed acyclic graphs to model the dependent transactions in IIoT systems to establish the mathematical relationships between transaction priority and waiting time as well as dependencies. Secondly, we propose an algorithm to specify a higher priority to a transaction with longer waiting time without violating transaction dependencies. It eliminates the time required to traverse the subsets of transactions in other algorithms. Thirdly, to further reduce the response delay for transactions with the same priority level, we choose to first pack transactions with smaller sizes. We prove that this selection can achieve the lowest average response time. Finally, simulations are conducted to benchmark the Priority-Pack against the state-of-the-art algorithms including Fair-Pack and Random-Pack. The results demonstrate that Priority-Pack outperforms the others in terms of average response time and deviations.	10.1109/TNSM.2025.3527810
Akio Kawabata, Sanetora Hiragi, Bijoy Chand Chatterjee, Eiji Oki	Distributed Processing Network Design Scheme for Virtual Application Processing Platform	2025	Early Access	Delays Synchronization Servers	Delay-sensitive applications have been provided through a low-delay network utilizing multiple edge clouds. For applications that involve sharing status among multiple users, it is crucial to prevent longer communication delays for users who are farther from the application server compared to those who are closer. To address this issue, this paper proposes a distributed processing network design scheme for virtual processing platforms using low-delay networks and widely distributed servers. The proposed scheme introduces Tapl as a given parameter for correcting events in occurrence order. Events within Tapl delay are sorted in occurrence order. The proposed scheme can change its operation mode from a conservative synchronization to an optimistic synchronization depending on the setting of Tapl. The proposed scheme is formulated as a mixed-integer linear programming problem to determine users’ and servers’ distributed processing network configuration. We evaluate the proposed scheme on two different network topologies. Numerical results indicate that, depending on the setting of Tapl, the proposed scheme can reduce the maximum amount of memory used for rollback processes in optimistic synchronization-based applications or realize a conservative synchronization algorithm. The computation time under the condition of 1000 users is within a maximum of nine sec, an acceptable amount of time for preparation before starting a planned service. These results indicate that the proposed scheme realizes event order correction with excellent delay characteristics and applies to virtual processing platforms.	10.1109/TNSM.2025.3562208
Latif U. Khan, Waseem Ullah, Sami Muhaidat, Mohsen Guizani, Bechir Hamdaoui	Block Successive Upper-Bound Minimization for Resource Scheduling in Wireless Metaverse	2025	Early Access	Metaverse Sensors Costs	In recent years, there has been a rising trend towards emerging applications (e.g., brain-computer interaction and haptics-based autonomous cars) with diverse requirements. To effectively enable these applications via autonomous operation and intelligent analytics, one can use a metaverseFor more details on how a metaverse can enable emerging applications and architecture, please refer to khan2024ametaverse. In a metaverse, we have two spaces: (a) a meta space based on a virtual model that performs analysis and resource management and (b) a physical space comprised of real world entities. A metaverse effectively enables emerging applications by performing three main tasks: (a) distributed learning of metaverse models; (b) instantly serving the end-users; and (c) sensing of the physical environment and sharing it with the meta space for synchronized operation. To perform these tasks, efficient wireless resource management is needed. Therefore, a novel resource scheduling framework for the wireless metaverse to enable various applications is proposed. Our aim is to minimize the cost of learning and sensing in metaverse. Subsequently, we formulate a problem. Meanwhile, the reliability as well as latency constraints of the service-requesting devices/users will be fulfilled. We assign multiple resource blocks to learning and sensing devices/units, whereas we use a concept of puncturing for service-requesting devices/users upon arrival. We use a scheme that is based on block successive upper-bound minimization and convex optimization for solving our formulated problem. At the end, we use empirical cumulative distribution function vs. cost and cost vs. metaverse entities for numerical evaluations.	10.1109/TNSM.2025.3562516
Mengyuan Zhang, Juan Fang, Ziyi Teng, Yaqi Liu, Shen Wu	Joint DNN Partitioning and Task Offloading Based on Attention Mechanism-Aided Reinforcement Learning	2025	Early Access	Partitioning algorithms Computational modeling Artificial neural networks	The rapid advancement of artificial intelligence applications has resulted in the deployment of a growing number of deep neural networks (DNNs) on mobile devices. Given the limited computational capabilities and small battery capacity of these devices, supporting efficient DNN inference presents a significant challenge. This paper considers the joint design of DNN model partitioning and offloading under high-concurrent tasks scenarios. The primary objective is to accelerate DNN task inference and reduce computational delay. Firstly, we propose an innovative adaptive inference framework that partitions DNN models into interdependent sub-tasks through a hierarchical partitioning method. Secondly, we develop a delay prediction model based on a Random Forest (RF) regression algorithm to estimate the computational delay of each sub-task on different devices. Finally, we designed a high-performance DNN partitioning and task offloading method based on an attention mechanism-aided Soft Actor-Critic (AMSAC) algorithm. The bandwidth allocation for each user is determined by the attention mechanism based on the characteristics of the DNN tasks, and the Soft Actor-Critic algorithm is used for adaptive layer-level partitioning and offloading of the DNN model, reducing collaborative inference delay. Extensive experiments demonstrate that our proposed AMSAC algorithm effectively reduces DNN task inference latency cost and improves service quality.	10.1109/TNSM.2025.3561739
Vikash Kumar Bhardwaj, Aagat Shukla, Om Jee Pandey	Energy-Efficient Node Localization in Time-Varying UAV-RIS-Assisted and Cluster-Based IoT Networks	2025	Early Access	Interference Signal to noise ratio Location awareness	No abstract	10.1109/TNSM.2025.3561269
Ahsan Raza Khan, Habib Ullah Manzoor, Rao Naveed Bin Rais, Sajjad Hussain, Lina Mohjazi, Muhammad Ali Imran, Ahmed Zoha	Semantic-Aware Federated Blockage Prediction (SFBP) in Vision-Aided Next-Generation Wireless Network	2025	Early Access	Sensors Wireless sensor networks Semantics	Predicting signal blockages in millimetre-wave and terahertz networks is essential for enabling proactive handover (PHO) and ensuring seamless connectivity. Existing approaches utilising deep learning, multi-modal vision and wireless sensing data primarily depend on centralised model training. Although these techniques are effective, they come with high communication costs, inefficient bandwidth usage, and latency issues, which restrict their real-time applicability. This paper proposes a Semantic-Aware Federated Blockage Prediction (SFBP) framework, leveraging the lightweight computer vision technique MobileNetV3 for edge-based semantic extraction, lowering communication and computation costs. Furthermore, we introduce a Similarity-Driven Federated Averaging (SD-FedAVG) mechanism to enhance the robustness of the model aggregation process, effectively mitigating the impact of noisy updates and adversarial attacks. Our proposed SFBP framework achieves 97.1% blockage prediction accuracy, closely matching centralised learning methods, while reducing communication costs by 88.75% compared to centralised learning and by 57.87% compared to FL without semantic extraction. Moreover, on-device inference reduces the latency by 23% compared to centralised learning and 18% compared to FL without semantic extraction, improving real-time decision-making for PHO. Additionally, the SD-FedAVG mechanism improves prediction accuracy under noisy conditions, directly impacting the PHO by reducing the handover failure rate by 7%.	10.1109/TNSM.2024.3525338
Lijun He, Ziye Jia, Juncheng Wang, Erick Lansard, Zhu Han, Chau Yuen	Joint Power Allocation and Task Scheduling for Data Offloading in Non-Geostationary Orbit Satellite Networks	2025	Early Access	Satellites Resource management Scheduling	In Non-Geostationary Orbit Satellite Networks (NGOSNs) with a large number of battery-carrying satellites, proper power allocation and task scheduling are crucial to improving data offloading efficiency. In this work, we jointly optimize power allocation and task scheduling to achieve energy-efficient data offloading in NGOSNs. Our goal is to properly balance the minimization of the total energy consumption and the maximization of the sum weights of tasks. Due to the tight coupling between power allocation and task scheduling, we first derive the optimal power allocation solution to the joint optimization problem with any given task scheduling policy. We then leverage the conflict graph model to transform the joint optimization problem into an Integer Linear Programming (ILP) problem with any given power allocation strategy. We explore the unique structure of the ILP problem to derive an efficient semidefinite relaxation-based solution. Finally, we utilize the genetic framework to combine the above special solutions as a two-layer solution for the original joint optimization problem. Simulation results demonstrate that our proposed solution can properly balance the reduction of total energy consumption and the improvement of the sum weights of tasks, thus achieving superior system performance over the current literature.	10.1109/TNSM.2025.3561266
Xiaojun Zhang, Qing Liu, Bingyun Liu, Yuan Zhang, Jingting Xue	Dynamic Certificateless Outsourced Data Auditing Mechanism Supporting Multi-Ownership Transfer via Blockchain Systems	2025	Early Access	Cloud computing Servers Blockchains	Data auditing contributes to checking the integrity of outsourced data, promoting the vigorous development of cloud storage services. In actual scenarios, such as migration of electronic medical records or data transfer of enterprise mergers and acquisitions, it always require data auditing to help clients with dynamic data migration and integrity checking. In this paper, we present an efficient dynamic certificateless outsourced data auditing mechanism supporting multi-ownership transfer (CDA-MOT), addressing the issue of key escrow and without needing complex certificate management. By integrating a certificateless multi-signature on the same data file into the construction of a homomorphic authenticator based on the Lagrange inverse Multinomial theorem, CDA-MOT not only achieves integrity verification but also enables clients to transfer ownership rights and responsibilities for multi-ownership data in collaboration with cloud servers. Utilizing blockchain systems to store necessary data conversion and update records, as well as smart contracts to fulfill auditing tasks, CDA-MOT owns the characteristics of openness, transparency, accountability, and decentralized public auditing. Besides, CDA-MOT could be further applied in the extension of dynamic update operations, even if outsourced data have been transferred. The security analysis and performance evaluation have demonstrated the feasibility of CDA-MOT in the secure deployment of cloud storage.	10.1109/TNSM.2025.3525462
Mahdi Nouri, Sima Sobhi-Givi, Hamid Behroozi, Mahrokh G. Shayesteh, Md. Jalil Piran, Zhiguo Ding	Joint Slice Resource Allocation and Hybrid Beamforming with Deep Reinforcement Learning for NOMA based Vehicular 6G Communications	2025	Early Access	Ultra reliable low latency communication Resource management NOMA	The escalating demand for high data rates and dependable communications in forthcoming wireless networks has led to the exploration of innovative solutions. Among these, the fusion of millimeter-wave (mmWave) communications and non-orthogonal multiple access (NOMA) holds significant promise. This paper delves into the optimization of hybrid mmWave-NOMA networks coexisting with Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) communications, accommodating ultra-reliable low-latency communication (uRLLC) and enhanced mobile broadband (eMBB) services. Our aim is to maximize system spectral efficiency (SE) and energy efficiency (EE) while ensuring fairness among users in terms of signal-to-leakage-and-noise ratio (SLNR). To tackle the intricate optimization challenges, we decompose them into two sequential sub-problems: power allocation and beamforming design, alongside resource block (RB) allocation. We advocate the application of deep reinforcement learning (DRL) algorithms to jointly optimize these sub-problems. Extensive evaluations demonstrate that RL-based approaches effectively enhance SE, EE, fairness, and resource utilization in the mmWave-NOMA network.	10.1109/TNSM.2025.3561251
Hnin Pann Phyu, Diala Naboulsi, Razvan Stanica	ICE-CREAM: multI-agent fully CooperativE deCentRalizEd frAMework for Energy Efficiency in RAN Slicing	2025	Early Access	Quality of service Base stations Energy efficiency	Network slicing is one of the major catalysts proposed to turn future telecommunication networks into versatile service platforms. Along with its benefits, network slicing is introducing new challenges in the development of sustainable network operations, as it entails a higher energy consumption compared to non-sliced networks.Using a sliced architecture, which includes guaranteeing the communication and computation requirements for each slice, is essential for operators to provide a satisfying user quality of service (QoS) in a multi-service network. At the same time, building sustainable mobile networks, with the least amount of resources used, is crucial today, for both economic and environmental reasons. As a result, mobile operators need to find a middle ground between these two objectives – a tough nut considering they are both antithetical and important. In this light, we investigate a joint slice activation/deactivation and user association problem, with the aim of minimizing energy consumption and maximizing the QoS. The proposed multI-agent fully CooperativE deCentRalizEd frAMework (ICE-CREAM) addresses the formulated joint problem, with agents acting at two different granularity levels. Not only all the agents can access the shared information with their direct neighbors, but also they are trained with one global reward, which is an ideal approach in multi-agent cooperative settings. We evaluate ICE-CREAM using a real-world dataset that captures the spatio-temporal consumption of three different mobile services in France. Experimental results demonstrate that the proposed solution provides more than 30% energy efficiency improvement compared to a configuration where all the slice instances are always active while maintaining the same level of QoS. From a broader perspective, our work explicitly shows the impact of prioritizing the energy over QoS, and vice versa.	10.1109/TNSM.2024.3524503
Tao Huang, Jingyuan Liu, Zheng Chang, Yao Wei, Xu Zhao, Ying-Chang Liang	Energy Efficient Spectrum Sharing and Resource Allocation for 6G Air-Ground Integrated Networks	2025	Early Access	Resource management Autonomous aerial vehicles Interference	In this paper, we investigate the spectrum sharing and resource allocation scheme for air-ground integrated wireless network which consists of multiple unmanned aerial vehicles (UAVs) and a high altitude platform (HAP). We consider the UAVs are required to provide services or execute certain missions in the area that HAP owns the spectrum and other resources. Correspondingly, we propose an energy efficient spectrum sharing and resource allocation scheme so that the UAVs can flexibly utilize the radio resources within the area without degrading the quality of service (QoS) of the HAP. In the proposed scheme, we jointly optimize pricing of spectrum and transmit power to maximize the utility of both the HAP and UAVs in the considered system in an energy efficient manner. A game theoretic approach is then presented to find the spectrum sharing and resource allocation strategies for both HAP and UAVs and the problem has been addressed via convex optimization. Our extensive simulations demonstrate marked improvements in system utility, spectrum and energy efficiency, and also highlight the effectiveness of the proposed scheme.	10.1109/TNSM.2025.3527651
Huili Liu, Yinglong Ma, Chenqi Guo, Xiaofeng Liu, Tingdong Wang	MOHFL: Multi-Level One-Shot Hierarchical Federated Learning With Enhanced Model Aggregation Over Non-IID Data	2025	Early Access	Data models Training Servers	Hierarchical federated learning (HFL) is a privacy-preserving distributed machine learning framework with a client-edge-cloud hierarchy, where multiple edge servers perform partial model aggregation to reduce costly communication with the cloud server. Nevertheless, most existing HFL methods require extensive iterative communication and public datasets, which not only increase communication overhead but also raise privacy and security concerns. Moreover, non-independent and identically distributed (non-IID) data among devices can significantly impact the accuracy of the global model in HFL. To address these challenges, we propose a multi-level one-shot HFL framework (MOHFL), which aims to improve the performance of the global model in a single communication round. Specifically, we employ conditional variational autoencoders (CVAEs) as local models and use the aggregated decoders to generate an IID training set for the global model, thereby mitigating the negative impact of non-IID data. We improve the performance of CVAEs under different levels of data heterogeneity through a dominant class-based data selection method. Subsequently, an edge aggregation scheme based on multi-teacher knowledge distillation and contrastive learning is proposed to aggregate the knowledge from local decoders to edge decoders. Extensive experiments on four real-world datasets demonstrate that MOHFL is very competitive against four state-of-the-art baselines under various settings.	10.1109/TNSM.2025.3560629
Xinyu Yuan, Yan Qiao, Zhenchun Wei, Zeyu Zhang, Minyue Li, Pei Zhao, Rongyao Hu, Wenjing Li	Diffusion Models Meet Network Management: Improving Traffic Matrix Analysis With Diffusion-Based Approach	2025	Early Access	Estimation Routing Training	Due to network operation and maintenance relying heavily on network traffic monitoring, traffic matrix analysis has been one of the most crucial issues for network management related tasks. However, it is challenging to reliably obtain the precise measurement in computer networks because of the high measurement cost, and the unavoidable transmission loss. Although some methods proposed in recent years allowed estimating network traffic from partial flow-level or link-level measurements, they often perform poorly for traffic matrix estimation nowadays. Despite strong assumptions like low-rank structure and the prior distribution, existing techniques are usually task-specific and tend to be significantly worse as modern network communication is extremely complicated and dynamic. To address the dilemma, this paper proposed a diffusion-based traffic matrix analysis framework named Diffusion-TM, which leverages problem-agnostic diffusion to notably elevate the estimation performance in both traffic distribution and accuracy. The novel framework not only takes advantage of the powerful generative ability of diffusion models to produce realistic network traffic, but also leverages the denoising process to unbiasedly estimate all end-to-end traffic in a plug-and-play manner under theoretical guarantee. Moreover, taking into account that compiling an intact traffic dataset is usually infeasible, we also propose a two-stage training scheme to make our framework be insensitive to missing values in the dataset. With extensive experiments with real-world datasets, we illustrate the effectiveness of Diffusion-TM on several tasks. Moreover, the results also demonstrate that our method can obtain promising results even with 5% known values left in the datasets.	10.1109/TNSM.2025.3527442
Fangyu Zhang, Yuang Chen, Hancheng Lu, Yongsheng Huang	Network-Aware Reliability Modeling and Optimization for Microservice Placement	2025	Early Access	Reliability Software reliability Microservice architectures	Optimizing microservice placement to enhance the reliability of services is crucial for improving the service level of microservice architecture-based mobile networks and Internet of Things (IoT) networks. Despite extensive research on service reliability, the impact of network load and routing on service reliability remains understudied, leading to suboptimal models and unsatisfactory performance. To address this issue, we propose a novel network-aware service reliability model that effectively captures the correlation between network state changes and reliability. Based on this model, we formulate the microservice placement problem as an integer nonlinear programming problem, aiming to maximize service reliability. Subsequently, a service reliability-aware placement (SRP) algorithm is proposed to solve the problem efficiently. To reduce bandwidth consumption, we further discuss the microservice placement problem with the shared backup path mechanism and propose a placement algorithm based on the SRP algorithm using shared path reliability calculation, known as the SRP-S algorithm. Extensive simulations demonstrate that the SRP algorithm reduces service failures by up to 22% compared to the benchmark algorithms. By introducing the shared backup path mechanism, the SRP-S algorithm reduces bandwidth consumption by up to 64% compared to the SRP algorithm with the fully protected path mechanism. It also reduces service failures by up to 11% compared to the SRP algorithm with the shared backup mechanism.	10.1109/TNSM.2025.3562913
Ammar Kamal Abasi, Moayad Aloqaily, Mohsen Guizani	6G mmWave Security Advancements through Federated Learning and Differential Privacy	2025	Early Access	Millimeter wave communication 6G mobile communication Data models	This paper presents a new framework that integrates Federated Learning (FL) with advanced privacy-preserving mechanisms to enhance the security of millimeter-wave (mmWave) beam prediction systems in 6G networks. By decentralizing model training, the framework safeguards sensitive user information while maintaining high model accuracy, effectively addressing privacy concerns inherent in centralized Machine learning (ML) methods. Adaptive noise augmentation and differential privacy principles are incorporated to mitigate vulnerabilities in FL systems, providing a robust defense against adversarial threats such as the Fast Gradient Sign Method (FGSM). Extensive experiments across diverse scenarios, including adversarial attacks, outdoor environments, and indoor settings, demonstrate a significant 17.45% average improvement in defense effectiveness, underscoring the framework’s ability to ensure data integrity, privacy, and performance reliability in dynamic 6G environments. By seamlessly integrating privacy protection with resilience against adversarial attacks, the proposed solution offers a comprehensive and scalable approach to secure mmWave communication systems. This work establishes a critical foundation for advancing secure 6G networks and sets a benchmark for future research in decentralized, privacy-aware machine learning systems.	10.1109/TNSM.2025.3528235
Abdulsamet Dağaşan, Ezhan Karaşan	Resilient Multi-Hop Autonomous UAV Networks With Extended Lifetime for Multi-Target Surveillance	2025	Early Access	Autonomous aerial vehicles Relays Target tracking	Cooperative utilization of Unmanned Aerial Vehicles (UAVs) in public and military surveillance applications has attracted significant attention in recent years. Most UAVs are equipped with sensors and wireless communication equipment with limited ranges. Such limitations pose challenging problems to monitor mobile targets. This paper examines fulfilling surveillance objectives to achieve better coverage while building a resilient network between UAVs with an extended lifetime. The multiple target tracking problem is studied by including a relay UAV within the fleet whose trajectory is autonomously calculated in order to achieve a reliable connected network among all UAVs. Optimization problems are formulated for single-hop and multi-hop communications among UAVs. Three heuristic algorithms are proposed for multi-hop communications and their performances are evaluated. A hybrid algorithm, which dynamically switches between single-hop and multi-hop communications is also proposed. The effect of the time horizon considered in the optimization problem is also studied. Performance evaluation results show that the trajectories generated for the relay UAV by the hybrid algorithm can achieve network lifetimes that are within 95% of the maximum possible network lifetime which can be obtained if the entire trajectories of all targets were known a priori.	10.1109/TNSM.2025.3528495
Xingyu Yang, Jipeng Hou, Lei Xu, Liehuang Zhu	zkFabLedger: Enabling Privacy Preserving and Regulatory Compliance in Hyperledger Fabric	2025	Early Access	Blockchains Privacy Regulation	Preserving the privacy of transactions and ensuring the regulatory compliance of transactions are two important requirements for blockchain-based financial applications. However, these two requirements are somewhat contradictory. Techniques for protecting transaction privacy, such as data encryption and zero-knowledge proof, generally make it difficult to regulate and audit the transactions. In this paper, we propose a system named zkFabLedger which enhances both the privacy and the auditability of the classic permissioned blockchain platform Hyperledger Fabric. The proposed system utilizes commitments and non-interactive zero-knowledge proofs to hide the detailed information of transactions while enabling the endorsing peer nodes to verify the regulatory compliance of transactions. Transactions are recorded on table-structured ledgers, so that the regulator can perform complex auditing of transactions. Moreover, we utilize the ring signature scheme and the secret handshake protocol to ensure the anonymity of the transaction sender while enabling the regulator to trace the sender’s identity. Simulation results demonstrate that the proposed system can balance well between privacy, regulation and efficiency.	10.1109/TNSM.2024.3525045
Lo-Yao Yeh, Sheng-Po Tseng, Chia-Hsun Lu, Chih-Ya Shen	Auditable Homomorphic-Based Decentralized Collaborative AI with Attribute-based Differential Privacy	2025	Early Access	Servers Blockchains Data models	In recent years, the notion of federated learning (FL) has led to the new paradigm of distributed artificial intelligence (AI) with privacy preservation. However, most current FL systems suffer from data privacy issues due to the requirement of a trusted third party. Although some previous works introduce differential privacy to protect the data, however, it may also significantly deteriorate the model performance. To address these issues, we propose a novel decentralized collaborative AI framework, named Auditable Homomorphic-based Decentralised Collaborative AI (AerisAI), to improve security with homomorphic encryption and fine-grained differential privacy. Our proposed AerisAI directly aggregates the encrypted parameters with a blockchain-based smart contract to get rid of the need of a trusted third party. We also propose a brand-new concept for eliminating the negative impacts of differential privacy for model performance. Moreover, the proposed AerisAI also provides the broadcast-aware group key management based on ciphertext-policy attribute-based encryption (CP-ABE) to achieve fine-grained access control based on different service-level agreements. We provide a formal theoretical analysis of the proposed AerisAI as well as the functionality comparison with the other baselines. We also conduct extensive experiments on real datasets to evaluate the proposed approach. The experimental results indicate that our proposed AerisAI significantly outperforms the other state-of-the-art baselines.	10.1109/TNSM.2025.3529774
Shashank Motepalli, Hans-Arno Jacobsen	Decentralization in PoS Blockchain Consensus: Quantification and Advancement	2025	Early Access	Consensus protocol Blockchains Measurement	Decentralization is a foundational principle of permissionless blockchains, with consensus mechanisms serving a critical role in its realization. This study quantifies the decentralization of consensus mechanisms in proof-of-stake (PoS) blockchains using a comprehensive set of metrics, including Nakamoto coefficients, Gini, Herfindahl-Hirschman Index (HHI), Shapley values, and Zipf’s coefficient. Our empirical analysis across ten prominent blockchains reveals significant concentration of stake among a few validators, posing challenges to fair consensus. To address this, we introduce two alternative weighting models for PoS consensus: Square Root Stake Weight (SRSW) and Logarithmic Stake Weight (LSW), which adjust validator influence through non-linear transformations. Results demonstrate that SRSW and LSW models improve decentralization metrics by an average of 51% and 132%, respectively, supporting more equitable and resilient blockchain systems.	10.1109/TNSM.2025.3561098
Xiaonan Wang, Yajing Song	Personalized Preference and Social Attribute Based Data Sharing for Information-Centric IoT	2025	Early Access	Internet of Things Smart devices Performance evaluation	With the rapid increase in the number of smart devices connected to the Internet of Things (IoT), network traffic has imposed serious overload on backhaul networks and led to network congestion. Data sharing among IoT devices through multi-hop communication between smart devices is expected to ease increasing pressure of backhaul traffic. In this paper, we propose a personalized preference and social attribute based data sharing framework for information-centric IoT, aiming to improve success rates of data sharing among IoT devices and reduce data sharing delays. This framework proposes personalized preferences and social attributes to reduce data response time and avoid data delivery failures caused by obsolete FIB and broken reverse paths. The experiment results justify the advantages of the proposed framework in terms of data sharing success rates and delays.	10.1109/TNSM.2025.3529291