TNSM Searchable Index

Author(s)	Title	Year	Publication	Keywords
Kang Liu, Jianchen Hu, Donglai Ma, Xiaoyu Cao, Yuzhou Zhou, Lei Zhu, Li Su, Wenli Zhou, Xueqi Wu, Feng Gao	Topology-Aware Virtual Machine Placement through the Buffer Migration Mechanism	2026	Early Access	Central Processing Unit Filtering Filters	The virtual machine (VM) placement considering the topology constraints is difficult because the unpredictable topological VMs raise additional structural requirements (including the affinity, anti-affinity and fault-domain) on the resource pool. Thus, the service level agreement (SLA) can be violated even when the occupancy of the resource pool is quite modest. In order to solve this problem, we propose an efficient buffer-migration-based heuristic online algorithm. First, we build an integer programming model for the topology-aware VM placement problem. Second, we propose a hierarchical resource-preserving online approach, where the Rack and physical machine (PM) nodes are selected in the upper and lower layers respectively. Finally, we utilize the buffer to place and migrate the unfitted VMs to enhance the capacity of the resource pool. The proposed approach is tested with high proportional topological VM requests (nearly 60%) in the resource pool with the scale of 500, 1000 and 1500 PMs. The results show that our online approach (with unknown upcoming VM information) can achieve more than 85% of the performance for the offline approach (with complete upcoming VM information). The latency is lower than 5ms per VM.	10.1109/TNSM.2026.3678976
Amin Mohajer, Abbas Mirzaei, Mostafa Darabi, Xavier Fernando	Joint SLA-Aware Task Offloading and Adaptive Service Orchestration with Graph-Attentive Multi-Agent Reinforcement Learning	2026	Early Access	Quality of service Resource management Observability	Coordinated service offloading is essential to meet Quality-of-Service (QoS) targets under non-stationary edge traffic. Yet conventional schedulers lack dynamic prioritization, causing deadline violations for delay-sensitive, lower-priority flows. We present PRONTO, a multi-agent framework with centralized training and decentralized execution (CTDE) that jointly optimizes SLA-aware offloading and adaptive service orchestration. PRONTO builds on Twin Delayed Deep Deterministic Policy Gradient (TD3) and incorporates spatiotemporal, topology-aware graph attention with top-K masking and temperature scaling to encode neighborhood influence at linear coordination cost. Gated Recurrent Units (GRUs) filter temporal features, while a hybrid reward couples task urgency, SLA satisfaction, and utilization costs. A priority-aware slicing policy divides bandwidth and compute between latency-critical and throughput-oriented flows. To improve robustness, we employ stability regularizers (temporal smoothing and confidence-weighted neighbor alignment), mitigating action jitter under bursts. Extensive evaluations show superior QoS and channel utilization, with up to 27.4% lower service delay and over 18% higher SLA Satisfaction Rate (SSR) compared with strong baselines.	10.1109/TNSM.2026.3673188
Deemah H. Tashman, Soumaya Cherkaoui	Trustworthy AI-Driven Dynamic Hybrid RIS: Joint Optimization and Reward Poisoning-Resilient Control in Cognitive MISO Networks	2026	Early Access	Reconfigurable intelligent surfaces Reliability Optimization	Cognitive radio networks (CRNs) are a key mechanism for alleviating spectrum scarcity by enabling secondary users (SUs) to opportunistically access licensed frequency bands without harmful interference to primary users (PUs). To address unreliable direct SU links and energy constraints common in next-generation wireless networks, this work introduces an adaptive, energy-aware hybrid reconfigurable intelligent surface (RIS) for underlay multiple-input single-output (MISO) CRNs. Distinct from prior approaches relying on static RIS architectures, our proposed RIS dynamically alternates between passive and active operation modes in real time according to harvested energy availability. We also model our scenario under practical hardware impairments and cascaded fading channels. We formulate and solve a joint transmit beamforming and RIS phase optimization problem via the soft actor-critic (SAC) deep reinforcement learning (DRL) method, leveraging its robustness in continuous and highly dynamic environments. Notably, we conduct the first systematic study of reward poisoning attacks on DRL agents in RIS-enhanced CRNs, and propose a lightweight, real-time defense based on reward clipping and statistical anomaly filtering. Numerical results demonstrate that the SAC-based approach consistently outperforms established DRL base-lines, and that the dynamic hybrid RIS strikes a superior trade-off between throughput and energy consumption compared to fully passive and fully active alternatives. We further show the effectiveness of our defense in maintaining SU performance even under adversarial conditions. Our results advance the practical and secure deployment of RIS-assisted CRNs, and highlight crucial design insights for energy-constrained wireless systems.	10.1109/TNSM.2026.3660728
Yuchen Wu, Muyu Mei, Li Feng, Jiangtao Wang, Chunhui Feng, Xu Bao, Mingwu Yao	Deep Reinforcement Learning-based Cluster Selection for Network-layer Performance Guarantee in Federated Learning	2026	Early Access		Federated learning (FL) is a privacy-preserving technique that enables local model training on devices without raw data sharing. However, a critical challenge in FL lies in the communication requirement of uploading the trained models to servers, which can be hindered by interference from ambient devices, particularly in unreliable wireless environments. To address this, hierarchical FL (HFL) introduces an additional intermediate layer where the edge server performs work aggregation from the devices nearby, aiming at reducing the communication load and improving the efficiency of model training. However, existing approaches suffer from two critical limitations. First, they fail to fully quantify the impact of device competition-induced interference on transmission performance, which leads to unacceptably high upload latency and low success upload probability (SUP). Second, they lack a targeted optimization strategy to balance model accuracy and transmission efficiency under dynamic interference conditions. To address these critical limitations and mitigate their adverse impacts on FL performance, we take these gaps as the core motivation of our work and propose a targeted solution. Specifically, we first model the network as a two-layer binomial point process (BPP), which allows us to analyze the network-layer performance and calculate the SUP for the trained model. Based on this model, we propose optimizing cluster selection to balance accuracy and latency, thereby enhancing overall FL performance. We formulate this optimization as a Markov decision process (MDP) and solve it using a twin-delayed deep deterministic policy gradient (TD3)-based cluster selection algorithm (CS-TD3). In addition, to guarantee network-layer performance and enhance the efficiency of HFL, we employ an experimental exhaustive search algorithm to find the best solution within a limited range. The experimental results show that our algorithm overperforms other commonly-used algorithms in terms of HFL accuracy and model transmission latency, achieving a 10.95% improvement over the other methods.	10.1109/TNSM.2026.3680350
Xinyue Zhang, Xuan Zhou, Jie Ma, Zeqi Li, Feng He	Interference-Aware Multi-Metric Delay Evaluation and Optimization for Switched Networks	2026	Early Access		Switched networks are essential to modern real-time systems, where packet delays must be tightly bounded with minimal variation. Traditional delay analysis often focuses on worst-case bounds, but may overlook delay jitter induced by fine-grained inter-flow interference, which can degrade real-time performance and stability. Existing routing schemes typically rely on proxy indicators such as link load or path length, offering limited explicit control over delay and jitter behavior. To address these limitations, we propose an interference-aware delay evaluation and optimization framework that models the encounter interval and magnitude of flow interference at the packet level. From this, we derive worst-case delay, average delay, and delay jitter, and integrate these metrics into a unified, tunable optimization objective. We design a K-shortest-path genetic algorithm to jointly reduce them. Experimental results over multiple traffic loads demonstrate consistent improvements in delay and jitter performance, indicating that the proposed approach is scalable and practical for delay-sensitive and stability-critical switched networks.	10.1109/TNSM.2026.3680250
Luyao Jiang, Xinguo Ming, Mengli Wei	Privacy Decentralized Online Federated Learning for Smart Healthcare Service Systems	2026	Early Access		The Smart Healthcare Service Systems (SHSS) aim to integrate decentralized healthcare institutions, intelligent technologies, and end users into a cyber-physical system that enables high-quality medical decision-making. However, the sensitive nature of healthcare data presents significant privacy and security challenges, which hinder effective collaboration among healthcare providers. Moreover, existing research lacks a comprehensive theoretical framework that spans the full pipeline from data acquisition to intelligent decision services. To address these challenges, we propose a theoretical framework for SHSS that systematically analyzes data processing and user demand to establish the goals of secure, stable, and adaptive collaborative learning. Guided by these goals, a Decentralized Online Federated Learning (DOFL) network model is tailored for SHSS, where participating institutions interact through a decentralized federated learning structure. Building on this model, we design DP-DOOR (Differentially Private Decentralized Online Federated Learning with One-Point Residual Feedback), a fully decentralized algorithm that supports row-stochastic communication topologies, accommodating practical limitations where bidirectional synchronization is often infeasible. DP-DOOR ensures data privacy through differential privacy (DP) mechanisms and achieves efficient gradient estimation using a one-point residual feedback (OPRF) approach. Theoretical analysis shows that DP-DOOR provides ϵ-DP guarantees and achieves sub-linear regret. Experimental evaluations on diverse real-world medical datasets under both IID and non-IID settings demonstrate the algorithm’s robustness and effectiveness in enabling secure, decentralized collaboration and enhancing adaptability in dynamic healthcare environments.	10.1109/TNSM.2026.3680310
Julien Ali El Amine, Nour El Houda Nouar, Olivier Brun	Online Network Slice Deployment across Multiple Domains under Trust Constraints	2026	Early Access		Network slicing across multiple administrative domains raises two coupled challenges: enforcing slice-specific trust constraints while enabling fast online admission and placement decisions. This paper considers a multi-domain infrastructure where each slice request specifies a VNF chain, resource demands, and a set of (un)trusted operators, and formulates the problem as a Node–Link (NL) integer program to obtain an optimal benchmark, before proposing a Path–Link (PL) formulation that pre-generates trust and order-compliant candidate paths to enable real-time operation. To mitigate congestion, resource prices are made dynamic using a Kleinrock congestion function, which inflates marginal costs as utilization approaches capacity, steering traffic away from hotspots. Extensive simulations across different congestion levels and slice types show that: (i) PL closely tracks NL with negligible gaps at low load and moderate gaps otherwise, (ii) dynamic pricing significantly reduces blocking under scarce resources, and (iii) PL reduces computation time by about 3×–6× compared to NL, remaining within a few seconds even at high load. These results demonstrate that the proposed PL and dynamic pricing framework achieves near-optimal performance with practical runtime for online multi-domain slicing under trust constraints.	10.1109/TNSM.2026.3679794
Xiaolong Wang, Haipeng Yao, Lin Zhu, Wenji He, Wei Zhang, Mohsen Guizani	Joint Optimization of Routing and Scheduling in Cross-Domain Deterministic Networks	2026	Early Access		Industrial Internet applications require networks to guarantee deterministic end-to-end latency and zero packet loss at both the data link and network layers. Traditional best-effort communication models in consumer networks are insufficient to meet these stringent demands. To meet these stringent demands, the IEEE 802.1 standards introduce Time-Sensitive Networking (TSN) at the data link layer, while the IETF proposes Deterministic Networking (DetNet) for the network layer. However, enabling seamless cross-domain communication between TSN and DetNet remains a significant challenge. This paper proposes a unified cross-domain network architecture and a time-slot alignment strategy that compensates for synchronization errors between the TSN and DetNet layers. We further develop a Joint Routing and Scheduling algorithm for Deterministic Cross-Domain Transmission (JRS-DCT), which simultaneously addresses routing and scheduling under cross-domain constraints. The algorithm leverages Cycle-Specified Queuing and Forwarding (CSQF) in DetNet and Cycle Queuing and Forwarding (CQF) in TSN to ensure bounded latency and deterministic transmission. Extensive simulations demonstrate that the proposed JRS-DCT algorithm significantly improves the scheduling success rate and effectively reduces network resource utilization compared to two baseline algorithms. These results validate the effectiveness and robustness of the proposed framework in supporting time-sensitive communication across heterogeneous network environments.	10.1109/TNSM.2026.3679810
Yifei Xie, Zhi Lin, Kefeng Guo, Ruiqian Ma, Hussam Al Hamadi, Fatima Asiri, Ahlam Almusharraf	Lightweight Learning for Symbiotic Secure and Efficient ISAC in RIS-assisted Intelligent Transportation Networks	2026	Early Access		Achieving real-time processing in integrated sensing and communication (ISAC) systems presents significant challenges due to the high computational burden of conventional optimization methods, particularly within intelligent transportation networks (ITN). This paper addresses these challenges by proposing lightweight supervised and unsupervised deep learning (DL) algorithms, respectively for quasi-static and dynamic environments, aiming to improve the secrecy energy efficiency (SEE) of ITN under the constraints of the Cram´er-Rao bound (CRB) for direction-of-arrival (DOA) estimation and the transmission rate of each user. By jointly optimizing power allocation and reconfigurable intelligent surface (RIS) phase shifts, the framework ensures robust physical layer security (PLS) alongside communication efficiency, aligning with defense-in-depth strategies for securing next-generation ITN. For quasi-static environments, a supervised deep neural network (DNN) algorithm leverages offline codebook-generated labels to achieve near-optimal channel state information (CSI) mapping, explicitly minimizing signal leakage to eavesdroppers. In dynamic scenarios, an unsupervised channel attention mechanism-based residual network (CAM-ResNet) eliminates labeling overhead through direct physics-informed SEE optimization with adaptive constraint enforcement, enabling real-time adaptation to rapidly varying channels and evolving security threats. Simulation results demonstrate that both algorithms achieve comparable SEE performance with the zero-forcing (ZF) method, while significantly reducing computational complexity, with the CAM-ResNet demonstrating superior resilience to dynamic security threats. This work contributes to advancing secure and efficient ISAC solutions, reinforcing multi-layered defense mechanisms critical for future ITN.	10.1109/TNSM.2026.3679370
Raffaele Carillo, Francesco Cerasuolo, Giampaolo Bovenzi, Domenico Ciuonzo, Antonio Pescapé	A Federated and Incremental Network Intrusion Detection System for IoT Emerging Threats	2026	Early Access	Training Incremental learning Adaptation models	Ensuring network security is increasingly challenging, especially in the Internet of Things (IoT) domain, where threats are diverse, rapidly evolving, and often device-specific. Hence, Network Intrusion Detection Systems (NIDSs) require (i) being trained on network traffic gathered in different collection points to cover the attack traffic heterogeneity, (ii) continuously learning emerging threats (viz., 0-day attacks), and (iii) be able to take attack countermeasures as soon as possible. In this work, we aim to improve Artificial Intelligence (AI)-based NIDS design & maintenance by integrating Federated Learning (FL) and Class Incremental Learning (CIL). Specifically, we devise a Federated Class Incremental Learning (FCIL) framework–suited for early-detection settings—that supports decentralized and continual model updates, investigating the non-trivial intersection of FL algorithms with state-of-the-art CIL techniques to enable scalable, privacy-preserving training in highly non-IID environments. We evaluate FCIL on three IoT datasets across different client scenarios to assess its ability to learn new threats and retain prior knowledge. The experiments assess potential key challenges in generalization and few-sample training, and compare NIDS performance to monolithic and centralized baselines.	10.1109/TNSM.2026.3675031
Jing Zhang, Chao Luo, Rui Shao	MTG-GAN: A Masked Temporal Graph Generative Adversarial Network for Cross-Domain System Log Anomaly Detection	2026	Early Access	Anomaly detection Adaptation models Generative adversarial networks	Anomaly detection of system logs is crucial for the service management of large-scale information systems. Nowadays, log anomaly detection faces two main challenges: 1) capturing evolving temporal dependencies between log events to adaptively tackle with emerging anomaly patterns, 2) and maintaining high detection capabilities across varies data distributions. Existing methods rely heavily on domain-specific data features, making it challenging to handle the heterogeneity and temporal dynamics of log data. This limitation restricts the deployment of anomaly detection systems in practical environments. In this article, a novel framework, Masked Temporal Graph Generative Adversarial Network (MTG-GAN), is proposed for both conventional and cross-domain log anomaly detection. The model enhances the detection capability for emerging abnormal patterns in system log data by introducing an adaptive masking mechanism that combines generative adversarial networks with graph contrastive learning. Additionally, MTG-GAN reduces dependency on specific data distribution and improves model generalization by using diffused graph adjacency information deriving from temporal relevance of event sequence, which can be conducive to improve cross-domain detection performance. Experimental results demonstrate that MTG-GAN outperforms existing methods on multiple real-world datasets in both conventional and cross-domain log anomaly detection.	10.1109/TNSM.2026.3654642
Wangqing Luo, Jinbin Hu, Hua Sun, Pradip Kumar Sharma, Jin Wang	SALB: Security-Aware Load Balancing for Large Language Model Training in Datacenter Networks	2026	Early Access	Training Load management Packet loss	To meet the massive compute and high-speed communication demands of Large Language Model (LLM) training, modern datacenters typically adopt multipath topologies such as Fat-Tree and Clos to host parallel jobs across hundreds to thousands of GPUs. However, LLM training exhibits periodic, high-bandwidth communication patterns. Existing load-balancing schemes become misaligned under dynamic congestion and anomalous surges: they struggle to promptly mitigate iteration-peak congestion and lack effective isolation of anomalous traffic. To address this, we propose Security-Aware Load Balancing (SALB) for LLM training. SALB leverages a Deep Reinforcement Learning (DRL) controller with queue and delay signals for packet-level multipath load balancing and employs path binding to confine suspicious flows. By integrating data security into load balancing, SALB simultaneously achieves high throughput and robust traffic isolation. NS-3 simulation results show that, compared with CONGA, Hermes, and ConWeave, SALB reduces the 99th-percentile flow completion time (FCT) of short flows by an average of 65% and increases the throughput of long flows by an average of 54%. It further outperforms the baselines in aggregate throughput, path utilization, and packet loss rate, thereby significantly enhancing system stability, robustness, and data security.	10.1109/TNSM.2026.3678979
Basharat Ali, Guihai Chen	MIRAGE-DoH: Metamorphic Intelligence and Resilient AI Grid for Autonomous Governance of Encrypted DNS	2026	Early Access	Cryptography Domain Name System Fingerprint recognition	Existing DNS over HTTPS defenses have demonstrated limited resilience against polymorphic traffic shaping, staged tunneling, and adaptive mimicry, largely because they rely on static learning pipelines and rigid cryptographic configurations. MIRAGE-DoH was designed to examine whether adaptive inference, persistent structural encoding, and calibrated cryptographic agility could be integrated into a deployable and measurable encrypted DNS control architecture. The framework combined flow-level Cognitive MetaAgents capable of internal reconfiguration, Topological Memory Networks that preserved stable geometric irregularities across temporal windows, and Metamorphic Cryptographic Shards that adjusted key encapsulation policies according to empirically calibrated threat severity. A Causal Counterfactual Environment modeled constrained attacker decision pathways, while Spectral Game Intelligence analyzed flow interaction graphs to anticipate structural attack transitions.Evaluation on extended CIC-DoH2023 and Gen-C-DDD-2022 datasets was conducted under fixed flow-level decision intervals with explicit accounting for synchronization overhead, spectral graph construction cost, and cryptographic rotation latency. Cross-dataset experiments yielded a mean detection accuracy of 97.8% with a 0.41% false positive rate, sustaining median inference latency of 62μs and cryptographic morph latency of 3.7 ms under load. Quantum-assisted inference was assessed through bounded simulations, indicating constrained information gain within the adopted lattice-based configuration, without asserting unconditional post-quantum immunity. These results demonstrated that adaptive encrypted DNS governance can be empirically grounded, operationally bounded, and stress-evaluated without reliance on unqualified claims of perfect security.	10.1109/TNSM.2026.3677474
Jianwei Zhang, Bowen Cui	Bandwidth-Delay Optimal Segment Routing: Upper-Bound and Lower-Bound Algorithms	2026	Early Access	Routing Optimization Quality of service	Segment routing (SR) is a novel source routing paradigm that enables network programmability. However, existing research rarely considers multicriteria optimization problems in SR networks. Given the critical role of bandwidth and delay in quality-of-service (QoS) routing, we formally define the bandwidth-delay optimal SR (BDoSR) problem for the first time and prove its NP-hardness. By leveraging the label correcting algorithm schema, we design a suite of polynomial-time algorithms, including an upper-bound algorithm (BDoSR-UB) and a lower-bound algorithm (BDoSR-LB). BDoSR-UB enables rapid estimation of the optimal solution while BDoSR-LB is accuracy-adjustable and delivers (near-)optimal feasible solutions. We rigorously analyze their performance gap through carefully constructed network examples, providing deep insights into the adjustable parameters of BDoSR-LB. Finally, we validate our algorithms on realistic network topologies, demonstrating that both BDoSR-UB and BDoSR-LB frequently converge to the optimal solution in practice while offering superior computational efficiency compared to existing approaches.	10.1109/TNSM.2026.3678190
Eduardo Castilho Rosa, Daniel Nunes Corujo, Flávio de Oliveira Silva	CoFIB: A Memory-Efficient NDN FIB Design for Programmable Edge Switches	2026	Vol. 23, Issue	Data structures Random access memory Pipelines	Designing high-performance and memory-efficient data structures for the Forwarding Information Base (FIB) in Named-Data Networking (NDN) is a challenging task. Since the FIB size is orders of magnitude larger than IP routing tables, scaling it to store millions of prefixes in SRAM/TCAM memory in programmable switches is an open problem. To address this issue, we propose a Compressed FIB data structure called CoFIB, designed to run on edge programmable switches in an SDN-based environment. The CoFIB is a collection of exact-match tables placed in an optimized manner in both ingress and egress pipelines. We propose a LNPM algorithm that carefully recirculates packets in the pipeline. We also introduce the concept of canonical name prefixes to reduce memory footprint and propose an algorithm to extract canonical prefixes from the Routing Information Base (RIB). Experimental results show that CoFIB can compress memory up to $16.58{\times }$ compared to the state-of-the-art, with no significant impact on throughput compared to hardware-based solutions. Additionally, our proposed table placement optimization for LNPM increases the number of packets processed at a line rate by 23.17% compared to the linear table placement approach using a large NDN name dataset.	10.1109/TNSM.2025.3641145
Sheng-Wei Wang, Show-Shiow Tzeng	An Accurate and Efficient Analytical Model for Security Evaluation of PoW Blockchains With Multiple Independent Selfish Miners	2026	Vol. 23, Issue	Blockchains Analytical models Accuracy	Selfish mining poses significant security challenges to Proof-of-Work (PoW) blockchains by allowing strategic miners to gain disproportionate rewards through protocol deviation. While the impact of a single selfish miner has been extensively studied, the security implications of multiple independent selfish miners remain insufficiently understood. This paper presents an accurate and efficient analytical model for security evaluation of PoW blockchains under multiple independent selfish mining behaviors. The blockchain dynamics are modeled as a Markov chain with a novel state aggregation approximation, enabling closed-form estimation of miner rewards. Numerical results show that the proposed model achieves high accuracy, with deviations typically less than 5.09% compared to simulations in a blockchain with two selfish miners. In a blockchain with more than two selfish miners, the proposed analytical model yields more accuracy approximation leading to less than 2% error. We also propose a truncation mechanism to reduce the number of states in the proposed Markov chain. Numerical results show that the proposed analytical model with truncation significantly reduce the computation time while the accuracy is still maintained. Two use cases are presented: determining the profitable threshold of total selfish mining power and analyzing reward dis-proportionality between strong and weak selfish miners. The proposed model provides a practical framework for quantifying incentive-driven security risks and evaluating their impact on blockchain fairness and decentralization.	10.1109/TNSM.2025.3637840
Sajjad Alizadeh, Majid Khabbazian	On Scalability Power of Payment Channel Networks	2026	Vol. 23, Issue	Topology Routing Network topology	Payment channel networks have great potential to scale cryptocurrency payment systems. However, their scalability power is limited as payments occasionally fail in these networks due to various factors. In this work, we study these factors and analyze their imposing limitations. To this end, we propose a model where a payment channel network is viewed as a compression method. In this model, the compression rate is defined as the ratio of the total number of payments entering the network to the total number of transactions that are placed on the blockchain to handle failed payments or (re)open channels. We analyze the compression rate and its upper limit, referred to as compression capacity, for various payment models, channel-reopening strategies, and network topologies. For networks with a tree topology, we show that the compression rate is inversely proportional to the average path length traversed by payments. For general networks, we show that if payment rates are even slightly asymmetric and channels are not reopened regularly, a constant fraction of payments will always fail regardless of the number of channels, the topology of the network, the routing algorithm used and the amount of allocated funds in the network. We also examine the impact of routing and channel rebalancing on the network’s compression rate. We show that rebalancing and strategic routing can enhance the compression rate in payment channel networks where channels may be reopened, differing from the established literature on credit networks, which suggests these factors do not have an effect.	10.1109/TNSM.2025.3640098
Jiahe Xu, Chao Guo, Moshe Zukerman	Virtual Network Embedding for Data Centers With Composable or Disaggregated Architectures	2026	Vol. 23, Issue	Servers Data centers Greedy algorithms	Virtual Network Embedding (VNE) is an important problem in network virtualization, involving the optimal allocation of resources from substrate networks to service requests in the form of Virtual Networks (VNs). This paper addresses a specific VNE problem in the context of Composable/Disaggregated Data Center (DDC) networks, characterized by the decoupling and reassembly of different resources into resource pools. Existing research on the VNE problem within Data Center (DC) networks primarily focuses on the Server-based DC (SDC) architecture. In the VNE problem within SDCs, a virtual node is typically mapped to a single server to fulfill its requirements for various resources. However, in the case of DDCs, a virtual node needs to be mapped to different resource nodes for different resources. We aim to design an optimization method to achieve the most efficient VNE within DDCs. To this end, we provide an embedding scheme that acts on each arriving VN request to embed the VN with minimized power consumption. Through this scheme, we demonstrate that we also achieve a high long-term acceptance ratio. We provide Mixed Integer Linear Programming (MILP) and scalable greedy algorithms to implement this scheme. We validate the efficiency of our greedy algorithms by comparing their performance against the MILP for small problems and demonstrate their superiority over baseline algorithms through comprehensive evaluations using both synthetic simulations and real-world Google cluster traces.	10.1109/TNSM.2025.3639958
Junfeng Tian, Junyi Wang	D-Chain: A Load-Balancing Blockchain Sharding Protocol Based on Account State Partitioning	2026	Vol. 23, Issue	Sharding Blockchains Load modeling	Sharding has become one of the key technologies for improve the performance of blockchain systems. However, the imbalance of transaction load between shards caused by extremely hot accounts leads to an imbalance in the utilization of system resources as well as the increase of cross-shard transactions with the number of shards limits the expansion of sharding systems, and sharding systems do not achieve the desired performance improvement. We propose a new blockchain sharding system called D-Chain. D-Chain splits and distributes the state of extremely hot accounts into multiple shards, allowing transactions for an account can be processed in multiple shards, thus balancing the load between shards and reducing the number of cross-shard transactions. We have implemented a prototype of D-Chain, and evaluated its performance using real-world Ethereum transactions. Experimental results show that the proposed system achieves a more balanced shard load and outperforms other baselines in terms of throughput, transaction latency, and cross-shard transaction ratio.	10.1109/TNSM.2025.3640097
Long Chen, Yukang Jiang, Zishang Qiu, Donglin Zhu, Zhiquan Liu, Zhenzhou Tang	Toward Energy-Saving Deployment in Large-Scale Heterogeneous Wireless Sensor Networks for Q-Coverage and C-Connectivity: An Efficient Parallel Framework	2026	Vol. 23, Issue	Wireless sensor networks Metaheuristics Sensors	Efficient deployment of thousands of energy-constrained sensor nodes (SNs) in large-scale wireless sensor networks (WSNs) is critical for reliable data transmission and target sensing. This study addresses the Minimum Energy Q-Coverage and C-Connectivity (MinEQC) problem for heterogeneous SNs in three-dimensional environments. MnPF (Metaheuristic–Neural Network Parallel Framework), a two-phase method that can embed most metaheuristic algorithms (MAs) and neural networks (NNs), is proposed to address the above problem. Phase-I partitions the monitoring region via divide-and-conquer and applies NN-based dimensionality reduction to accelerate parallel optimization of local Q-coverage and C-connectivity. Phase-II employs an MA-based adaptive restoration strategy to restore connectivity among subregions and systematically assess how different partitioning strategies affect the number of restoration steps. Experiments with four NNs and twelve MAs demonstrate efficiency, scalability, and adaptability of MnPF, while ablation studies confirm the necessity of both phases. MnPF bridges scalability and energy efficiency, providing a generalizable approach to SN deployment in large-scale WSNs.	10.1109/TNSM.2025.3640070