TNSM Searchable Index

Author(s)	Title	Year	Publication	Keywords
Jiale Zhu, Xiaoyao Zheng, Shukai Ye, Ming Zheng, Liping Sun, Liangmin Guo, Qingying Yu, Yonglong Luo	Federated Recommendation Model Based on Personalized Attention and Privacy-Preserving Dynamic Graph	2026	Early Access	Modeling Federated learning Privacy	Graph Neural Networks (GNNs) have been widely adopted in recommendation systems. When integrated into a federated learning framework, GNNs can enhance the model’s expressive capability. However, challenges arise in personalized representation and graph expansion due to the heterogeneity and locality of user data in federated recommendation systems. To address these challenges, we propose a federated recommendation model based on personalized attention and privacy-preserving dynamic graphs. The method first matches neighbor users for each selected client. Subsequently, it counts the interaction frequencies of items for both local and neighbor users to construct personalized weights, which captures the unique characteristics of different users. Additionally, we designs a method for constructing privacy-preserving dynamic graphs. In each round of federated training, the selected client adds pseudo-interaction items to its own interaction subgraph, perturbing the real interactions. After completing local training, the noisy interaction subgraph is incorporated into the global graph to capture higher-order connectivity information among users while safeguarding their interaction privacy. We conduct extensive experiments on three benchmark datasets, and the results demonstrate that the proposed PADG method achieves superior performance while effectively protecting privacy.	10.1109/TNSM.2026.3691659
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
Songshou Dong, Yanqing Yao, Huaxiong Wang, Yining Liu	LCMS: Efficient Lattice-based Conditional Privacy-preserving Multi-receiver Signcryption Scheme for Internet of Vehicles	2026	Early Access	Optical waveguides Optical fibers Broadcasting	Internet of Vehicles (IoV) requires robust security and privacy protection mechanisms to enable trusted traffic information exchange, while also requiring low communication and low computing overhead to meet the real-time requirements of IoV. Existing signcryption schemes suffer from quantum vulnerability, inadequate unlinkability/vehicle anonymity, absence of revocability, poor scalability, inadequate management of malicious entities, and high communication and computational overhead. So we propose an efficient lattice-based conditional privacy-preserving multi-receiver signcryption scheme (LCMS) that systematically addresses these gaps through three core innovations: 1) Privacy preservation is achieved via a pseudonym mechanism integrated with certificateless key generation, which ensures vehicle anonymity and weak unlinkability while preventing malicious key generation center and key escrow; 2) Malicious entity management through dynamic revocability and distributed decryption among roadside units, preventing unilateral message access; and 3) Post-quantum efficiency is achieved by leveraging the Learning With Rounding problem to eliminate expensive Gaussian sampling, combined with ciphertext packing techniques. This reduces time overhead, the size of signcryptexts, and communication overhead, while lowering the overall storage overhead of the scheme through the MP12 trapdoor. Security proofs show LCMS achieves Existential Unforgeability under Adaptive Identity Chosen-Message Attack and Indistinguishability under Adaptive Identity Chosen-Ciphertext Attack in the Random Oracle Model, with rigorously validated resistance against multiple IoV-specific attacks. Experimental results via SageMath implementation demonstrate that our scheme exhibits a smaller signcryptext size and lower signcryption/unsigncryption time compared to existing random lattice-based signcryption schemes. Scalability tests with 300 vehicles and 300 roadside units (RSUs) were completed within 230 seconds. Communication overhead analysis confirms practical feasibility for IEEE 802.11p vehicle communication protocol, and RSU serving capability evaluation under realistic vehicle density (100–200/km2) and speed (40–60 km/h) further validates system practicality. LCMS provides a quantum-resistant, privacy-preserving, and efficient solution for production IoV.	10.1109/TNSM.2026.3688507
Arad Kotzer, Tom Azoulay, Yoad Abels, Aviv Yaish, Ori Rottenstreich	SoK: DeFi Lending and Yield Aggregation Protocol Taxonomy, Empirical Measurements, and Security Challenges	2026	Early Access	Filtering Application specific integrated circuits Filters	Decentralized Finance (DeFi) lending protocols implement programmable credit markets without intermediaries. This paper systematizes the DeFi lending ecosystem, spanning collateralized lending (including over- and under- collateralized designs, and zero-liquidation loans), uncollateralized primitives (e.g., flashloans), and yield aggregation protocols which allocate capital across underlying lending platforms. Beyond a taxonomy of mechanisms and comparing protocols, we provide empirical on-chain measurements of lending activity and user behavior, using Compound V2 and AAVE V2 as case studies, and connect empirical observations to protocol design choices (e.g., interestrate models and liquidation incentives). We then characterize vulnerabilities that arise due to notable designs, focusing on interestrate setting mechanisms and time-measurement approaches. Finally, we outline open questions at the intersection of mechanism design, empirical measurement and security for future research.	10.1109/TNSM.2026.3682174
Awaneesh Kumar Yadav, Madhusanka Liyanage, An Braeken	An Improved and Provably Secure EDHOC Protocol Supporting the Extended Canetti–Krawczyk (eCK) Security Model	2026	Early Access	Aerospace and electronic systems Telemetry Central Processing Unit	Transport Layer Security (TLS) is considered to be the most used standard security protocol for the Internet of Things (IoT). However, as TLS was originally designed for computer networks, it is not optimal with respect to efficiency. Therefore, a new protocol called Object Security for Constrained RESTful Environments (OSCORE) has been standardized for securing constrained devices. Currently, the Ephemeral Diffie Hellman Over COSE (EDHOC) protocol, which is a key exchange protocol to define a session key used in OSCORE, is also in the process of being standardized. This paper shows that the four authentication modes of the EDHOC protocol are vulnerable in the extended Canetti–Krawczyk (eCK) security model, which is a common security model used in IoT. In addition, also resistance to Distributed Denial of Service (DDoS) attacks is weak. Taking this into account, we propose two new variants of EDHOC. The first variant, EDHOC2, is able to overcome both issues but has a slightly higher cost for communication, computation, storage, and energy consumption. The second variant, EDHOC3, offers only additional protection in the eCK security model and has, on average, similar, even better performance in one authentication mode, compared to EDHOC. Additionally, the Real-Or-Random (ROR) logic and Scyther validation tool are employed to ensure the security of the designed variants. Furthermore, a prototype implementation is conducted to demonstrate the real-time deployment of the designed versions.	10.1109/TNSM.2026.3690530
Arman Sanaei, Massoud Reza Hashemi	Adaptive, Profit-Aware RAN Slicing for Multi-Operator Networks via Spatio-Temporal Prediction and Energy-Aware SAC	2026	Early Access		The proliferation of heterogeneous 5G/6G services and the emergence of multi-tenant deployments have made isolation-preserving RAN slicing a central requirement for shared infrastructures. This paper studies multi-tenant RAN slicing where multiple Mobile Virtual Network Operators (MVNOs) compete for a common pool of radio and edge-compute resources and must maximize long-term economic performance while preserving slice isolation. We propose a two-timescale framework that couples (i) prediction-driven, per-cell Long Time Slot (LTS) reservation with an isolation-aware congestion/pressure cost that coordinates competing MVNO demands, and (ii) Short Time Slot (STS) per-user allocation modeled as a Markov decision process and solved via an energy-aware Soft Actor–Critic (SAC) policy. This design separates strategic capacity planning from fast, stochastic user-level control, while retaining tractability under dynamic traffic, mobility, and channel uncertainty. Extensive simulations across small- and large-scale deployments show that the proposed approach improves MVNO profit per cell per LTS by up to 26% over representative baselines, while maintaining robust isolation under asymmetric demand and rival-tenant growth.	10.1109/TNSM.2026.3694799
Yuxiang Wang, Jiao Zhang, Leixin Cai, Tao Huang	Mercury: Multipath Spraying for Joint Congestion and Reordering Control in RDMA	2026	Early Access		Due to the low entropy traffic characteristics of LLM (Large Language Model) training, existing load balancing mechanisms such as Equal-Cost Multi-Path (ECMP) fail to fully utilize the redundant bandwidth between computing nodes in RDMA over Converged Ethernet (RoCE). Packet spraying mechanism has become a typical solution to the load balancing problem in RoCEs. However, it has a negative effect on congestion control mechanisms and suffers severe out-of-order problems. In this paper, we propose Mercury, a host-driven spraying scheme that synergizes congestion feedback and reordering control. Mercury selects paths by leveraging ECN, RTT, and reordering metrics, adjusts rates via multi-metric window. It also employs receiver-side buffers with priority-based dropping to mitigate out-of-order penalties. Evaluations in ns-3 under AllReduce and All-to-All traffic show that Mercury consistently outperforms the ECMP-based baselines, including DCQCN, TIMELY, HPCC, SWIFT, and BOLT, with the largest reduction in Max FCT reaching 63%. Under multi-path load balancing, Mercury delivers the lowest Max FCT for large messages in AllReduce and for most message sizes in All-to-All. It outperforms STRACK and MP-RDMA by up to 28% and 35% in AllReduce, and by up to 25% and 30% in All-to-All.	10.1109/TNSM.2026.3692452
Shaimaa Alkaabi, Mark A Gregory, Shuo Li	A Stateless Orchestrated Handover Protocol for Multi-Access Edge Computing	2026	Early Access		In Multi-access Edge Computing (MEC) environments, session continuity during user mobility remains a pressing challenge due to decentralized infrastructure and high-throughput, latency-sensitive applications. Existing mobility protocols often rely on stateful mechanisms or centralized control, leading to increased signaling overhead, limited scalability, and vulnerability to performance degradation in dynamic networks. This paper introduces the Server Search and Select Algorithm Protocol (SSSAP), a lightweight, UDP-based handover protocol tailored for MEC deployments. The protocol is an extension of our previous work on a handover Server Search and Selection Algorithm (SSSA). SSSAP enables seamless session redirection through a three-phase signaling scheme (pre-handover, handover initiation, and handover termination), preserving service continuity without coupling session state to transport layers. The protocol’s design features extensible headers for multi-metric evaluation and future security adaptation while maintaining minimal dependency on intermediary control nodes. Through extensive simulation and testing, we have validated the SS-SAP efficiency across user equipment nodes and MEC servers. Results demonstrate high handover success rates, low-session setup delays, and balanced server load distribution. SSSAP achieves superior performance in mobility robustness, packet loss mitigation, and integration simplicity. The research outcomes position SSSAP as a scalable and application-agnostic mobility protocol for MEC systems, especially in vehicular and high-mobility scenarios.	10.1109/TNSM.2026.3692555
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
Minh-Thuyen Thi, Mohan Gurusamy	Multi-dimensional Cross-granularity Open-set Network Intrusion Detection	2026	Early Access	Modeling Labeling Distance measurement	Network intrusion detection systems (NIDSs) face critical challenges from continuously evolving cyber-attacks. Traditional machine learning methods, while requiring extensive labeled training data, still often fail against unknown and out-of-distribution (OOD) attacks. Furthermore, new sophisticated adversaries are exploiting the detection blind spots inherent in traditional feature representation approaches that do not provide adequate comprehensive traffic analysis. In this paper, we propose MDCG-IDS, an NIDS framework that introduces multi-dimensional cross-granularity (MDCG) feature representation for open-set detection, in which network traffic is analyzed thoroughly across three complementary dimensions (traffic statistics, temporal, spatial), each at multiple granularity levels. These dimensions and granularities jointly capture the structures of sophisticated attacks that may be invisible from single analytical perspectives. We design a tensor structure that provides a unified encoding for the MDCG features while supporting the use of optimal transport theory to measure the distance between benign traffic and known or unknown attacks. MDCG-IDS uses a semi-supervised learning model that is trained exclusively on benign traffic and validated on a small set of labeled data, significantly reducing the effort of data labeling. Experiments on various datasets achieve AUC-ROC scores of more than 0.948, exceeding the best competing state-of-the-art methods by up to 7%. Regarding the amount of labeled validating data, MDCG-IDS obtains an AUC-ROC score of over 0.94 with only 3% of entire validating samples, outperforming the baseline models.	10.1109/TNSM.2026.3693141
Dinghao Zeng, Fagui Liu, Runbin Chen, Jingwei Tan, Dishi Xu, Qingbo Wu, C.L. Philip Chen	CoreScaler: A Resource-Efficient Hybrid Scaling Framework for Dynamic Workloads in Cloud	2026	Early Access	Resource management Central Processing Unit Memory	Containerized microservices face significant challenges in balancing service quality and resource efficiency under dynamic workloads. Existing approaches suffer from horizontal scaling’s cold start latency, vertical scaling’s resource ceilings, and hybrid methods’ limited adaptability. We present CoreScaler, a resource-efficient hybrid scaling framework based on analysis of CPU usage patterns revealing substantial consumption differences between working mode and waiting mode instances. This insight drives our dual-mode instance management model that distinguishes between working instances actively handling requests and waiting instances maintaining hot standby with minimal resource allocation. CoreScaler employs a master-subordinate distributed architecture where the master node performs capacity planning using multi-confidence interval predictions and contextual multi-armed bandit optimization, while subordinate nodes execute mode-aware CPU quota adjustments. Comprehensive evaluation on a Kubernetes cluster with a typical microservice system under four representative production work-loads demonstrates that CoreScaler maintains SLO compliance while reducing CPU and memory allocation by 22.53% and 30.83% respectively compared to state-of-the-art solutions. The framework achieves substantially higher resource utilization than single-dimension scaling approaches, validating the effectiveness of coordinated hybrid scaling for dynamic cloud environments.	10.1109/TNSM.2026.3692955
Xingyu He, Nianci Li, Panxing Huang, Chunhua Gu, Guisong Yang, Yunhuai Liu	Dynamic Spatiotemporal Dual-Encoder Transformer for Long-Term Traffic Prediction in LEO Satellite Networks	2026	Early Access	Satellites Modeling Low earth orbit satellites	Accurate long-term traffic prediction in Low Earth Orbit (LEO) satellite networks is essential for proactive resource allocation and congestion avoidance, yet remains challenging due to highly dynamic topologies, intermittent connectivity, and scarce real traffic data. Existing approaches are largely limited to short-term prediction or assume static spatial dependencies, making them inadequate for non-stationary LEO environments. To address these challenges, this paper proposes DST-DEformer, a dynamic spatial–temporal Transformer framework that jointly models evolving inter-satellite topology and multi-scale temporal dependencies. Specifically, a topology-adaptive graph convolution module captures time-varying spatial correlations, while a dual temporal encoder decouples long-term global trend modeling from short-term local fluctuation learning. In addition, a hybrid simulation–calibration framework is developed to generate realistic satellite traffic by incorporating orbital dynamics, demographic information, and real-world traffic trends. Extensive experiments on simulated LEO satellite traffic and the PEMS08 benchmark show that DST-DEformer consistently outperforms state-of-the-art methods in long-term prediction, achieving 4%-13% reductions in MSE and MAE and significantly slower error accumulation as the prediction horizon increases. These results demonstrate the effectiveness and robustness of DST-DEformer for long-term traffic prediction under dynamic network topologies.	10.1109/TNSM.2026.3693648
Jiang Mo, Ke Zhao, Limei Peng, Hsiao-Chun Wu	PDO-SFCM: Prediction-Driven Orchestration for SFC Migration in SAGIN via Fine-Tuned Large Time-Series Model and DRL	2026	Early Access	Modeling Space-air-ground integrated networks Timing	Space-air-ground integrated networks (SAGINs) have emerged as an appealing enabling technology for the next-generation ubiquitous connectivity. By extending terrestrial networks with aerial and space platforms, SAGIN can provide seamless coverage and flexible resource-access across various altitudes. However, dynamic link conditions, intermittent connectivity, and heterogeneous latency constraints would often introduce serious challenges to the service function chain (SFC) migration and orchestration. In this work, we introduce a novel PDO-SFCM (prediction-driven orchestration for SFC migration) approach, which utilizes a fine-tuned large time-series model (LTM) for network status prediction and a deep reinforcement learning (DRL) module for proactive SFC migration in SAGINs. In detail, the fine-tuned LTM predicts multi-horizon estimates of SFC arrivals and per virtual network function (per-VNF) resource demands, which will form the observation space of the DRL agent. The DRL module thus schedules appropriate migration actions on the cost-augmented time-expanded graph (C-eTEG), which can satisfy the feasibility subject to the bandwidth, buffering, and precedence constraints. Extensive simulation results demonstrate that our proposed new PDO-SFCM scheme consistently greatly improves the acceptance rate, reduces the end-to-end delay, and lowers the migration cost in comparison with DRL baselines under different prediction settings. Our proposed new scheme can significantly leverage the SAGIN performance by the devised foundation-level time-series prediction and learning-based orchestration mechanisms.	10.1109/TNSM.2026.3694203
Jiahang Pu, Hongyu Ye, Jing Cheng, Feng Shan, Runqun Xiong	Balancing Timeliness and Accuracy: A Hybrid Data-Control Plane Framework for Volumetric DDoS Defense in IoT	2026	Early Access	Modeling Internet of Things Planing	Resource-constrained IoT devices in Industrial Internet environments are highly vulnerable to DDoS attacks due to infrequent security updates and insufficient built-in protection mechanisms. Existing defense solutions primarily rely on external filtering servers or programmable switches, but these approaches fail to simultaneously meet the stringent real-time performance and high accuracy requirements of industrial applications. To address these limitations, we propose a novel cross-plane defense framework that exploits the temporal invariance characteristics of attack traffic patterns. In the data plane, an adaptive variance threshold mechanism immediately mitigates high-volume, low-variance traffic flows, while a bidirectional dual-hash table captures low-collision flow features for efficient export to the control plane. The control plane constructs temporally-enhanced flow sequences that enable deep learning models to perform accurate attack detection, subsequently directing the data plane to block identified malicious sources. We implemented and evaluated a prototype of this framework on a software switch platform using both real-world attack datasets and custom-generated traffic patterns. Experimental results demonstrate that our framework successfully mitigates 86% of attack traffic within milliseconds and achieves complete source blocking within 52 seconds. Compared to baseline methods, our framework can effectively counter both DoS and DDoS attacks without generating false positives on benign traffic.	10.1109/TNSM.2026.3693266
Elie Inaty, Charbel Maroun, Ghattas Akkad, Ali Mansour, Martin Maier	A 6G Driven Multiclass Power Efficient Dynamic Bandwidth Allocation (MPE-DBA) Scheme for Passive Optical Network (PON)	2026	Early Access	Costing Costs Algorithms	The latest ITU IMT-2030 recommendations for sixth generation (6G) networks have imposed strict specifications on communication systems. Some of these requirements include increased throughput, ultra-low delay and jitter, differentiated services, and energy efficiency. Current dynamic bandwidth allocation (DBA) schemes for passive optical networks (PON) may meet some of these requirements, yet they fail to fulfill other recommendations, especially energy efficiency. Therefore, we propose a new PON architecture, whose objective is to offer flexibility in meeting the IMT-2030 recommendations. It uses a multiclass power efficient dynamic bandwidth allocation (MPE-DBA) scheme that helps achieving both differentiated services and sustainability in terms of energy and cost. For computational efficiency, we propose a two stages Mamdani fuzzy inference system (FIS). The inputs of the first FIS are the latency and cost of the 6G traffic, whereas the latency and cost of the non-6G (N6G) traffic are the inputs of the second FIS stage. Both FISs use the variation in the number of channels as output. The proposed algorithm achieves less than 100 μs delay, less than 10μs jitter and high aggregate throughput for the 6G packets. In addition, it reduces the power consumption by three times and the cost of traffic transmission by four times as compared to the state-of-the art solution.	10.1109/TNSM.2026.3694150
Lizhuang Tan, Nguyen Van Tu, Xinhang Wang, Peiying Zhang, James Won-Ki Hong	SDNIE: A Software-Defined Approach to High-Performance Network Impairment Emulation using Programmable Switches	2026	Early Access	Emulation Central Processing Unit Testing	Network testing is critical for evaluating the performance, reliability, and security of modern computer networks. A key challenge is creating an accurate, cost-effective, and high-performance network emulation environment. Network Impairment Emulators (NIEs) emulate real-world network conditions such as bandwidth constraints, latency, and packet loss, but existing CPU- and FPGA-based solutions suffer from limited performance, high costs, and poor flexibility. This paper proposes Software-Defined Network Impairment Emulation (SDNIE), a novel framework that leverages programmable switches for scalable, cost-efficient network impairment emulation. SDNIE introduces three key techniques: (1) intent-driven network impairment configuration, automating impairment modeling; (2) serial-parallel combined execution, optimizing performance; and (3) CPU-Tofino collaborative deployment, offloading complex computations. Experimental results show that SDNIE matches commercial emulators in performance while significantly reducing costs. This work demonstrates the potential of programmable switches in network testing, offering a scalable, cost-effective, and high-performance alternative for next-generation network impairment emulation.	10.1109/TNSM.2026.3694388
Anum Lodhi, Xiong Li, Muhammad Asad Saleem, Muhammad Ali Lodhi, Khalid Mahmood, Salman Shamshad	DroneSec: Efficient and Secure Communication for Resource-Constrained Drones in IoD Systems	2026	Early Access	Protocols Drones Authentication	The Internet of Drones (IoD) represents an emerging paradigm of the Internet of Things (IoT), enabling seamless, coordinated communication among drones and integration with other connected systems. This interconnected network enables autonomous decision-making among drones. As this IoD paradigm continues to expand it faces significant challenges due to its reliance on public channel. Therefore, existing methods often suffer from impersonation, cloning, anonymity violation, and fails to offer end-to-end key secrecy. Moreover, they require high computation resources which present challenges of deployment in resource-constrained IoD environment. To address these challenges, we propose a secure and efficient protocol that provides mutual authentication among participating entities. The protocol resists impersonation, cloning, anonymity violation and offers end-to-end key secrecy. The protocol employs Physical Unclonable Function (PUF) and Elliptic Curve Cryptography (ECC), along with a fuzzy extractor, to ensure secure communication. The resilience of the proposed protocol was evaluated through an informal analysis. Its security properties were rigorously verified using formal analysis based on the Real-Or-Random (ROR) model. Evaluation of its performance shows that the proposed protocol outperforms existing solutions, achieving reductions of 20.9% in computation cost and 32.6% in communication overhead. The results demonstrate that the protocol improves security and provides reliability under the evaluated scenarios of IoD systems.	10.1109/TNSM.2026.3694492
Ashely Li, Jeffrey Chang, Steven S. W. Lee	Modeling and Optimization Algorithm for Capacity Planning in Hose Model VPN Networks	2026	Early Access	Joining processes Modeling Algorithms	Hose-based VPNs offer greater bandwidth flexibility, as they allow traffic to and from a hose endpoint to be arbitrarily distributed across other endpoints. Existing studies on hose-based VPNs have primarily focused on VPN provisioning algorithms, while optimal capacity planning for hose-based VPN networks remains largely unexplored. Given budget constraints and forecasts of future bandwidth demands at VPN endpoints, the capacity planning problem requires the joint optimization of routing decisions and link capacity allocation. Although the problem can be formulated as a nonlinear programming model, its nonconvex nature makes direct solution computationally challenging. To address this issue, we reformulate the problem as a sequence of linear programming problems and develop a solution framework based on a water-filling algorithm. For any defined budget and relative tolerance, the proposed algorithm yields a near-optimal solution where the network expansion cost stays within the allowed margin. Numerical results demonstrate that the proposed approach efficiently solves the hose-based VPN capacity planning problem within practical computation time.	10.1109/TNSM.2026.3694390
Somchart Fugkeaw, Kittipat Tangtanawirut, Pakapon Rattanasrisuk, Archawit Changtor	MK-WISE: Secure and Efficient Multi-Keyword Wildcard ABSE With Keyword-Level Revocation for Device–Edge–Cloud EHRs Data Sharing	2026	Vol. 23, Issue	Encryption Cryptography Access control	The rapid proliferation of Internet of Things (IoT) in healthcare has transformed the management of Electronic Health Records (EHRs), but also introduced critical challenges in secure retrieval, dynamic revocation, and verifiable integrity over encrypted data. Existing Searchable Encryption (SE) and Attribute-Based Searchable Encryption (ABSE) models remain limited: 1) most support only exact or prefix keyword matching and cannot handle flexible wildcard or substring queries common in medical search; 2) revocation is coarse-grained, often requiring costly key redistribution or ciphertext re-encryption; and 3) integrity verification either incurs heavy blockchain overhead or exposes access structures, undermining privacy. To address these gaps, we propose MK-WISE, a secure and efficient multi-keyword wildcard ABSE framework for IoT–EHR systems. MK-WISE integrates an Index–Wildcard Tree (IWT) with Substring Bloom Filters (SBF) to enable expressive wildcard and substring queries, employs a puncturable PRF–based revocation workflow with edge-local enforcement, hierarchical key updates, and optional blockchain anchoring, and incorporates homomorphic MACs for lightweight correctness and completeness verification. Security analysis proves that MK-WISE achieves confidentiality, keyword privacy, unlinkability, and revocability under standard assumptions. Experimental results demonstrate that MK-WISE significantly outperforms state-of-the-art schemes in trapdoor generation, search scalability, and revocation cost, achieving millisecond-level revocation without user disruption. These results highlight MK-WISE as a practical and comprehensive solution for privacy-preserving EHR retrieval in IoT-enabled healthcare.	10.1109/TNSM.2026.3657982
Ze Wei, Rongxi He, Chengzhi Song, Xiaojing Chen	Differentiated Offloading and Resource Allocation With Energy Anxiety Level Consideration in Heterogeneous Maritime Internet of Things	2026	Vol. 23, Issue	Internet of Things Resource management Carbon footprint	The popularity of maritime activities not only exacerbates the carbon footprint (CF) but also places higher demands on Maritime Internet of Things (MIoTs) to support heterogeneous MIoT devices (MIoTDs) with different prioritized tasks. High-priority tasks can be processed cooperatively via local computation, offloading to nearby MIoTDs (helpers), or offloading to edge servers to ensure their timely and successful completion. Due to the differences in energy availability and rechargeability, MIoTDs exhibit distinct energy states, impacting their operational behaviors. We propose the Energy Anxiety Level (EAL) to quantify these states: Higher EAL tends to lead to increased packet dropping and earlier shutdown. Although low-EAL MIoTDs seem preferable as helpers, their scarce residual computational resources after local task completion may cause offloaded high-priority tasks to drop or time out. Therefore, helper selection should jointly consider candidate MIoTDs’ EALs and loads to evaluate their unsuitability. This paper addresses the problem of differentiated task offloading and resource allocation in MIoTs by formulating it as a mixed integer nonlinear programming model. The objective is to minimize system-wide carbon footprint (CF), packet loss, helper unsuitability risk, and high-priority task latency. To solve this complex problem, we decompose it into two subproblems. We then design algorithms to determine optimal offloading patterns, task partitioning factors, MIoTD transmission powers, and computation resource allocation for MIoTDs and edge servers. Simulation results demonstrate that our proposal outperforms benchmarks in reducing CF and EAL, lowering high-priority task latency, and improving task completion ratio.	10.1109/TNSM.2026.3655385