TNSM Searchable Index

Author(s)	Title	Year	Publication	Keywords
Muhammad Muhammad Bala, Abdullahi Uwaisu Muhammad, Kamaluddeen Ibrahim Yarima, Aseel Smerat, Mulikatu Yakubu Ibrahim, Safiyanu Yahaya, Hamza Adamu	Isolation and Optimization Cost of Service-based Radio Access Network Slicing: A Smart-Contract-Based Approach	2026	Early Access	Network slicing Smart contracts Radio access networks	The service-based Radio Access Network (RAN) slicing enabled via Software Defined Networking (SDN) and Network Function Virtualization (NFV) can support diverse service requirements and address the rapid data traffic growth from both the vertical industry and the Internet of Things (IoT). However, network slice isolation and resource sharing between slices should be be improved for future wireless network requirements. Firstly, this paper address the isolation enhancement of future wireless networks through Blockchain-Smart-Contract, by creating two smart-contract-based access control to secure access to different service-based RAN applications and secure the sharing of resources. These contract are Verification and Authorization Contract (VAC), as well as Misconduct and Revocation Contract (MRC). The proposed framework is designed to support key 6G service classes, such as enhanced Mobile Broadband (eMBB) and ultra-Reliable Low-Latency Communications (uRLLC), enabling high data rates and low-latency communication. Secondly, to ensure the servicebased RAN achieves better isolation the optimization goal is to minimize the deployment cost to obtain the best deployment scheme. Hence, we divide the service-based RAN slice isolation deployment problem into two sub-problems, i.e., service-based RAN slice isolation and slice deployment problem, by formulating a Mixed Integer Linear Programming (MILP) model to minimize the deployment cost. Finally, to verify the feasibility of the design implementation an experimental platform is built and the results show the architecture achieves isolation enhancement through smart-contract and reduces the deployment cost by 78% and improve the isolation performance by 93% compared to the Blockchain-enabled Network Slice (BcNS) and the service-based RAN.	10.1109/TNSM.2026.3732250
Huixiang Zhang, Faria Khandaker, Mahzabeen Emu	A Topology-Aware LLM-Augmented Digital Twin Framework for Scalable IoT Device Management	2026	Early Access	Internet of Things Topology Management	The growing scale and dynamic nature of Internet of Things (IoT) deployments demand management approaches that can maintain accurate system awareness. Existing large language models (LLMs) can reduce the interface burden of network management. However, without explicit grounding in the physical system state, they may generate nonexistent devices, incorrect topological relations, or non-executable management actions. To address this problem, this paper proposes a digital twin (DT) grounded LLM augmented management framework for IoT device management. The framework uses the DT as a structured state source, allowing the model to access topology consistent device, connection, and status information before generating management responses. A topology importance driven adapter training method, implemented through Hierarchical Importance Organizer (HIO), is further developed to encode hierarchical paths and critical nodes into training samples. We further characterize how grounded management degrades as the DT drifts from the physical topology, isolating the robustness contribution of topology-aware adaptation. Across 34,200 completed per-sample model outputs, including a 7,200-output main benchmark and a 27,000-output topology-drift sweep, HIO is evaluated against schema-only prompting, a base plus DT model, and a GenTwin-like adapter. On the 1,800-sample main benchmark, HIO achieves 0.869 Direct F1, improving over the GenTwin-like adapter by 3.3 points and over the base plus DT model by 29.1 points. HIO also improves Exact Match from 0.753 to 0.827. The gain is most pronounced in topology-sensitive impact analysis, where HIO improves Direct F1 from 0.784 to 0.918. HIO has positive gains in all nine topology–scale cells, with 95% confidence intervals excluding zero in seven cells. Under DT topology drift, HIO consistently outperforms the GenTwin-like adapter over δ ∈ [0, 0.20] and degrades more slowly, with Direct F1 degradation slopes of −0.157 versus −0.189.	10.1109/TNSM.2026.3736467
Junior Momo Ziazet, Brigitte Jaumard	Energy Efficient Placement of Logical Functionalities in 5G Networks	2026	Early Access	Energy Copper Modeling	Although 5G networks are more efficient in terms of power consumption to traffic ratio, efforts still need to be made to further increase energy efficiency not only for the radio part, but also with respect to the growing cloud component with edge servers. Consolidation of traffic workloads onto shared infrastructures is a key feature of cloud computing to reduce energy consumption, and logical functionality placement plays a key role in this regard. Here, in the cloud RAN context, we propose a unified and energy-aware logical placement of 5G E2E functionalities, i.e., distributed units (DUs), centralized units (CUs), and user plane functions (UPFs), together with traffic routing. The placement problem is formulated as a large-scale integer linear program and solved using a column generation-based decomposition technique, complemented by an efficient heuristic to ensure tractability and improved scalability. The model captures key network and cloud (compute) resources, jointly optimizing the placement of DU, CU, and UPF components, along with traffic routing, to minimize energy consumption while maintaining low latency and high Quality of Service (QoS). Numerical results, based on an open Montreal traffic dataset, demonstrate that the proposed column generation algorithm achieves near-optimal solutions, while the heuristic approach offers significantly better scalability with consistently strong performance. The proposed methods reduce energy consumption by up to 14% and maintain low-latency service delivery. Furthermore, the results highlight that static, peak-time-based placement strategies can lead to inefficiencies throughout the day, emphasizing the importance of accounting for broader temporal traffic patterns.	10.1109/TNSM.2026.3729149
Mubashir Murshed, Glaucio H. S. Carvalho, Robson E. De Grande	Holistic Intelligent Traffic Steering Management in Multi-RAT Vehicular Networks	2026	Early Access	Radio access technologies Rats Vehicles	Multiple Radio Access Technology (multi-RAT) environments provide a promising foundation for service-aware communication in intelligent transportation systems (ITS) and smart cities. However, traffic steering (TS) in highly mobile and ultra-dense vehicular networks remains challenging due to dynamic network conditions, heterogeneous RAT capabilities, varying vehicle requirements, packet loss, latency, and frequent ping-pong RAT switching. In this context, we propose Holistic Intelligent Traffic Steering (HITS), a proactive bi-level TS management framework for multi-RAT vehicular networks. HITS integrates centralized network-wide guidance with local vehicleside decision-making. At the central level, a Graph Convolutional Network–Long Short-Term Memory (GCN–LSTM) model captures holistic spatio-temporal network dynamics and evaluates RAT optimality. At the local level, a State-Action-Reward- State-Action (SARSA) reinforcement learning agent performs adaptive, vehicle-specific RAT selection using local observations and central-level optimality guidance. Results show that HITS achieves up to 6.5% higher average throughput, reduces packet loss ratio by more than 30.2%, lowers latency by nearly 12.2%, and reduces the ping-pong RAT switching rate by over 24% compared with baseline and state-of-the-art (SoTA) TS approaches.	10.1109/TNSM.2026.3729840
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
Amr Aboeleneen, Mohamed Abdallah, Aiman Erbad, Amr Salem	CIVIC: Cooperative Immersion Via Intelligent Credit-sharing in DRL-Powered Metaverse	2026	Early Access	Resource management Modeling Metaverse	The Metaverse faces complex resource allocation challenges due to diverse Virtual Environments (VEs), Digital Twins (DTs), dynamic user demands, and strict immersion needs. This paper introduces CIVIC (Cooperative Immersion Via Intelligent Credit-sharing), a novel framework optimizing service-profile provisioning and budget-credit sharing among multiple Metaverse Service Providers (MSPs) to enhance user immersion. Unlike existing methods, CIVIC integrates VE rendering, DT synchronization, credit sharing, and immersion-aware provisioning within a cooperative multi-MSP model. The resource allocation problem is formulated as two NP-hard challenges: a non-cooperative setting where MSPs operate independently and a cooperative setting utilizing a General Credit Pool (GCP) for dynamic budget support. Using Deep Reinforcement Learning (DRL) for tuning resources and managing cooperating MSPs, CIVIC achieves 12-36% higher request completion, 23-70% higher fulfillment rates, 20-60% more served clients, and up to 51% more fairly distributed requests, all with competitive costs. Extensive experiments demonstrate CIVIC’s resilience, adaptability, and robust performance under dynamic load conditions and unexpected demand surges, making it suitable for real-world distributed Metaverse infrastructures.	10.1109/TNSM.2026.3737119
Larisa-Mihaela Tufeanu, Marius-Constantin Vochin, Frank Y. Li	Residual Artifact Governance With ML-Enabled Garbage Collection Prediction in Kubernetes-Based 5G/6G Service-Based Architecture	2026	Early Access	Management Retrieval augmented generation Monitoring	The evolution of the service-based architecture (SBA) requires cloud-native core networks, and its open and modular design nature makes Kubernetes the most eminent platform for SBA implementation. One critical-yet-underexplored issue when deploying the SBA based on Kubernetes is the accumulation of residual artifacts that persist and degrade observability and stability inside Kubernetes clusters. In this paper, we propose residual artifact governance (RAG), a non-intrusive add-on module to the SBA that enforces bounded garbage collection (GC) policies without modifying the underlying architecture. The module follows a master–agent structure: a GC-master derives data-driven machine learning-enabled cleanup policies using the GC data provided by GC-agents, while GC-agents co-located at each network function execute deterministic, auditable cleanup actions. As a proof-of-concept demonstration, we implement a Kubernetes-based prototype to validate the operability and feasibility of RAG. While a machine learning predictor located at the GC-master provides early warning signals on time-to-collapse caused by the accumulation of residual artifacts, periodic fractional cleanup actions at GC-agents keep residual peaks bounded under both nominal and stress leak regimes. Results reveal that the accumulation of residual artifacts can be measured, predicted, and controlled using Kubernetes-native mechanisms through our RAG enhancement while remaining compatible with 3GPP-aligned observability and stability assurance principles.	10.1109/TNSM.2026.3734448
Ahmed Rjiba, Hicham Lakhlef, Joachim Bruneau-Queyreix, Meriem Afif	Federated Learning in Fog Computing within IoT Environments: An up-to-date and comprehensive survey	2026	Early Access	Federated learning Internet of Things Edge computing	The Internet of Things (IoT) connects diverse, resource-constrained devices, driving innovation in domains such as healthcare, smart cities, and industrial automation. However, the exponential growth of IoT devices poses critical challenges in data processing, privacy, security, and latency. Fog Computing (FC) mitigates these issues by decentralizing computational resources, processing and storing data locally to enable low-latency, high-quality services. This makes FC an ideal platform for integrating Federated Learning (FL), a decentralized machine learning paradigm that trains models locally on IoT devices and shares only aggregated updates, preserving data privacy. Since its introduction, FL has garnered considerable attention for enabling privacy-preserving collaborative model training in distributed environments. The convergence of IoT, FC, and FL offers substantial opportunities to advance IoT system performance, but it also presents challenges in resource allocation, security, energy efficiency, computational complexity, and system heterogeneity. This survey provides a comprehensive and up-to-date analysis of the integration of FL and FC within IoT environments, exploring their synergies, challenges, and state-of-the-art advancements.We review critical aspects, including infrastructure enhancements, security mechanisms, and the emerging role of Digital Twin (DT) technology, which creates virtual replicas of IoT devices to optimize system efficiency and real-time performance. Through case studies in healthcare and smart cities, we highlight practical applications of FL-FC integration. We compare our work with existing surveys, highlight its specific focus on the FL-FC-IoT-DT convergence, and identify open challenges and future research directions toward secure, scalable, and intelligent IoT ecosystems.	10.1109/TNSM.2026.3731410
Stephen Jasina, Loqman Salamatian, Joshua Mathews, Scott Anderson, Paul Barford, Mark Crovella, Walter Willinger	Matisse: Visualizing Measured Internet Latencies as Manifolds	2026	Early Access	Manifolds Internet Measurement	Manifolds are complex topological spaces that can be used to represent datasets of real-world measurements. Visualizing such manifolds can help with illustrating their topological characteristics (e.g., curvature) and providing insights into important properties of the underlying data (e.g., anomalies in the measurements). In this paper, we describe a new methodology and system for generating and visualizing manifolds that are inferred from actual Internet latency measurements between different cities and are projected over a 2D Euclidean space (e.g., a geographic map). Our method leverages a series of graphs that capture critical information contained in the data, including well-defined locations (for vertices) and Ricci curvature information (for edges). Our visualization approach then generates a curved surface (manifold) in which (a) geographical locations of vertices are maintained and (b) the Ricci curvature values of the graph edges determine the curvature properties of the manifold. The resulting manifold highlights areas of critical connectivity and defines an instance of “Internet delay space” where latency measurements manifest as geodesics. We describe details of our method and its implementation in a tool, which we call Matisse, for generating, visualizing and manipulating manifolds projected onto a base map. We illustrate Matisse with three case studies: a simple example to demonstrate key concepts, and visualizations of the US and Europe public Internet to show Matisse’s utility.	10.1109/TNSM.2026.3730274
Yao Xin, Yuqiao Luo, Shufan Cao, Chongwu Dong, Qingfeng Tan	HBT: A Hybrid Bidding Tree for High-Performance Packet Classification	2026	Early Access	Heterojunction bipolar transistors Trees (botanical) Vegetation	Traditional packet classification algorithms based on decision trees often rely on rule replication to increase lookup speed, which inevitably leads to memory explosion. Conversely, existing zero-replication methods frequently suffer from extreme tree depth and structural fragmentation. To address this dilemma, this paper proposes the Hybrid Bidding Tree (HBT), a high-performance architecture designed to enforce zero-replication while sustaining deterministic lookup throughput. First, HBT employs an Overlap-Aware Rule Decomposition (OARD) framework to proactively isolate topologically entangled rules, purifying the primary geometric space. Second, HBT introduces a dynamic competitive bidding mechanism for tree construction. At each node, a discrete bit-selection path and a continuous range-partitioning path compete to determine the optimal splitting strategy based on local geometric heterogeneity. Finally, to guarantee an O(N) memory boundary, unpartitionable residual rules are assigned to a single-level Onion-Peeling fallback structure, preserving linear memory growth while introducing additional sequential checks in the auxiliary path. Experimental evaluations on ClassBench-ng rulesets containing up to 256k rules demonstrate the efficacy of HBT. Compared with state-of-the-art algorithms such as PT-Tree and TupleTree, HBT achieves the highest lookup throughput across all twelve evaluated rulesets at both the 128k and 256k scales, while maintaining strong memory efficiency and highly competitive construction and update latencies.	10.1109/TNSM.2026.3734240
Hamidreza Mazandarani, Masoud Shokrnezhad, Tarik Taleb	A Semantic-Aware Multiple Access Scheme Leveraging Spatial Redundancy for Uplink-Dominant Network Services	2026	Early Access		The transition toward semantic-aware communication offers a paradigm shift for next-generation mobile networks, promising to decouple information significance from raw data transmission. Despite advances in semantic extraction, the integration of semantic intelligence into the Medium Access Control (MAC) layer remains underexplored, particularly in exploiting spatial correlations among users. To address this, we introduce a novel multiple access scheme designed for uplink-dominant network services. This framework optimizes the trade-off between spectrum utilization and sustainability by formulating variable-packet-length access as distinct α-fairness and energy efficiency problems. A key innovation of our approach is the quantification of spatial redundancies through novel metrics of self-throughput and assisted-throughput, which account for the semantic correlation of data across user equipment. We analyze these formulations to identify optimal bounds before proposing PRISM (Protocol for Redundancy Identification in Semantic Multiple-access). Grounded in Model-free Multi-Agent Deep Reinforcement Learning (MADRL), PRISM enables devices to autonomously govern spectrum access using only local observations. Extensive evaluations demonstrate that PRISM successfully leverages redundancies to outperform semantic-oblivious schemes, achieving up to 90% of the centralized optimal benchmark and improving both objectives by up to 2× across diverse user-semantic association matrices. These results validate PRISM as a viable candidate for future distributed mobile network applications, complemented by orthogonal Multiple Access Schemes where signals are multiplexed in the semantic domain.	10.1109/TNSM.2026.3737571
Vinícius Gruske Domeles, Laura Rodrigues Soares, Jéferson Campos Nobre, Edison Pignaton De Freitas	An Energy Cost-Benefit Analysis of Client-Side VPNs on CPE Devices	2026	Early Access	Energy Licenses Nuclear facility regulation	The reduction of CO2 emissions and conscientious use of energy resources is one of the biggest current challenges. Computer networks and the Internet are no exception to the global necessity of reassessing current energy consumption paradigms, and security mechanisms are some of the most costly in the networking stack. In the other hand, Customer-Premises Equipment (CPE) devices at the edge of the Internet structure play a significant role in service provisioning and securing the connection of the customer. As such, the impact of standard security tools on the energy consumption profile of these devices should be studied in depth. In this context, this work evaluates the energy cost-benefit of client-side Virtual Private Networks (VPNs) implemented on commercial CPE devices. Through experimental measurement and precise instrumentation, both energy consumption and network performance across different traffic profiles are analyzed. The main finding is that the use of VPNs can reduce the energy efficiency of the CPE per megabyte transferred by half, even under moderate load, highlighting a significant energy overhead imposed by security mechanisms on edge devices. Furthermore, the study shows that the most suitable protocol depends directly on scenario-specific requirements. Finally, the study proposes comparative metrics, a device-protocol calibrated model and presents the future directions for assessing the energy impact of Software-Defined Wide Area Network (SD-WAN) architectures.	10.1109/TNSM.2026.3733609
José Antonio Pastor Valera, Martin Husák, Jesús García Rodríguez, Jorge Bernal Bernabé, Antonio Skarmeta	Real Time Cyber Situational Awareness for 6G Networks Leveraging Spatial Metrics	2026	Early Access	Modeling Timing Fluid flow	The dynamic and heterogeneous nature of 6G networks demands continuous, real-time cyber situational awareness (CSA) to support cognitive security operations such as behavior analysis, threat hunting, and adaptive defense. Traditional CSA frameworks like CRUSOE capture structural and mission-level data but cannot process the dynamic, high-frequency telemetry typical of 6G environments. This paper presents the Extended Infrastructure and Service Information Model (EISIM) and its implementation within a Cyber Situational Awareness Platform (CSAP) designed for real-time, context-aware security management. CSAP aggregates and models data from diverse sources into a unified graph representing assets, flows, services, and vulnerabilities, enabling continuous assessment of operational and security posture. The platform introduces novel spatial risk metrics, including Flow Load Centrality and Operational Risk Centrality, which integrate topological position, traffic intensity, vulnerability exposure, and resource capacity to quantify risk, exposure, and criticality. By combining workflow-driven data collection with real-time spatial analytics, CSAP enhances situational awareness and decision-making within 6G Security Operation Centers (6G-SOCs), enabling proactive identification of critical network elements and improved resilience through cognitive, data-driven management.	10.1109/TNSM.2026.3734629
Liwei Zhang, Tong Zhang, Xiaoqin Feng, Wenxue Wu, Hao Yang, Ping Liu, Yanying Ma, Fengyuan Ren	Leveraging Hot Standby Routing to Improve Reliability in TSN	2026	Early Access	Fluid flow Timing Joining processes	Time-Sensitive Networking (TSN) is widely deployed in industrial networks because it can provide deterministic transmission services for Time-Triggered (TT) flows. Link failures pose severe threats to the reliability of TT flows. Frame Replication and Elimination for Reliability (FRER) defined by IEEE 802.1 CB tolerates such failures by transmitting the same frames via disjoint paths, but this introduces excessive bandwidth overhead. To this end, we present a Hot Standby Routing (HSR) mechanism tailored for TSN to ensure the reliability of TT flows while minimizing bandwidth usage. Unlike FRER, HSR can locally reroute a single frame to achieve tolerance to link failures. Specifically, the primary and secondary paths are computed hop-by-hop for each TT flow and installed on the switches in the network. Under normal conditions, the secondary path is in a silent standby state. If the primary path fails, the affected TT flow will be seamlessly rerouted to the secondary path by the local switch for transmission. The simulation results show that HSR can provide highly reliable transmission for TT flows while significantly reducing bandwidth consumption. Furthermore, HSR exhibits stronger robustness in large-scale networks.	10.1109/TNSM.2026.3733170
Marco Garofalo, Luca D’Agati, Laura García, Rafael Asorey-Cacheda, Antonio-Javier Garcia-Sanchez, Joan Garcia-Haro, Antonio Puliafito, Giovanni Merlino, Francesco Longo	Trustless SLA Enforcement and Roaming in LoRaWAN through Smart Contracts	2026	Early Access	Roaming Service level agreements LoRaWAN	LoRaWAN is widely used for Internet of Things (IoT) services that require long-range, low-power wireless connectivity. As deployments grow, roaming between different network operators becomes increasingly important to maintain service continuity for mobile IoT devices. In practice, however, roaming still depends on bilateral agreements and trusted intermediaries, which limit scalability and reduce transparency in multi-operator settings. This work introduces a blockchain-based roaming architecture that uses Algorand smart contracts to automate Service Level Agreement (SLA) management between providers. The system supports dynamic roaming agreements, immutable packet accounting, and transparent settlement. In our system, the enforced guarantee concerns forwarding-level service quality at the roaming interface, namely payment conditional on observed delivery ratio, rather than deterministic radio-layer latency or jitter guarantees. We implemented the full infrastructure, including a custom Gateway Bridge that extracts the Network Identifier (NetID), a blockchain service that interacts with Algorand smart contracts for SLA validation, and a decentralized provider catalog for operator discovery. We evaluated the system on a testbed with production-grade ChirpStack network servers and compared it with our previous non-blockchain implementation. Both versions achieve comparable throughput (5800–5900 packets/minute with 1000 devices) and maintain 99% packet forwarding efficiency. Blockchain integration adds measurable overhead, including a forwarding latency overhead in the 400–490 ms range for SLA validation, largely independent of the underlying network delay, but remains acceptable for delay-tolerant IoT services. Overall, the results show that decentralized LoRaWAN roaming can be implemented without breaking compatibility with existing network architectures.	10.1109/TNSM.2026.3734694
Serkut Ayvaşık, Alba Jano, Fidan Mehmeti, Wolfgang Kellerer	Sentinel: Vision-Based Signaling-Free SNR Prediction for Proactive 5G Resource Management	2026	Early Access	Resource management 5G mobile communication Convolutional neural networks	Reliable and efficient radio resource management in 5G systems critically depends on accurate Channel State Information (CSI) availability at the base stations. Traditionally, base stations perform scheduling, resource allocation, and link adaptation using the Channel Quality Indicator (CQI), either computed directly for uplink or obtained via CSI feedback reports for downlink. Both uplink and downlink procedures rely on frequent pilot and feedback transmissions, introducing significant overhead that challenges scalability and ultra-reliable communication demands. In this work, we introduce Sentinel, a vision-based machine learning system that leverages grayscale image sequences from an indoor environment to predict the SNR between user equipment and base station with a foresight window of 200 ms. Sentinel’s SNR prediction enables flexible CQI acquisition, allowing different SNR-to-CQI mappings without modifying the system, and eliminates the need for CQI-related pilot or feedback signaling. The proposed system is evaluated in a dynamic multi-user scenario comprising three heterogeneous 5QI service profiles across 40 users. Sentinel demonstrates superior CQI prediction performance, achieving substantial to near-perfect agreement with true CQI labels, as measured by the quadratic weighted kappa, and outperforming benchmark foresight-based CQI prediction models in both CQI classification and resource management effectiveness. Proactive resource management evaluations show that Sentinel meets the strict reliability targets of mission-critical 5QI services, achieving packet error rates below 10−4, and approaching 10−5 when integrated with signaling. Furthermore, Sentinel reduces total radio resource usage by up to 24% in the 40-user scenario by eliminating CQI-related signaling overhead.	10.1109/TNSM.2026.3735937
Amr Aboeleneen, Mohamed Abdallah, Aiman Erbad, Amr Mohamed	ZTCI: Zero-Touch Cooperative Immersion, a Plug-and-Deploy Deep Reinforcement Learning-Based Framework for Resource Allocation and Cooperation in the Metaverse	2026	Early Access	Resource management Modeling Metaverse	The Metaverse requires edge providers, termed Metaverse Service Stations (MSSs), to provision high-quality virtual environments (VEs) and faithful digital twins (DTs) for heterogeneous virtual venues under tight compute and network budgets. In multi-MSS deployments, localized demand surges can overwhelm one station while neighboring stations remain underutilized. Cooperation is therefore essential but challenging because venue requirements are heterogeneous, demand is bursty, and proximity-based neighborhood sets change over time. These dynamics motivate zero-touch operation that requires neither retraining nor reconfiguration at deployment. We introduce the General Optimized Agent (GOA), a plug-and- deploy Deep Reinforcement Learning (DRL) agent that jointly optimizes per-venue VE/DT service levels and inter- MSS resource sharing. GOA is trained through staged curriculum learning and uses a position-aware set encoder with learned positional embeddings and attention-based pooling to map variable-size neighbor sets to fixed-dimensional representations. This design yields a single policy that generalizes across MSS types, budget levels, and dynamic team sizes. Extensive evaluation shows that GOA improves request satisfaction, load balancing, and cost efficiency over representative baselines, supporting scalable, zero-touch Metaverse cooperation under realistic infrastructure constraints.	10.1109/TNSM.2026.3737187
Kim Hammar, Rolf Stadler	Online Identification of IT Systems through Active Causal Learning	2026	Early Access	Modeling Learning (artificial intelligence) Costing	Identifying a causal model of an IT system is fundamental to many branches of systems engineering and operation. Such a model can be used to predict the effects of control actions, optimize operations, diagnose failures, detect intrusions, etc., which is central to achieving the longstanding goal of automating network and system management tasks. Traditionally, causal models have been designed and maintained by domain experts. This, however, proves increasingly challenging with the growing complexity and dynamism of modern IT systems. In this paper, we present the first principled method for online, data-driven identification of an IT system in the form of a causal model. The method, which we call active causal learning, estimates causal functions that capture the dependencies among system variables in an iterative fashion using Gaussian process regression based on system measurements, which are collected through a rollout-based intervention policy. We prove that this method is optimal in the Bayesian sense and that it produces effective interventions. Experimental validation on two testbeds shows that our method enables accurate identification of a causal system model while inducing low interference with system operations.	10.1109/TNSM.2026.3736090
Messaoud Ait-Yahia, Wael Jaafar, Rami Langar	Joint Design of Blockchain-Enabled Service Placement and Task Assignment in Vehicular Fog Computing Networks	2026	Early Access	Delays Timing Optimization	Driven by the evolution of blockchain and fog computing, vehicular networks are increasingly capable of supporting latency-sensitive applications with enhanced security and trust guarantees. However, the joint resource allocation for task offloading and blockchain services has been insufficiently investigated in existing works. To address this gap, this paper proposes a framework for jointly allocating resources of blockchain, users’ virtualized services, and Mobile Edge Computing (MEC) task assignment in Vehicular Fog Computing (VFC) networks. Specifically, we formulate the optimization problem as an integer nonlinear programming model aiming to maximize the satisfaction rate of users’ service requests while minimizing the corresponding blockchain operation time under mobility, queuing, instantiation, and resource constraints. To solve it in a timely manner, we design two-stage hierarchical low-complexity solutions, namely a Particle Swarm Optimization-based Joint Blockchain-enabled Service placement and Task Assignment algorithm (PSO-JBSTA), and a Genetic Algorithm-based approach (GA-JBSTA). Through extensive simulations, we demonstrate the effectiveness of PSO-JBSTA (resp. GA-JBSTA) and their adaptability to network conditions, achieving an average 35% (resp. 24%) improvement in users’ service satisfaction rate and 9.5% (resp. 10.2%) reduction in average blockchain validation delay compared with the baselines.	10.1109/TNSM.2026.3737068
Abdul Samim, Attiq Ur Rehman, KyungHi Chang	Intelligent Handover Management for 6G LEO Satellite Constellations: A Predictive Multi-Agent PPO Approach	2026	Early Access	Satellites Handover Loading	The integration of Low Earth Orbit (LEO) satellite constellations into 6G networks promises ubiquitous connectivity, yet poses unprecedented challenges for handover management due to rapid orbital motion and dynamic channel conditions. Traditional reactive handover algorithms, designed for quasistatic terrestrial networks, fail to address the multi-dimensional optimization requirements of LEO systems where satellites move at velocities exceeding 7 km/s and user-satellite connections last only 2-4 minutes. This paper proposes a Predictive Multi-Agent Proximal Policy Optimization (PMA-PPO) framework for SNR-aware load-balanced handover management in dual-layer LEO satellite networks. The framework integrates three core components: Gated Recurrent Unit (GRU) networks for temporal forecasting of channel conditions and satellite loads, distributed PPO agents for autonomous handover decision-making, and a coordination mechanism that balances signal quality with load distribution. Through comprehensive simulations of a realistic dual-layer constellation, PMA-PPO achieves significant performance improvements: up to 77.7% reduction in handover failure rates, 74.06% reduction in satellite overload duration, 35.13% improvement in throughput fairness, and ping-pong handover rates consistently below the practical 5% threshold across all load conditions, compared to state-of-the-art base-line approaches. The proposed approach achieves polynomial computational complexity versus exponential cost for exhaustive optimization, making it suitable for real-time deployment in large-scale LEO constellations.	10.1109/TNSM.2026.3735527