Sep 2026· IEEE Transactions on Mobile Computing· Vol 25, pp. 14608-14624· 0 citations· 50 references
Abstract
The fourth industrial revolution drives AI-powered smart manufacturing through cloud-edge computing, enabling intelligent production processes and data-driven automation. To handle security concerns arising from massive IoT deployments, attribute-based access control (ABAC) has become essential for smart factories. It offers flexibility in dynamic environments by utilizing attributes of users, devices, and contextual conditions to decide whether an access request should be permitted or denied. However, the proliferation of IoT devices drastically increases the number of attributes, causing exponential growth in policy complexity and severe decision latency at resource-constrained edge nodes. To address this issue, we propose ABAC-Prune, a cloud–edge collaborative framework for ABAC policy pruning. The framework adaptively determines pruning strategies based on the real-time security state of the factory. Specifically, it employs deep reinforcement learning (DRL) for coarse-grained control in highly dynamic environments, while switching to a Deterministic Policy Optimizer (DPO) for fine-grained adjustment under quasi-static conditions. The pruned lightweight ABAC policy subset is then deployed on edge nodes for real-time access decisions. By continuously monitoring factory conditions and analyzing historical access requests, ABAC-Prune dynamically adjusts pruning strategies. Simulation results on our containerized digital-twin testbed show that ABAC-Prune reduces security response latency by 22% and improves operational efficiency by 30%, while maintaining robust security with anomaly rates consistently below 10%.
The growing amount of heterogeneous devices with scarce resources is compromising the security of the Internet of Things (IoT), as they are more likely to adapt to a fixed and identity-based access control. Conventional security systems tend to assume that once a device is authenticated, the network may be exposed to credential theft, firmware, and insider abuse. In this study a behavior-sensitive access control solution is presented, which integrates lightweight supervised Machine Learning (ML) on the IoT gateway to provide dynamic authorization. Unlike the traditional passive intrusion detection models, the proposed framework uses a Supervised Random Forest model to process real-time statistical feature summaries in terms of mean, standard deviation, and sparsity of the IoT telemetry data. The method converts the output of anomaly detection directly into access (full, restricted or blocked) levels. The system was implemented on a Flask-based gateway and tested with ToN-IoT benchmark dataset. The results of the experiments show an anomaly-class recall of 0.9986 (99.86%) with 91,169 correctly detected attack and 125 false negatives among the 91,294 attack instances, for a security-oriented Zero Trust profile. As an example, when rounded to two decimal places, this value is 1.00, but the unrounded value is reported so as not to suggest 100% detection. The enforcement layer focuses on reducing risk and removes or filters out requests that were determined to be malicious or unauthorized in the scenarios. The architecture is designed to provide low latency through feature extraction and inference on the edge, which provides data privacy because telemetry processing is locally done without relying on the cloud.
Yaseen Alduwayl, Abdullah T. Al-Essa, Mounir Frikha· International Journal of Adv...· 0 citations
The rapid proliferation of Internet of Things (IoT) devices within 5G-enabled smart city environments has introduced unprecedented challenges in communication protocol management across heterogeneous network architectures. With connected IoT devices projected to reach 21.1 billion by the end of 2025 and approximately 39 billion by 2030, existing static protocol selection mechanisms are unable to accommodate the dynamic Quality of Service (QoS) requirements of different smart city applications, such as enhanced Mobile Broadband (eMBB), Ultra-Reliable Low-Latency Communication (URLLC), and massive Machine-Type Communication (mMTC). This paper presents APO-DRL (Adaptive Protocol Optimization using Deep Reinforcement Learning), a framework that utilizes a Dueling Double Deep Q-Network (D3QN) combined with a Prioritized Experience Replay mechanism for intelligent, real-time communication protocol selection and parameter optimization in heterogeneous IoT networks. The proposed framework formulates the protocol optimization problem as a Markov Decision Process (MDP), wherein the DRL agent dynamically selects the optimal communication protocol (NB-IoT, LTE-M, LTE Cat-1, or 5G NR) and adaptively tunes transmission parameters based on real-time network conditions. Experimental evaluation in a 3GPP TR 38.901 Urban Macro simulation environment with N = 30 devices demonstrates that APO-DRL achieves a 138.9% improvement in average throughput compared to Static Allocation (60.00 vs. 25.12 Mbps), while simultaneously achieving the highest QoS satisfaction (83.38%) across all methods, albeit with higher energy consumption and packet loss than Static Allocation. Relative to D3QN+PER, APO-DRL exhibits substantially lower cross-seed throughput variance (±0.88 vs. ±11.03 Mbps), confirming that QA-PER produces a more stable and reproducible learned policy.
Saddam K. Alwane, Shereen S. Jumaa, M. Saleh et al.· IoT· 0 citations
The rapid proliferation of Internet of Things (IoT) devices has placed unprecedented pressure on the network edge, where applications such as augmented reality, real-time analytics, and autonomous navigation demand low latency and tight energy budgets that traditional cloud-centric architectures cannot meet. Multi-access Edge Computing (MEC) addresses this gap by relocating computation closer to end users, but the core question of where and how each task should be executed remains open: rulebased and single-objective offloading strategies fail to simultaneously balance service latency, energy efficiency, and user experience under dynamic, large-scale conditions. In this paper we propose TARLOT (Two-Agent Reinforcement Learning Offloading Tasks), a cooperative framework for threetier IoT–MEC–Cloud environments. TARLOT decouples the offloading decision from the resourceallocation problem and assigns each to a dedicated Q-learning agent, so that the two subproblems are specialised independently while still being optimised jointly. The framework is evaluated on PureEdgeSim under heterogeneous IoT workloads, device densities ranging from 200 to 2,400, and diverse application profiles, and is compared against five widely-used baselines (Random, Round-Robin, Trade-Off, Pure-Edge, and Pure-Cloud). At 2,400 devices, TARLOT delivers an average service time of 1.1 s (against 4.3 s for Pure-Cloud), a Quality of Experience of 0.77 (against 0.22 for Pure-Cloud), a task-failure rate below 2 % (against nearly 14 % for Pure-Cloud), and a per-device energy consumption of only 3.6 W (against 11.2 W for Pure-Cloud) — roughly a 68 % reduction. Balanced CPU utilisation across the local, edge, and cloud tiers further confirms that TARLOT prevents resource bottlenecks, establishing it as a practical solution for next-generation large-scale IoT deployments.
Oussama Lagnfdi, Marouane Myyara, A. Darif· International journal of Com...· 0 citations
The fast rise of wireless communication networks, including 6G, Internet of Things (IoT), and edge com puting, has created unprecedented demand for spectrum and energy resources.become a significant challenge in modern IoT networks due to heterogeneous devices, dynamic traffic patterns, and diverse QoS requirements. This study proposes a Deep Reinforcement Learning (DRL)–basedframework for optimizing network resource allocation in IoT environments using real-world sensor data. The proposed framework differs from existing studies that typically assess reinforcement learning methodologies under simplified wireless network assumptions and idealized conditions. Our method functions on heterogeneous IoT traffic produced by various device types, including sensors, actuators, and cameras, each possessing distinct Quality of Service (QoS) requirements. To ensure practical applicability, a realistic IoT simulation environment is developed, incorporating dynamic bandwidth release and queue-aware resource management to emulate real-world network behavior. Furthermore, a Deep Q-Network (DQN) agent with an enhanced exploration strategy is designed to improve learning stability and convergence performance, enabling more efficient and adaptive resource allocation in dynamic IoT scenarios. Experimental results show that the proposed DQN agent achieves a 26.7% improvement in cumulative reward compared to a random policy and consistently outperforms conventional heuristic approaches. This significant gain indicates that the agent effectively learns a structured resource allocation strategy rather than making uninformed decisions. These results confirm that reinforcement learning–based resource allocation provides a scalable and effective solution for IoT networks, particularly in environments characterized by large state spaces, dynamic network conditions, and stochastic traffic patterns.
L. Hoang, Van-Tam Hoang, Huu-Huy Ngo· International journal of Com...· 1 citation
The rapid deployment of artificial intelligence (AI) across healthcare, industrial control, supply-chain management, and Internet of Medical Things (IoMT) environments has intensified the need for computing architectures that can simultaneously provide low-latency inference, scalability, security, and operational resilience. Conventional cloud-centric AI architectures offer substantial computational capacity but may introduce communication latency, bandwidth dependency, privacy exposure, and single-point operational dependencies. Edge-cloud integration addresses these limitations by distributing data processing and AI inference across resource-constrained edge nodes, intermediate fog layers, and centralized cloud infrastructures. This research and review paper examines the architectural principles required to develop resilient and real-time AI decision systems through intelligent edge-cloud integration. The study synthesizes the provided literature on fog-cloud security, federated learning, intrusion detection, machine learning, blockchain-enabled IoMT, serverless computing, and healthcare cybersecurity. A conceptual architecture is developed around five functional layers: data acquisition, edge intelligence, collaborative fog coordination, cloud intelligence, and resilient decision orchestration. The analysis indicates that effective edge-cloud AI systems require adaptive workload placement, privacy-preserving distributed learning, security-aware inference, explainability, fault tolerance, and continuous resource optimization rather than simple physical distribution of computation. The findings further indicate that federated and lightweight learning mechanisms can reduce centralized exposure, while fog-cloud coordination can improve responsiveness for latency-sensitive applications. However, heterogeneous hardware, communication failures, model synchronization overhead, adversarial threats, and resource constraints remain significant barriers. The paper positions intelligent edge-cloud integration as an architectural strategy in which resilience, security, and inference performance are jointly optimized rather than treated as independent system properties.
Dr. Amir Hosseini, dr.nematollah karimi· International Journal of Adv...· 0 citations
The proliferation of Internet of Things (IoT) devices has generated unprecedented volumes of data, rendering traditional cloud-centric processing paradigms inadequate due to latency constraints, bandwidth limitations, and privacy concerns. This paper presents a comprehensive mathematical framework for distributed intelligence at the edge, enabling decentralized learning across heterogeneous IoT networks. We propose a novel Federated Edge Learning (FEL) architecture that integrates software-defined networking principles with gossip-based communication protocols to facilitate collaborative model training while preserving data locality. The framework addresses critical challenges including device heterogeneity, non-independent and identically distributed (non-i.i.d.) data distributions, resource constraints, and communication efficiency. We formalize the decentralized learning problem, derive convergence bounds under heterogeneous conditions, and introduce a multi-worker selection mechanism optimized through swarm learning principles. Experimental validation using real-world IoT datasets demonstrates that our approach achieves 30-50% reduction in training latency and 35-55% decrease in energy consumption compared to conventional federated averaging methods, while maintaining competitive accuracy of 92.86% on classification tasks . The proposed framework offers a scalable, privacy-preserving solution for deploying artificial intelligence at the network edge.
N.Durga, A. Mary Posonia, Selvakumar et al.· International journal of com...· 0 citations
Known for his clear and elegant writing style, Bertsekas shaped fields from control and optimization to large-scale computation and artificial intelligence.
