Data collection is the most critical capability of wireless sensor networks. Recent studies have shown that sink mobility along a fixed path can improve energy efficiency and simultaneously lead to high network throughput. However, achieving these improvements results in increased data transmission delay. To address this issue, we propose a novel data collection scheme in which all sensed data must be collected within a given time constraint while ensuring that the energy overhead is minimal. This novel data collection model is first formulated as an integer linear programming problem and then solved using an effective hybrid search method named probabilistic learning-based optimal node assignment (PLONA). The method integrates a reinforcement probability learning-based framework to guide the search toward new promising regions and a mixed feasible and infeasible search coupled with a diversified late acceptance strategy. Additional features of PLONA include an innovative transplant for generating diversified, high-quality initial solution and a streamlining technique to accelerate candidate solution evalution. The proposed PLONA algorithm is rigorously evaluated on extensive public benchmarks. The results demonstrate that PLONA matches CPLEX on all 22 small and medium-scale instances, outperforms CPLEX on 15 of 22 large-scale “DataColl” instances and on the majority of the 36 independent benchmark cases, and surpasses the state-of-the-art heuristic MDGOSP across most configurations while requiring substantially less computation time.
Intent-Based Networking (IBN) is a key paradigm in sixth-generation networks to manage the heterogeneity, scalability, and dynamism of resources across the Cloud Continuum. In distributed environments, resource allocation and orchestration are hindered by the trust among end-users and the complex chains of services between multiple stakeholders and domains. Traditional Service Level Agreements (SLAs) are insufficient to address these dynamics, requiring adaptations to incorporate trust characteristics (Trust Level Agreements, TLAs) that define trustworthiness thresholds, monitor real-time variations, and enforce compliance throughout a relationship. Accordingly, this article presents a trust-aware monitoring framework that establishes a monitoring-to-Knowledge Graph (KG) pipeline to track intent compliance. The framework leverages telemetry from Cloud Continuum resources, following RFC 9417 for service assurance, to evaluate operational states and assign health scores that lay the foundation for future trustworthy service selection. A tailored trust-oriented ontology and KG ensure semantic alignment, thereby enhancing advanced reasoning capabilities and a broader pattern recognition. The framework is validated under attack-scenario simulation experiments, achieving promising results (0.771 ± 0.043 F1-Score, 0.771 ± 0.090 precision, and 0.810 ± 0.041 recall) in detecting and evaluating relevant changes in service health. These results show its feasibility and potential to enhance trust-aware automation and intent compliance in 6G IBN environments.
The convergence of Artificial Intelligence, the Industrial Internet of Things (IIoT), and collaborative robotics in Industry 5.0 exponentially multiplies attack surfaces while dissolving the well-defined perimeters on which traditional security models rely. Zero Trust Architecture (ZTA), grounded in the principle of never trust, always verify, reorients industrial cybersecurity from boundary defence to continuous identity verification, dynamic access control, and least-privilege policy enforcement across every device, user, and data flow. Despite growing adoption interest, ZTA research for IIoT remains fragmented across isolated mechanism categories and lacks a unified synthesis spanning the full spectrum of Industry 5.0 constraints, including brownfield legacy integration, real-time determinism, federated learning, and quantum-resilient cryptography. This systematic literature review, conducted according to PRISMA 2020 guidelines, synthesises 45 peer-reviewed original articles selected from an initial pool of 492 documents published between 2022 and 2026. A five-category ZTA taxonomy is developed covering authentication and access control, anomaly detection and intrusion prevention, blockchain-enabled trust management, federated and split learning, and post-quantum and hardware-enforced security. A 15-dimension coverage analysis reveals persistent gaps in legacy-compatible adaptive trust models and blockchain-secured distributed data storage. Quantitative synthesis finds that ZTA implementations achieve mean authentication latency of 35 ms, IDS accuracy of 95.2% peaking at 99.9%, and blockchain-enabled federated learning consensus within 250 ms, while gateway-optimised architectures demonstrate communication latency reductions of up to 73%. These findings point toward IEC 62443-compliant, gateway-based adaptive ZTA proxies integrated with blockchain-secured distributed storage as the priority research direction for brownfield Industry 5.0 deployments.
