Search Results (173)

Advanced wireless sensor networks powered by artificial intelligence are increasingly required for applications demanding continuous monitoring, autonomous operation, reliable communication, and fast decision support. This systematic review examines recent work from 2023 to 2026 on energy-efficient, AI-enabled wireless sensor networks (WSNs) in mission-critical environments, with particular focus on power electronics and urban infrastructure systems. The authors synthesize a corpus of 50 DOI-indexed studies satisfying inclusion criteria that received qualitative thematic coding and comparative analysis. Other references were only cited to provide historical, methodological, or technical context and were not included in the systematic review corpus. As such, our results show that AI can improve WSN energy behaviour through routing and clustering, edge AI, reinforcement learning, fuzzy logic, metaheuristic optimization, and AI-based security. At the same time, energy efficiency cannot be treated as an isolated performance target. In mission-critical systems, security, latency, and reliability are closely interlinked requirements. The review concludes that future work should move away from optimizing protocols in isolation, and instead focus on building lightweight, explainable, secure, and field-tested AI-driven WSN architectures suited to real operational environments. Full article

Background: Elite soccer performance emerges from the interplay of cognitive, emotional, psychophysiological, and tactical processes that operate in real time during matches. Advances in wearable sensors and artificial intelligence (AI) now allow continuous monitoring of physiological and psychological states. They also allow modeling of how these states relate to tactical and physical performance. Existing reviews have examined machine learning in soccer, heart rate variability (HRV) monitoring, and psychological determinants of performance separately. No scoping review has mapped the intersection of AI analytics, wearable psychophysiological monitoring, and psychological performance constructs as one integrated decision-support framework in elite soccer. Aim: The aim of this study was to map the available evidence on the integration of AI and machine learning with psychophysiological monitoring for performance modeling in elite soccer, to identify the psychological constructs already used as model inputs, to describe the wearable technologies and AI methods applied, and to set out the translational challenges and evidence gaps that need priority attention. Methods: The review followed the PRISMA extension for Scoping Reviews (PRISMA-ScR) and the updated Joanna Briggs Institute (JBI) methodology. The protocol was registered on the Open Science Framework (OSF). Six databases (PubMed/MEDLINE, Scopus, Web of Science, SPORTDiscus, IEEE Xplore, and PsycINFO) were searched from January 2000 to March 2026 using the Population–Concept–Context (PCC) framework. Two reviewers independently screened titles, abstracts, and full texts (Cohen’s kappa = 0.82). Results: Thirty-six sources met the eligibility criteria after screening of 3104 records. AI and machine learning have been applied widely to predict physical and tactical performance in soccer, yet they rarely include psychological constructs. Reported models (decision trees, gradient boosting, and artificial neural networks) reach high accuracy for physical outcomes in internal validation, for example, above 66% for injury risk. Multi-modal models that add physiological and psychological inputs report stronger prediction. These figures come mostly from internal validation, and external validation and overfitting controls are seldom reported, so they should be read as optimistic upper bounds. Psychological and psychophysiological inputs remain under-represented. Explainable AI (XAI) methods, in particular Shapley Addictive exPlanations (SHAP) values, are appearing, but validation with domain experts is scarce. HRV has been reviewed as a psychophysiological marker in soccer, yet its use within AI decision-support tools for real-time psychological readiness has not been mapped. Three translational challenges stand out: the ecological validity gap between laboratory cognitive tests and match-embedded psychophysiology; the interpretability problem of opaque AI in high-stakes decisions; and the data fragmentation problem created by disconnected physical, tactical, and psychological data streams. Conclusions: Integrating AI with wearable psychophysiological monitoring offers a credible route toward integrated performance modeling in elite soccer. Closing this gap calls for multi-modal frameworks that combine psychological constructs, physiological markers, and tactical data within explainable AI. Research priorities include ecologically valid psychophysiological assessment protocols, position-specific psychological profiling, and practitioner-validated tools that turn AI outputs into usable coaching recommendations. Full article

Energy-efficient and robust routing remains a critical problem in wireless sensor networks (WSNs), where limited energy resources and dynamic topologies hinder performance. To address this challenge, a novel cluster-based routing protocol FGOGNN is proposed in this paper, which integrates a Fungal Growth Optimizer (FGO) for adaptive cluster head (CH) selection and a Graph Neural Network (GNN) for inter-cluster routing. The FGO simulates fungal growth processes, ensuring balanced and energy-efficient CH selection while preventing premature convergence. In the routing phase, the GNN dynamically adapts routing paths by leveraging node energy, connectivity, and directional edge features, offering low computational overhead while maintaining accuracy. Extensive simulations show that FGOGNN outperforms existing routing protocols, extending network lifetime by up to 75% and improving throughput by 38%, with a reduction in end-to-end delay. These results demonstrate FGOGNN’s potential for deployment in real-time WSN applications, where energy efficiency and dynamic adaptability are paramount. Full article

Ion-imprinted polymers (IIPs) are advanced adsorbents featuring selective recognition cavities for targeted metal ion capture, offering a promising route to high-efficiency separation in extractive metallurgy. In the present work, the evolution, design principles, synthesis strategies, separation mechanisms, and practical applicability of IIPs for metal recovery from complex aqueous matrices are overviewed. Key material components, including functional monomers, crosslinkers, template ions, initiators, solvents, and support materials, are discussed in relation to adsorption capacity, selectivity, kinetics, stability, and recyclability. Major preparation routes, such as surface imprinting, bulk polymerization, in situ polymerization, and sol–gel methods, are critically compared to clarify their advantages and limitations. Recent applications for base metals, precious metals, and rare-earth elements demonstrate that IIPs can achieve high specificity and rapid equilibrium under optimized conditions. However, their translation from simulated solutions to real leachates remains constrained by interfering ions, organic contaminants, mass transfer resistance, incomplete template removal, and matrix complexity. Mitigation strategies, including sample pretreatment, improved polymer architecture, and hybrid supports, are therefore emphasized. Additionally, chemometric modelling, machine learning, or artificial intelligence-assisted design may be implemented to advance the prospects of IIPs in industry. Conclusively, IIPs represent a strong separation platform, yet industrial deployment requires robust validation with real feed streams and scalable regeneration protocols during column operation, as well as under chemically aggressive conditions at scale. Full article

Vehicular Ad Hoc Networks (VANETs) are essential components of Intelligent Transportation Systems (ITS), allowing communication exchanges between vehicles and road infrastructure elements. These networks face challenges from vehicular mobility, including frequent topology changes, link instability, and variable wireless channel quality. This paper presents an extensive evaluation of Software-Defined Networking (SDN) integrated with two traffic engineering technologies, Multi-Protocol Label Switching (MPLS) and Segment Routing (SR), applied to the AODV and OLSR routing protocols. Nine incremental configurations are evaluated for each protocol, ranging from the default protocol through MPLS-enhanced forwarding, SDN-based centralized optimization, combined SDN-MPLS and SDN-SR integration, to advanced configurations using distance-based IS-IS weighted topology metrics with both Fixed and Adaptive metric computation approaches. Two distinct SDN topology construction methods are compared: a Protocol-based approach that uses routing table entries with equal hop-count metrics, and a distance-based approach using IS-IS weighted metrics. The simulation uses a realistic urban topology with 50 vehicles and 5 RSUs, evaluated across several traffic patterns, representing different application types. Results demonstrate that SR with distance-based IS-IS metrics achieves the highest Packet Delivery Ratio (PDR) and lowest delay by leveraging RSU infrastructure as reliable forwarding relays. Moreover, the proposed SDN-SR framework reduces routing overhead and control-plane signaling, improving network resource utilization and thereby indicating its potential to enhance the energy efficiency of vehicular communication infrastructures. Full article

Short-term traffic speed forecasting based on sensor-derived traffic signals is important for route guidance, adaptive signal control, congestion management and intelligent transportation services. Accurate prediction remains challenging because urban traffic states are influenced by nonlinear temporal dynamics, inter-node relationships, heterogeneous contextual information and uncertainty in external conditions. This study proposes U-GRU, a staged forecasting model that integrates node-wise gated recurrent temporal encoding, a one-dimensional U-Net-based ordered-node feature transformation module, external-feature alignment and channel–temporal recalibration. Unlike graph-convolutional methods that explicitly propagate information through a predefined or learned road topology, U-GRU investigates whether a transformation path along the stored node order in the data files can provide a useful representation under an ordered-node assumption. POI-related and weather-related auxiliary matrices are used as external representations provided in the public dataset; because field-level semantic metadata are unavailable, they are treated as dataset-level auxiliary inputs rather than manually interpreted variables. The experiments use chronological train/validation/test splitting, validation-based checkpoint selection, five random seeds for U-GRU, an expanded baseline set including the STGformer baseline evaluated under the same SZ-taxi setting, ablation studies, weather-validity analysis and node-order sensitivity analysis. On the SZ-taxi dataset, U-GRU reports MAE values of 2.70–2.72, RMSE values of 4.04–4.12 and R² values of 0.83–0.85 across the 15–60 min forecasting horizons. The results suggest comparable or slightly better short-term forecasting performance under the evaluated dataset and protocol, while the conclusions remain dataset-specific and should be further examined on additional traffic networks. Full article

Artificial intelligence architecture is no longer adequately described by model size alone. Dense Transformers remain the reference architecture for language and multimodal reasoning, but production systems increasingly combine conditional computation, retrieval, memory, tools, verifiers, edge-cloud routing, observability and governance. This review makes three engineering claims. First, sparse Mixture-of-Experts models are currently the clearest capacity-scaling pattern, because they decouple total parameters from active per-token computation, although routing imbalance and distributed communication remain hard constraints. Second, state-space, recurrent and linear attention hybrids are best interpreted as attention-budgeting architectures: they reduce KV-cache and long-context costs, but do not yet displace dense attention in every reasoning regime. Third, JEPA-style latent world models change the learning objective from surface-token or pixel prediction to representation prediction, which is strategically important for perception and planning but still not a drop-in replacement for general language interfaces. To make the maturity claims auditable, this review uses a PRISMA-inspired search protocol, an explicit technology readiness rubric, quantitative comparison tables, hardware and memory-bandwidth analysis, deployment and reproducibility categories, and failure cases for RAG and agents. The main conclusion is that the optimal architecture is task- and constraint-dependent: small dense or hybrid models are often preferred for real-time edge inference, RAG and graph memory for changing enterprise knowledge, frontier dense or sparse models for difficult reasoning, and agentic workflows only when tool permissions, rollback, provenance and human oversight are engineered as first-class components. Full article

Underwater Wireless Sensor Networks (UWSNs) hold significant economic and military value; however, their routing protocols remain inherently vulnerable to external attacks. Unlike terrestrial networks, UWSNs cannot readily adopt complex cryptographic verification systems due to the high propagation delay, limited bandwidth, and low connectivity inherent in underwater acoustic channels. To address the wormhole attack—one of the most critical threats to UWSN routing—this paper proposes an intelligent routing scheme (UWSN-IRS) that not only detects wormhole attacks effectively but also identifies the source nodes and eliminates the threat. The proposed scheme comprises four integrated modules: a self-adjusting routing mechanism, a wormhole attack detection mechanism, a wormhole node localization mechanism, and an anti-cheating mechanism. The self-adjusting routing mechanism optimizes node distribution and intelligently searches for the optimal forwarding path. Upon the occurrence of a wormhole attack, the detection mechanism employs an artificial neural network to identify the compromised links and outputs a set of suspected wormhole nodes. Subsequently, the localization mechanism determines the exact positions of these malicious nodes through ranging and iterative positioning. Finally, the anti-cheating mechanism isolates the detected attacking nodes and deploys substitute nodes to fill the resulting monitoring voids. The experimental results demonstrate that the UWSN-IRS exhibits superior performance in attack scenarios, enabling reliable wormhole detection, precise attacker localization, and sustained normal network communication. Full article

Payroll-anchored retail borrowers—individuals whose monthly remuneration is routed into an account at the lending institution through a salary-project arrangement—constitute the volume backbone of unsecured consumer lending in Kazakhstan, generating the largest origination flow, the lowest realized default rate, and the majority of the systemic regulatory and capital sensitivities of second-tier banks. Payroll anchoring also changes the lender’s information set, which motivates a study of how that advantage translates into model performance and borrower outcomes. We design and internally validate an explainable hybrid artificial-intelligence framework stratified by client tenure into two production models: a Weight-of-Evidence (WOE) logistic-regression scorecard for new salary-project applicants, and a hybrid scorecard for repeat applicants, in which a stacked ensemble of LightGBM, CatBoost and a multi-head self-attention neural network contributes a single WOE-encoded predictor to a second-stage L2-regularized logistic regression. The hybrid recovers a substantial share of the ensemble’s discriminatory lift while preserving an auditable, monotone scorecard at the point of decision, and isotonic recalibration restores the predicted probabilities of default to the empirical bad-rate scale required for IFRS 9 expected-credit-loss accrual and risk-based pricing. We report discrimination, calibration and stability evidence under a strict anti-leakage protocol and set out the structural preconditions under which the architecture transfers to other emerging-market payroll-anchored portfolios. We are explicit about scope: a true out-of-time validation and a full group-conditional fairness audit are identified as required next steps rather than claimed here. The contribution is a reproducible, interpretable scoring design that exploits payroll visibility while retaining full coefficient interpretability inside the production decision engine. Full article

Safety-critical Industrial Internet of Things (IIoT) sensor networks deployed in disaster scenarios require intelligent routing mechanisms that prioritize mission-critical packets without relying on centralized coordination. Federated learning on resource-constrained edge nodes presents three primary challenges: the absence of an interpretable supervisory signal, the inability to act conservatively based on per-inference confidence, and vulnerability to partial node availability. The proposed FedCARE framework addresses these issues by employing a Mamdani Fuzzy Inference System to generate traceable criticality labels from multi-modal sensor telemetry, a dropout-aware aggregation protocol that normalizes over only reachable nodes, and a confidence-gated resolver that defers to symbolic fuzzy classification when model confidence is insufficient, otherwise applying an auditable maximization rule to prevent under-prioritization of safety-critical data. Evaluation on 50-, 100-, and 200-node Watts–Strogatz topologies under fault rates up to 50%, using the Edge-IIoTset and WUSTL-IIoT-2021 benchmarks, demonstrates 99.00% critical recall and up to $1.8\times$ higher overall-packet delivery compared to RPL-RP under severe fault conditions. Routing improvements are primarily attributed to fuzzy criticality labeling and multi-path replication. These findings indicate that fuzzy-supervised federated inference offers a practical and interpretable solution for safety-critical IIoT routing, with an observed energy overhead of 7.8% per delivered packet. Full article

The Mobile Reliable Opportunistic Routing (MROR) protocol improves data-forwarding reliability in Cognitive Radio Sensor Networks (CRSNs) through mobility-aware virtual contention groups and handover zoning. However, its heuristic decision logic is difficult to optimize under highly dynamic spectrum access and random node mobility. To address this limitation, we present DRL-MROR, a refined routing framework that incorporates deep reinforcement learning (DRL) to enable intelligent and adaptive forwarding decisions. In DRL-MROR, the secondary users (SUs) act as autonomous agents that observe local state information, including primary-user activity, link quality, residual energy, and neighbor-mobility patterns. Each agent learns a forwarding policy through a Deep Q-Network (DQN) optimized for long-term network utility in terms of throughput, delay, and energy efficiency. We formulate routing as a Markov Decision Process (MDP) and use experience replay with prioritized sampling to improve learning stability and convergence. The DQN used at each node is intentionally lightweight, requiring 5514 trainable parameters, about 21.5 kB of weight storage in 32-bit precision, and approximately 5.4k multiply-accumulate operations per inference, which supports practical deployment on edge-capable CRSN nodes. Extensive simulations show that DRL-MROR outperforms the original MROR protocol and representative AI-based routing baselines such as AIRoute under diverse operating conditions. The results indicate gains of up to 38% in throughput, 42% in goodput, a 29% reduction in energy consumed per packet, and an approximately 18% improvement in network lifetime, while maintaining high route stability and fairness. DRL-MROR also reduces control overhead by about 30% and average end-to-end delay by up to 32%, maintaining strong performance even under elevated PU activity and higher node mobility. These results show that augmenting opportunistic routing with lightweight DRL can substantially improve adaptability and efficiency in next-generation IoT-oriented CRSNs. Full article

Energy-efficient routing is critical for extending the operational lifespan of wireless sensor networks (WSNs). While the Power-Efficient Gathering in Sensor Information Systems (PEGASIS) protocol achieves high efficiency through chain-based data aggregation, its standard round-robin leader selection fails to account for dynamic node factors, such as residual energy and historical reliability. This often leads to premature energy depletion and network instability. To address these limitations, this paper proposes K-NN-PEGASIS, a data-driven machine learning framework that utilises a weighted k-nearest neighbours (K-NN) algorithm for intelligent leader selection. By processing a normalised feature vector comprising residual energy, distance to the base station (BS), node degree, and historical performance, the framework adaptively identifies optimal leaders in each round. Simulations conducted in MATLAB for networks ranging from 100 to 1000 nodes demonstrate that K-NN-PEGASIS improves network lifetime by up to 47.3% and reduces total energy dissipation by 52.8% compared to baseline algorithms. Furthermore, the framework provides passive resilience against routing attacks, reducing the selection of malicious leaders by 96% and maintaining a 32.3% higher packet delivery ratio under attack scenarios. Full article

Vehicular ad hoc networks (VANETs) operating in dense urban environments are characterized by highly dynamic topology, fluctuating traffic conditions, and stringent latency requirements, which significantly complicate reliable data routing and packet forwarding. To address these challenges, this paper proposes an Intelligent Fuzzy Protocol (IFP) for adaptive vehicle-to-vehicle data routing under uncertain and rapidly changing traffic scenarios. The proposed protocol integrates fuzzy logic decision making with the real-time vehicular context, including vehicle velocity, traffic congestion level, distance to road junctions, and data urgency, to dynamically select appropriate forwarding actions. IFP employs a structured fuzzy inference engine comprising fuzzification, rule evaluation, inference aggregation, and centroid-based defuzzification to determine routing and forwarding decisions in a decentralized manner. To further enhance performance robustness, the fuzzy membership parameters and rule weights are optimized using metaheuristic techniques, namely, genetic algorithms (GAs) and particle swarm optimization (PSO). Extensive simulations are conducted using NS-3 coupled with SUMO under realistic urban mobility scenarios and varying network densities. The simulation results demonstrate that IFP significantly outperforms conventional routing approaches in terms of end-to-end delay, packet delivery ratio, and routing overhead. In particular, the optimized IFP variants achieve notable reductions in latency and improvements in delivery reliability under high-congestion conditions, while maintaining low computational and communication overhead. These findings confirm that IFP offers an interpretable, scalable, and energy-aware routing solution suitable for large-scale intelligent transportation systems and next-generation vehicular networks. Full article

Satellite communication protocols are increasingly optimized in software-defined, multiorbital networks that combine broadband satellite systems, non-terrestrial 5G components, and inter-satellite transport. This review examines intelligent optimization across the physical, medium-access, network, and transport layers, with emphasis on what can be measured, what can be controlled, and what can be safely deployed under standards and operational constraints. This paper first positions the literature across DVB/ETSI, 3GPP NTN, CCSDS/DTN, LEO routing, and recent AI and digital-twin research. It then links standards-defined control surfaces to layer-specific measurements, feedback delays, and safety constraints and compares optimization families using deployment-relevant criteria such as observability, runtime predictability, verification burden, and robustness. The review argues that the central challenge is not only a simulation-to-reality gap but an evidence gap between experimental gains and operational trust. To address this gap, this paper analyzes delayed observability, rare events, bounded onboard compute, action surface mismatch, certification, and security; formalizes a generic constrained optimization problem with delayed observations and standards-compliant actions; and proposes a digital-twin-assisted research methodology supported by a worked beam-hopping example. The main conclusion is that future progress is most likely to come from hybrid, standards-compliant, and twin-assisted optimization methods whose performance claims are tied to calibration, traceability, and explicit rollback logic. Full article

The maritime industry is under increased challenges of balancing operational effectiveness and environmental responsibility. This study examines the application of intelligent agents as a technology that can align these two goals in the triple-bottom-line model that involves social responsibility, environmental footprint, and economic sustainability. An agent architecture taxonomy is outlined and adapted to the maritime industry, distinguishing between reactive, deliberative, hybrid, and multi-agent systems (MAS). The application of these architectures is analysed throughout the maritime domain. In the ship-centric environment, the analysis highlights the role of agents in autonomous navigation, energy-efficient meteorological routing, and predictive maintenance. The analysis in the port and supply-chain domain demonstrates a shift towards decentralized asset orchestration and logistic coordination rather than centralized control. The paper outlines certain barriers to widespread adoption, namely the reality gap of simulation-based training and the lack of transparency in deep-learning models (“black box” problem). The paper concludes by outlining a future research agenda proposing a use of explainable artificial intelligence (XAI), high-fidelity simulation-to-real transfer, and communication protocol standardization to continue the trend of developing strong autonomous capabilities in sustainable maritime logistics. Full article