Search Results (58)

This paper investigates the integration of distributed computing and edge Artificial Intelligence (edge AI) as foundational enablers of sixth-generation (6G) mobile networks. Through a systematic review following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, encompassing over 200 peer-reviewed papers, architectural proposals, and standardization documents retrieved from IEEE Xplore, Scopus, Web of Science, MDPI, arXiv, ITU-R, 3GPP, and ETSI, this study provides a structured computational analysis of architectural approaches that integrate distributed computing paradigms and edge AI as core enablers of 6G. The analysis examines the evolution from cloud-centric to edge-centric computing, key edge AI techniques—including Federated Learning (FL), Split Learning (SL), and edge-adapted Large AI Models (LAMs)—and their role in enabling intelligent orchestration, resource optimization, and context-aware services. The comparative analysis demonstrates that edge computing architectures reduce end-to-end latency by 85–95% relative to cloud-centric deployments (under conditions of MEC servers within 1 km and 5G NR fronthaul), while federated learning with gradient compression achieves communication overhead reductions of up to 99% under IID data distributions and stable channel conditions. The results indicate that the tight integration of distributed computing and edge AI enhances network responsiveness, scalability, and adaptability, while also revealing persistent challenges related to orchestration complexity, resource constraints, security, and interoperability. The study concludes that holistic computational architectures and AI-native design principles are essential for the effective realization of 6G networks and for guiding future research and standardization efforts. Full article

The Model Context Protocol (MCP) enables Large Language Models (LLMs) to act as autonomous agents that orchestrate complex workflows over distributed systems, while MCP resolves integration bottlenecks by standardizing agent-to-resource communication. Its current registry relies on an unverified pointer architecture, exposing agentic workflows to supply chain poisoning and dynamic capability mutation (“Rug Pull”) attacks. This paper identifies this gap and proposes a three-layer architectural framework for a Trustworthy MCP Registry. The novelty of our contribution lies not in the individual standards employed (RFC 8615, Sigstore, and JCS/JWS are established technologies), but in their specific composition to address MCP’s unique runtime security requirements: (1) RFC 8615 Well-Known URIs for decentralized server discovery and domain-bound identity; (2) Sigstore Keyless signing to bind server artifacts to audited CI/CD environments without managing long-lived keys; and (3) JSON Canonicalization Scheme (RFC 8785) combined with JWS to provide deterministic, per-message integrity verification of live capability updates. We present a prototype implementation and an experimental evaluation conducted in a controlled, synthetic environment. Results indicate that the cryptographic overhead averages 0.61 ms per signing operation and that the Layer 3 mechanism correctly rejects all 100 simulated Rug Pull attempts, as expected by construction, since an attacker without the server’s private key cannot produce a valid signature. These findings suggest that the proposed approach is feasible and warrants further evaluation in real-world deployment settings. Full article

In IIoT edge computing, multi-edge server collaborative scheduling faces two core issues due to random task arrivals, heterogeneous resources, and complex topology: traditional model-driven methods cannot make dynamic decisions in dynamic environments, and conventional MARL fails to characterize inter-node topological dependencies and load correlations. To address this, this paper investigates the joint optimization of task offloading, computing resource allocation, and SFC orchestration in IIoT, constructs a cloud-edge-end collaborative architecture, and models the problem as a POMDP to minimize the overall system cost under multiple constraints. A graph-guided value-decomposition MARL method is proposed, which extracts spatial topology and neighborhood-load features of edge nodes via a GNN and combines them with the QMIX framework to realize multi-agent centralized training and distributed execution. Simulations show that the algorithm converges stably under different server scales and task loads, significantly outperforms benchmark algorithms, and can suppress performance degradation in high-load scenarios, demonstrating its robustness and scalability in complex industrial environments. Full article

Mobile edge computing (MEC) enables resource-constrained mobile devices to execute delay-sensitive and compute-intensive applications by offloading tasks to nearby edge servers. However, task orchestration in MEC is challenged by the highly dynamic system conditions, unreliable networks, and distributed edge environments. Moreover, as the number of mobile users, tasks, and distributed computing resources (edge/cloud servers) increases, the task orchestration process becomes more complex due to the expanded decision space and the need to efficiently allocate heterogeneous resources under latency and capacity constraints. As the decision space grows, exhaustive-search-based orchestration becomes computationally infeasible. Clustering approaches often rely on proximity-only grouping, while learning-based solutions require extensive training and parameter tuning. To address these challenges, this paper proposes a Multi-Criteria Hierarchical Clustering-based Task Orchestrator (MCHC-TO), a novel framework that integrates multi-criteria decision making with divisive hierarchical clustering for preference-aware and adaptive workload orchestration. Edge servers are first evaluated using multiple decision criteria, and the resulting preference rankings are exploited to form hierarchical preference-based clusters. Incoming tasks are then assigned to the most suitable cluster based on task requirements, enabling efficient resource utilization and dynamic decision-making. Extensive simulations conducted using an edge computing simulator demonstrate that the proposed MCHC-TO framework consistently outperforms benchmark approaches, achieving reductions in average service delay and task failure rate of up to 48% and 92%, respectively. These results highlight the effectiveness of combining multi-criteria evaluation with hierarchical clustering for robust and dynamic task orchestration in MEC environments. Full article

This paper presents a Proof of Concept (PoC) for PredictiveMaintenance MCP, an open-source server based on the Model Context Protocol (MCP) that supports machine condition monitoring and predictive maintenance via natural language interaction with Large Language Models (LLMs). The server constrains the LLM within an explicit perimeter of deterministic resources and tools for vibration-based diagnostics, including FFT spectral analysis with peak identification, envelope analysis for rolling element bearing defects, time-domain indicators, vibration severity assessment consistent with ISO standards and semi-supervised anomaly detection on extracted features. Each tool invocation produces structured outputs and artifacts that record inputs, parameters, and results. The LLM acts as an orchestrator that selects resources, configures parameters, invokes tools, and synthesizes conclusions anchored to computed evidence, thereby improving traceability and repeatability compared to unconstrained text-only interaction. End-to-end workflows are demonstrated in a reproducible package with code, examples, and demo data to support community-driven validation and extension toward industrial requirements. The software is archived on Zenodo and the GitHub repository serves as the collaboration hub. Full article

The integration of Unmanned Aerial Vehicles (UAVs) with the Internet of Things (IoT) is revolutionizing a wide range of applications. However, collecting massive sensing data from large-scale fields efficiently remains challenging, constrained by the limited energy of UAVs and sensing nodes. Existing schemes lack the computational intelligence of an Edge Server (ES) for deep coordination. To address this, we propose DC-CSAP, a novel “Edge-UAV-End” collaborative data collection framework. DC-CSAP introduces a systematic workflow orchestrated by the ES, which is operationalized through four dedicated collaboration mechanisms: (1) In our ES–UAV collaboration, we devise a two-phase path optimization algorithm that hybridizes Simulated Annealing (SA) with a convex-hull-inspired greedy method. (2) The ES–ISN collaboration features a prediction-based binary vector mechanism, transmitting only inaccurate data to slash communication overheads. (3) The UAV–ISN and (4) Inter-ISN protocols ensure efficient data exchange and aggregation. Extensive simulations validate that DC-CSAP outperforms benchmarks in terms of Correct Prediction Rate (CPR), energy efficiency, and UAV path length. Full article

This article presents a gamified digital twin in Minecraft designed to support practical exercises in digital logic in the Computer Engineering I course at the University of Salamanca. Implemented as a Spigot/Paper server plugin based on the Platform for Automatic coNstruction of orGanizations of intElligent Agents (PANGEA) multi-agent architecture, the system orchestrates four virtual organizations and employs a world cloning strategy (via Multiverse and WorldGuard) to ensure individual and isolated workspaces, while also enabling collaborative work. The central contribution is a multi-agent system with an integrated ‘black box’ verification engine that mitigates redstone asynchrony and latency through controlled signal injection and software clock synchronization, enabling real-time deterministic validation of both basic logic gates and more complex sequential circuits. Additionally, the ecosystem includes a specialized suite of logic scenarios and a web-based dashboard for real-time teacher monitoring. In a pilot study ($N=30$), the system achieved an average task completion rate of 89.1%, and an adapted Unified Theory of Acceptance and Use of Technology (UTAUT) analysis indicated that technical stability is positively associated with student performance. Full article

We present a combined ML/NLP (Machine Learning, Natural Language Processing) pipeline for protecting cloud-based APIs (Application Programming Interfaces), which works both at the level of individual HTTP (Hypertext Transfer Protocol) requests and at the access log file reading mode, linking explicitly technical anomalies with business risks. The system processes each event/access log through parallel numerical and textual branches: a set of anomaly detectors trained on traffic engineering characteristics and a hybrid NLP stack that combines rules, TF-IDF (Term Frequency-Inverse Document Frequency), and character-level models trained on enriched security datasets. Their results are integrated using a risk-aware policy that takes into account endpoint type, data sensitivity, exposure, and authentication status, and creates a discrete risk level with human-readable explanations and recommended SOC (Security Operations Center) actions. We implement this design as a containerized microservice pipeline (input, preprocessing, ML, NLP, merging, alerting, and retraining services), orchestrated using Docker Compose and instrumented using OpenSearch Dashboards. Experiments with OWASP-like (Open Worldwide Application Security Project) attack scenarios show a high detection rate for injections, SSRF (Server-Side Request Forgery), Data Exposure, and Business Logic Abuse, while the processing time for each request remains within real-time limits even in sequential testing mode. Thus, the pipeline bridges the gap between ML/NLP research for security and practical API protection channels that can evolve over time through feedback and retraining. Full article

Microservice deployment and request routing can help improve server efficiency and the performance of large-scale mobile edge computing (MEC). However, the joint optimization of microservice deployment and request routing is extremely challenging, as dynamic request routing easily results in asymmetric network structures and imbalanced microservice workloads. This article proposes multi-objective joint optimization for microservice deployment and request routing based on structural symmetry. Firstly, the structural symmetry of microservice deployment and request routing is defined, including spatial symmetry and temporal symmetry. A constrained nonlinear multi-objective optimization model was constructed to jointly optimize microservice deployment and request routing, where the structural symmetric metrics take into account the flow-aware routing distance, workload balancing, and request response delay. Secondly, an improved artificial plant community algorithm is designed to search for the optimal route to achieve structural symmetry, including the environment preparation and dependency installation, service packaging and image orchestration, arrangement configuration and dependency management, deployment execution and status monitoring. Thirdly, a benchmark experiment is designed to compare with baseline algorithms. Experimental results show that the proposed algorithm can effectively optimize structural symmetry and reduce the flow-aware routing distance, workload imbalance, and request response delay, while the computational overhead is small enough to be easily deployed on resource-constrained edge computing devices. Full article

This study presents a production-ready AI pipeline for audio content processing, implemented within the Youth Radio platform, which serves as an extension of the European School Radio initiative. The system uses a multi-server architecture: an AI Server that runs batch/offline jobs, orchestrated by Apache Airflow, and two Web Servers that deliver all the Backend as well as the Frontend applications, configured with load balancing and redundancy to ensure high availability and fault tolerance. The implemented AI Pipeline includes tasks such as preprocessing, transcription, audio classification and topic modeling. Processed Podcasts are indexed in a Qdrant vector database to facilitate both dense and sparse retrieval while a recommendation system enriches the user’s experience. We summarize design choices and report system-level metrics and task-level indicators (ASR quality after correction, retrieval effectiveness) to guide similar deployments. Full article

Inference of large machine learning models can quickly exceed the capabilities of edge devices in terms of performance, memory or energy consumption. When offloading computations to a cloud server is not possible or feasible, for instance, due to data sovereignty concerns or latency constraints, a solution can be to distribute the inference load across multiple devices in a local edge network. We propose an approach which is capable of orchestrating multi-stage inference tasks in a mobile ad-hoc network consisting of heterogeneous devices in a self-organized and fully distributed manner. As individual edge devices may be battery-powered and volatile, the framework ensures a high degree of reliability even in dynamic environments. In particular, new nodes are automatically and seamlessly integrated into the ensemble, rendering the approach highly scalable. Moreover, resilience against spontaneous node dropouts or connection failures is implemented through adaptive task rerouting. Finally, by enabling complex inference tasks to be processed in small segments on the most suitable hardware available in the network, the ensemble is able to attain considerable pipelining performance and energy efficiency. Full article

Edge computing is characterized by heterogeneous hardware, distributed deployment, and a need for on-site processing, which makes performance benchmarking challenging. This paper presents SHEAB (Scalable Heterogeneous Edge Automation Benchmarking), a novel framework designed to securely automate the benchmarking of Edge AI devices at scale. The proposed framework enables concurrent performance evaluation of multiple edge nodes, drastically reducing the time-to-deploy (TTD) for benchmarking tasks compared to traditional sequential methods. SHEAB’s architecture leverages containerized microservices for orchestration and result aggregation, integrated with multi-layer security (firewalls, VPN tunneling, and SSH) to ensure safe operation in untrusted network environments. We provide a detailed system design and workflow, including algorithmic pseudocode for the SHEAB process. A comprehensive comparative review of related work highlights how SHEAB advances the state-of-the-art in edge benchmarking through its combination of secure automation and scalability. We detail a real-world implementation on eleven heterogeneous edge devices, using a centralized 48-core server to coordinate benchmarks. Statistical analysis of the experimental results demonstrates a 43.74% reduction in total benchmarking time and a 1.78× speedup in benchmarking throughput using SHEAB, relative to conventional one-by-one benchmarking. We also present mathematical formulations for performance gain and discuss the implications of our results. The framework’s effectiveness is validated through the concurrent execution of standard benchmarking workloads on distributed edge nodes, with results stored in a central database for analysis. SHEAB thus represents a significant step toward efficient and reproducible Edge AI performance evaluation. Future work will extend the framework to broader workloads and further improve parallel efficiency. Full article

This paper proposes a scalable serverless architecture for real-time natural language processing (NLP) on large datasets using Amazon Web Services (AWS). The framework integrates AWS Lambda, Step Functions, and S3 to enable fully parallel sentiment analysis with Transformer-based models such as DistilBERT, RoBERTa, and ClinicalBERT. By containerizing inference workloads and orchestrating parallel execution, the system eliminates the need for dedicated servers while dynamically scaling to workload demand. Experimental evaluation on the IMDb Reviews dataset demonstrates substantial efficiency gains: parallel execution achieved a 6.07× reduction in wall-clock duration, an 81.2% reduction in total computing time and energy consumption, and a 79.1% reduction in variable costs compared to sequential processing. These improvements directly translate into a smaller carbon footprint, highlighting the sustainability benefits of serverless architectures for AI workloads. The findings show that the proposed framework is model-independent and provides consistent advantages across diverse Transformer variants. This work illustrates how cloud-native, event-driven infrastructures can democratize access to large-scale NLP by reducing cost, processing time, and environmental impact while offering a reproducible pathway for real-world research and industrial applications. Full article

The proliferation of Internet of Things (IoT) devices has created a distributed ecosystem where users generate vast amounts of review data across heterogeneous platforms, from smart home assistants to connected vehicles. This data is crucial for service improvement but is plagued by fake reviews, data quality inconsistencies, and significant privacy risks. Traditional centralized analytics fail in this landscape due to data privacy regulations and the sheer scale of distributed data. To address this, we propose FedDQ, a federated learning framework for Privacy-Preserving Federated Review Analytics with Data Quality Optimization. FedDQ introduces a multi-faceted data quality assessment module that operates locally on each IoT device, evaluating review data based on textual coherence, behavioral patterns, and cross-modal consistency without exposing raw data. These quality scores are then used to orchestrate a quality-aware aggregation mechanism at the server, prioritizing contributions from high-quality, reliable clients. Furthermore, our framework incorporates differential privacy and models system heterogeneity to ensure robustness and practical applicability in resource-constrained IoT environments. Extensive experiments on multiple real-world datasets show that FedDQ significantly outperforms baseline federated learning methods in accuracy, convergence speed, and resilience to data poisoning attacks, achieving up to a 13.8% improvement in F1-score under highly heterogeneous and noisy conditions while preserving user privacy. Full article

The successful integration of Terrestrial and Non-Terrestrial Networks (T/NTNs) in 6G is poised to revolutionize demanding domains like Earth Observation (EO) and Intelligent Transportation Systems (ITSs). Still, it requires Distributed Machine Learning (DML) frameworks that are scalable, private, and efficient. Existing methods, such as Federated Learning (FL) and Split Learning (SL), face critical limitations in terms of client computation burden and latency. To address these challenges, this paper proposes a novel hierarchical DML paradigm. We first introduce Federated Split Transfer Learning (FSTL), a foundational framework that synergizes FL, SL, and Transfer Learning (TL) to enable efficient, privacy-preserving learning within a single client group. We then extend this concept to the Generalized FSTL (GFSTL) framework, a scalable, multi-group architecture designed for complex and large-scale networks. GFSTL orchestrates parallel training across multiple client groups managed by intermediate servers (RSUs/HAPs) and aggregates them at a higher-level central server, significantly enhancing performance. We apply this framework to a unified T/NTN architecture that seamlessly integrates vehicular, aerial, and satellite assets, enabling advanced applications in 6G ITS and EO. Comprehensive simulations using the YOLOv5 model on the Cityscapes dataset validate our approach. The results show that GFSTL not only achieves faster convergence and higher detection accuracy but also substantially reduces communication overhead compared to baseline FL, and critically, both detection accuracy and end-to-end latency remain essentially invariant as the number of participating users grows, making GFSTL especially well suited for large-scale heterogeneous 6G ITS deployments. We also provide a formal latency decomposition and analysis that explains this scaling behavior. This work establishes GFSTL as a robust and practical solution for enabling the intelligent, connected, and resilient ecosystems required for next-generation transportation and environmental monitoring. Full article