We propose a novel blockchain-based traceability system that uniquely combines Physical Unclonable Functions (PUFs) and Non-Fungible Tokens (NFTs) to establish secure, tamper-evident, and verifiable digital identities for physical products. Unlike conventional approaches that rely solely on serial numbers or barcodes, our system uses embedded PUFs to generate a physically unclonable ID for each item, ensuring hardware-level authenticity. This PUF ID is cryptographically linked to an NFT minted on a public blockchain, encapsulating metadata such as product origin, manufacturing details, and certification status. Associating NFTs and certifications with PUF-tagged products not only provides transparent provenance but also enables decentralised validation of compliance and ethical standards. This methodology is distinct in its integration of immutable physical identity with blockchain-based digital certification, offering a robust solution for enhancing transparency, combating counterfeiting, and fostering trust across global supply chains. Furthermore, employing Edge and emerging 6G networks, our framework can execute PUF challenge-response and preliminary NFT minting directly at edge nodes. This establishes a real-time, low-latency architecture that reduces network congestion and gas costs for secure and sustainable supply chains.

This paper proposes SHIODEG, a hybrid metaheuristic that integrates the success-history intelligent optimizer (SHIO) with differential evolution (DE) and a Gaussian transformation (GT) to tackle two persistent challenges in optimization for engineering design: (i) the absence of a universally best optimizer across problem classes (as implied by the No-Free-Lunch perspective) and (ii) the limited ability of purely gradient-based methods to produce substantial improvements in complex, constrained, and often non-smooth real-world problems, motivating hybrid strategies that balance exploration and exploitation. SHIODEG follows a staged search process in which DE generates diverse trial solutions, GT injects normally distributed perturbations to reduce premature convergence and diversity collapse, and SHIO refines promising regions using success-history guidance from the best three leaders. SHIODEG is evaluated on the IEEE CEC2022 benchmark suite (12 functions) using 30 independent runs, a population size of 100, and a budget of 1000D function evaluations. The results show that SHIODEG consistently delivers top-tier performance across the benchmark suite, showing strong competitiveness, low variability, and statistically significant improvements over a wide range of alternative optimizers. It also demonstrates robust effectiveness on multiple constrained engineering design problems, achieving high-quality solutions across diverse real-world constraints.

A number of challenging aspects have to be considered, when the Internet of Things (IoT) and Artificial Intelligence (AI) are combined into intelligent IoT systems. A key aspect that demands high attention is trustworthiness. As part of the investigations we conduct in this area in collaboration with partner companies, the need of a holistic view for trustworthiness in Intelligent IoT systems has emerged. To address such need, and to identify suitable support for it, we analyzed existing ISO standards and literature and we found out that they lack a holistic view for trustworthiness in intelligent IoT systems.To bridge this gap, we propose a conceptual model for trustworthiness in intelligent IoT systems that includes stakeholders, systems, and primary concerns, and is built upon existing standards and literature. Our model can support the design, development, operations, evolution of and communication about intelligent IoT systems. We received positive confirmation of the validity of the conceptual model from industrial practitioners working in four companies in the intelligent IoT systems area. Together with our partner companies, we plan to develop and operate approaches leveraging the conceptual model as next step.

The growing adoption of wearable fitness devices and health applications has led to an exponential increase in fitness recommendations. However, privacy concerns remain significant barriers to user trust and regulatory compliance. Federated Learning (FL) offers a privacy-preserving paradigm by training models across decentralized devices without exposing raw data. However, FL introduces new challenges, including data heterogeneity, computational overhead, and the need for explainable AI (XAI). This work presents XFL, an integrated, explainable FL approach for personalized fitness recommendation systems. Our approach integrates FL with XAI techniques, SHAP, and LIME, to enhance transparency and interpretability while preserving privacy. By leveraging global and client-specific explanations, our framework empowers users to understand the rationale behind personalized recommendations, fostering trust and usability. Experimental results demonstrate that XFL performs better than centralized models while maintaining strong privacy guarantees. Furthermore, we evaluated the computational impact of integrating XAI in FL environments, providing insights into the efficiency of different explainability techniques. Our findings contribute to developing user-centric, privacy-aware, and interpretable AI-driven fitness solutions.

The Internet of Things (IoT) and Artificial Intelligence (AI) has enabled the development of innovative applications. The deployment of those applications is a complex process that should take into consideration multiple factors, including the applications' scale, complexity, distribution, and non-functional requirements (e.g., energy consumption, performance, and security). Moreover, deployment environments over the edge-cloud continuum are heterogeneous w.r.t. their processing capabilities, communication latencies, and energy consumption. Towards enabling efficient scheduling of tasks in such environments, we formulate the task scheduling problem as a multi-objective optimization task balancing energy efficiency and deadline adherence. To tackle this problem, we employ the Equilibrium Optimizer (EO)-a physics-inspired meta-heuristic algorithm that utilizes an equilibrium pool of top-performing solutions to guide its population toward high-quality schedules. To validate the feasibility of our approach, we run experiments where we compare our proposed approach against the multiple existing optimizers. The results demonstrate that EO exhibits a superior performance reflecting its potential to improve IoT systems' quality of service and reduce their operational costs in large-scale and time-sensitive IoT scenarios.

Vehicular edge computing for 6G must meet millisecond scale latency and stringent energy budgets over dynamic, multi tier networks. We introduce an ML driven orchestrator that uses a Naive Bayes classifier for edge and cloud tier selection with a regression model for service time prediction on multidimensional features (task attributes, network metrics, energy profiles, CPU load), and embeds a multipath feasibility module, augmented by transmission power control and dynamic CPU frequency scaling, to jointly optimize latency, reliability, and energy consumption. In comprehensive EdgeCloudSim SUMO experiments, our framework achieves a latency of up to 35% lower end- to-end, 30% fewer task failures, and keeps energy use within 10% of optimal compared to a randomized baseline. These results demonstrate millisecond scale decision capability and robust performance under realistic VEC conditions

Automating test case specification generation is vital for improving the efficiency and accuracy of software testing, particularly in complex systems like high-performance Electronic Control Units (ECUs). This study investigates the use of Natural Language Processing (NLP) techniques, including Rule-Based Information Extraction and Named Entity Recognition (NER), to transform natural language requirements into structured test case specifications. A dataset of 400 feature element documents from the Polarion tool was used to evaluate both approaches for extracting key elements such as signal names and values. The results reveal that the Rule-Based method outperforms the NER method, achieving 95% accuracy for more straightforward requirements with single signals, while the NER method, leveraging SVM and other machine learning algorithms, achieved 77.3% accuracy but struggled with complex scenarios. Statistical analysis confirmed that the Rule-Based approach significantly enhances efficiency and accuracy compared to manual methods. This research highlights the potential of NLP-driven automation in improving quality assurance, reducing manual effort, and expediting test case generation, with future work focused on refining NER and hybrid models to handle greater complexity.

Global supply chains face pervasive challenges-low traceability issues and counterfeit products cost industries billions each year. Opaque and siloed records make it hard to verify provenance or detect tampering. Numerous studies have proposed the use of RFID, NFC, and barcode technologies for product traceability in supply chains. However, these tagging methods present significant security limitations. RFID and NFC tags are susceptible to cloning and spoofing, while barcodes and optical markers can be easily duplicated or physically tampered with. Such vulnerabilities undermine the reliability of these systems for ensuring data integrity and product authenticity in high-security supply chain environments. To address the aforementioned challenges, we propose a lightweight framework for product authentication and traceability that leverages an Arbiter Physical Unclonable Function (PUF) simulated in Python with Elliptic Curve Cryptography (ECC) key pairs derived from Elliptic Curve Digital Signature Algorithm (ECDSA), integrated into a permissionless Ethereum blockchain using a smart contract. This two-step approach ensures genuine products and maintains an immutable and easily retrievable audit trail of their journey through the supply chain. The experiments included smart contract unit tests, gas profiling, and transaction latency measurements using a local blockchain environment. In addition, we conducted a controlled experiment to evaluate the performance impact of storing product and authentication data either fully on-chain or partially off-chain using IPFS-based content identifiers (CIDs). The results validate the feasibility of our architecture as a practical, cost-efficient, and scalable solution for real-world PUF-enabled supply chain systems.

The Internet of Things (IoT) has transformed our daily life by enabling devices and objects to collect data, communicate, and collaborate to provision novel types of services. Engineering IoT systems is a complex process that should consider a number of quality characteristics to meet the systems’ goals. Towards identifying the key quality characteristics of IoT systems, in this study, we conduct semi-structured interviews with seven companies developing IoT solutions within smart energy, smart healthcare, smart surveillance, and smart buildings application areas. The study used the ISO/IEC 25010 model as a reference and a qualitative research approach, i.e., we conducted semi-structured interviews with ten experts and performed content analysis on the data collected from the interviews. The study findings reveal that the ISO/IEC 25010 model does not include the following key quality characteristics that practitioners consider when engineering IoT systems: trust, privacy, and energy consumption. Additionally, we report about trade-offs between quality characteristics, architectural constraints, and challenges related to the achievement of the identified quality characteristics when engineering IoT systems in practice.

Federated learning (FL) is a distributed machine learning paradigm that enables multiple participants to collaboratively train a model without sharing raw data to preserve privacy. However, traditional FL frameworks remain vulnerable to integrity and accountability issues at both the global and local levels. Blockchain (BC), known for its decentralization, transparency, immutability, and cryptographic security, has been explored to enhance trust in FL. Yet, prior BC-integrated FL approaches often suffer from limitations such as resource-heavy consensus algorithms, on-chain storage of model weights, and unenforced incentive schemes in semi-decentralized settings. In this work, we propose a lightweight decentralized FL framework for supply chains. Our design features role-based governance, quorum-based model approval, stake-and-slash incentives, and off-chain model storage via the InterPlanetary File System (IPFS) to minimize gas costs. We validate the framework through simulation and perform BC performance evaluation, demonstrating its efficiency in terms of gas usage and latency across 100 FL rounds.