Search Results (3,946)

Watermarking protocols represent a possible solution to the problem of digital copyright protection of content distributed on the Internet. Their implementations, however, continue to be a complex problem due to the difficulties researchers encounter in proposing secure, easy-to-use and, at the same time, “trusted third parties” (TTPs)-free solutions. In this regard, implementations based on blockchain and smart contracts are among the most advanced and promising, even if they are affected by problems regarding the performance and privacy of the information exchanged and processed by smart contracts and managed by blockchains. This paper presents a watermarking protocol implemented by smart contracts and blockchain. The protocol uses a “layer-2” blockchain execution model and performs the computation in “trusted execution environments” (TEEs). Therefore, its implementation can guarantee efficient and confidential execution without compromising ease of use or resorting to TTPs. The protocol and its implementation can, thus, be considered a valid answer to the “trilemma” that afflicts the use of blockchains, managing to guarantee decentralization, security, and scalability. Full article

This paper addresses the problem of optimizing obesity, which has been a challenging issue in the last decade based on recent data revealed in 2024 by the World Health Organization (WHO). The current work introduces a new mathematical model of the dynamics of weight over time with embedded control parameters to optimize the number of obese, overweight, and comorbidity populations. The mathematical formulation of the model is developed under certain sufficient conditions that guarantee the positivity and boundedness of solutions over time. The model structure exhibits inherent symmetry in population group transitions, particularly around the equilibrium state, which allows the application of analytical tools such as the Routh–Hurwitz and Metzler criteria. Then, the analysis of local and global stability of the obesity-free equilibrium state is discussed based on these criteria. Based on the Pontryagin maximum principle (PMP), the deviation from the obesity-free equilibrium state is controlled. The model’s effectiveness is demonstrated through simulation using the Forward–Backward Sweeping algorithm with parameters derived from recent research in human health. Incorporating symmetry considerations in the model enhances the understanding of system behavior and supports balanced intervention strategies. Results suggest that the model can effectively inform strategies to mitigate obesity prevalence and associated health risks. Full article

To enable safe and effective human–robot collaboration (HRC) in smart manufacturing, it is critical to seamlessly integrate sensing, cognition, and prediction into the robot controller for real-time awareness, response, and communication inside a heterogeneous environment (robots, humans, and equipment). The proposed approach takes advantage of the prediction capabilities of nonlinear model predictive control (NMPC) to execute safe path planning based on feedback from a vision system. To satisfy the requirements of real-time path planning, an embedded solver based on a penalty method is applied. However, due to tight sampling times, NMPC solutions are approximate; therefore, the safety of the system cannot be guaranteed. To address this, we formulate a novel safety-critical paradigm that uses an exponential control barrier function (ECBF) as a safety filter. Several common human–robot assembly subtasks have been integrated into a real-life HRC assembly task to validate the performance of the proposed controller and to investigate whether integrating human pose prediction can help with safe and efficient collaboration. The robot uses OptiTrack cameras for perception and dynamically generates collision-free trajectories to the predicted target interactive position. Results for a number of different configurations confirm the efficiency of the proposed motion planning and execution framework, with a 23.2% reduction in execution time achieved for the HRC task compared to an implementation without human motion prediction. Full article

With the rapid proliferation of Internet of Things (IoT) devices and mobile applications and the growing demand for low-latency services, edge computing has emerged as a transformative paradigm that brings computation and storage closer to end users. However, the dynamic nature and limited resources of edge networks bring challenges such as load imbalance and high latency while satisfying user requests. Service migration, the dynamic redeployment of service instances across distributed edge nodes, has become a key enabler for solving these challenges and optimizing edge network characteristics. Moreover, the low-latency nature of edge computing requires that service migration strategies must be in real time in order to ensure latency requirements. Thus, this paper presents a systematic survey of real-time service migration in edge networks. Specifically, we first introduce four network architectures and four basic models for real-time service migration. We then summarize four research motivations for real-time service migration and the real-time guarantee introduced during the implementation of migration strategies. To support these motivations, we present key techniques for solving the task of real-time service migration and how these algorithms and models facilitate the real-time performance of migration. We also explore latency-sensitive application scenarios, such as smart cities, smart homes, and smart manufacturing, where real-time service migration plays a critical role in sustaining performance and adaptability under dynamic conditions. Finally, we summarize the key challenges and outline promising future research directions for real-time service migration. This survey aims to provide a structured and in-depth theoretical foundation to guide future research on real-time service migration in edge networks. Full article

In recent years, DC grids have gained traction, and several proposals regarding measuring strategies and several Power Quality (PQ) parameters have been defined to be used in such networks that differ from traditional AC power grids. As a complement to all this preliminary work, this study on the effect of modifying the sampling frequency on some of those parameters has been conducted. For time series evaluation of mean and RMS voltage values, the Dynamic Time Warping (DTW) algorithm has been used. Additionally, the consequence of varying the sampling rate in voltage event detection has also been analysed. As a result, relevant advice regarding sampling frequency is presented in this paper for an effective and optimum evaluation of RMS or mean voltage values and its implementation in detecting voltage events (dips or swells). At least for the parameters in the monitored DC microgrid, a clue for the minimum sampling rate that guarantees accurate measurements is found. Full article

Technological roadmaps are essential tools for managing and planning complex projects, especially in the rapidly evolving field of space exploration. Defined as dynamic schedules, they support strategic and long-term planning while coordinating current and future objectives with particular technology solutions. Currently, the available methodologies are mostly built on experts’ opinions and in just few cases, methodologies and tools have been developed to support the decision makers with a rational approach. In any case, all the available approaches are meant to draw “ideal” maturation plans. Therefore, it is deemed essential to develop an integrate new algorithms able to decision guidelines on “non-nominal” scenarios. In this context, Politecnico di Torino, in collaboration with the European Space Agency (ESA) and Thales Alenia Space–Italia, developed the Technology Roadmapping Strategy (TRIS), a multi-step process designed to create robust and data-driven roadmaps. However, one of the main concerns with its initial implementation was that TRIS did not account for time and budget estimates specific to the space exploration environment, nor was it capable of generating alternative development paths under constrained conditions. This paper discloses two main significant updates to TRIS methodology: (1) improved time and budget estimation to better reflect the specific challenges of space exploration scenarios and (2) the capability of generating alternative roadmaps, i.e., alternative technological maturation paths in resource-constrained scenarios, balancing financial and temporal limitations. The application of the developed routines to available case studies confirms the tool’s ability to provide consistent planning outputs across multiple scenarios without exceeding 20% deviation from expert-based judgements available as reference. The results demonstrate the potential of the enhanced methodology in supporting strategic decision making in early-phase mission planning, ensuring adaptability to changing conditions, optimized use of time and financial resources, as well as guaranteeing an improved flexibility of the tool. By integrating data-driven prioritization, uncertainty modeling, and resource-constrained planning, TRIS equips mission planners with reliable tools to navigate the complexities of space exploration projects. This methodology ensures that roadmaps remain adaptable to changing conditions and optimized for real-world challenges, supporting the sustainable advancement of space exploration initiatives. Full article

Frequency-hopping multiple access is widely adopted to blunt narrow-band jamming and limit spectral disclosure in cyber–physical systems, yet its practical resilience depends on three sequence-level properties. First, balancedness guarantees that every carrier is occupied equally often, removing spectral peaks that a jammer or energy detector could exploit. Second, a wide gap between successive hops forces any interferer to re-tune after corrupting at most one symbol, thereby containing error bursts. Third, a no-hit zone (NHZ) window with a zero pairwise Hamming correlation eliminates user collisions and self-interference when chip-level timing offsets fall inside the window. This work introduces an algebraic construction that meets the full set of requirements in a single framework. By threading a permutation over an integer ring and partitioning the period into congruent sub-blocks tied to the desired NHZ width, we generate balanced wide gap no-hit zone frequency-hopping (WG-NHZ FH) sequence sets. Analytical proofs show that (i) each sequence achieves the Lempel–Greenberger bound for auto-correlation, (ii) the family and zone sizes satisfy the Ye–Fan bound with equality, (iii) the hop-to-hop distance satisfies a provable WG condition, and (iv) balancedness holds exactly for every carrier frequency. Full article

This work addresses distributed fixed-time bearing-only formation stabilization for multi-agent systems lacking shared orientation knowledge. Addressing the challenge of missing global coordinate alignment in multi-agent systems, this work introduces a novel distributed estimator ensuring almost globally fixed-time convergence of orientation estimates. Leveraging this estimator, we develop a distributed bearing-only formation control law specifically designed for agents governed by double-integrator dynamics, guaranteeing fixed-time convergence. Comprehensive stability analysis proves the almost global fixed-time stability of the overall closed-loop system. Crucially, the proposed control strategy drives actual formation to achieve the desired geometric pattern with almost global exponential convergence within a fixed time bound. Rigorous numerical experiments corroborate the theoretical framework. Full article

Secure autonomous secret key distillation (SKD) systems traditionally depend on external common randomness (CR) sources, which often suffer from instability and limited reliability over long-term operation. In this work, we propose a novel SKD architecture that synthesizes CR by combining a keystream of a shared-key keystream generator $KSG\left(K_G\right)$ with locally generated binary Bernoulli noise. This construction emulates the statistical properties of the classical Maurer satellite scenario while enabling deterministic control over key parameters such as bit error rate, entropy, and leakage rate (LR). We derive a closed-form lower bound on the equivocation of the shared-secret key $K_G$ from the viewpoint of an adversary with access to public reconciliation data. This allows us to define an admissible operational region in which the system guarantees long-term secrecy through periodic key refreshes, without relying on advantage distillation. We integrate the Winnow protocol as the information reconciliation mechanism, optimized for short block lengths ($N=8$), and analyze its performance in terms of efficiency, LR, and final key disagreement rate (KDR). The proposed system operates in two modes: ideal secrecy, achieving secret key rates up to 22% under stringent constraints (KDR < 10⁻⁵, LR < 10⁻¹⁰), and perfect secrecy mode, which approximately halves the key rate. Notably, these security guarantees are achieved autonomously, without reliance on advantage distillation or external CR sources. Theoretical findings are further supported by experimental verification demonstrating the practical viability of the proposed system under realistic conditions. This study introduces, for the first time, an autonomous CR-based SKD system with provable security performance independent of communication channels or external randomness, thus enhancing the practical viability of secure key distribution schemes. Full article

The rapid growth of Internet of Things (IoT) deployment has led to an unprecedented volume of interconnected, resource-constrained devices. Securing their communication is essential, especially in vehicular environments, where sensitive data exchange requires robust authentication, integrity, and confidentiality guarantees. In this paper, we present an empirical evaluation of TLS (Transport Layer Security)-enhanced MQTT (Message Queuing Telemetry Transport) on low-cost, quad-core Cortex-A72 ARMv8 boards, specifically the Raspberry Pi 4B, commonly used as prototyping platforms for On-Board Units (OBUs) and Road-Side Units (RSUs). Three MQTT entities, namely, the broker, the publisher, and the subscriber, are deployed, utilizing Elliptic Curve Cryptography (ECC) for key exchange and authentication and employing the AES_256_GCM and ChaCha20_Poly1305 ciphers for confidentiality via appropriately selected libraries. We quantify resource consumption in terms of CPU utilization, execution time, energy usage, memory footprint, and goodput across TLS phases, cipher suites, message packaging strategies, and both Ethernet and WiFi interfaces. Our results show that (i) TLS 1.3-enhanced MQTT is feasible on Raspberry Pi 4B devices, though it introduces non-negligible resource overheads; (ii) batching messages into fewer, larger packets reduces transmission cost and latency; and (iii) ChaCha20_Poly1305 outperforms AES_256_GCM, particularly in wireless scenarios, making it the preferred choice for resource- and latency-sensitive V2X applications. These findings provide actionable recommendations for deploying secure MQTT communication on an IoT platform. Full article

With the large-scale integration of renewable energy into distribution networks, traditional fixed-setting overcurrent protection strategies struggle to adapt to rapid fluctuations in renewable energy (e.g., wind and photovoltaic) output. Optimizing current settings is crucial for enhancing the stability of modern distribution networks. This paper proposes an adaptive overcurrent protection method based on an improved NSGA-II algorithm. By dynamically detecting renewable power fluctuations and generating adaptive solutions, the method enables the online optimization of protection parameters, effectively reducing misoperation rates, shortening operation times, and significantly improving the reliability and resilience of distribution networks. Using the rate of renewable power variation as the core criterion, renewable power changes are categorized into abrupt and gradual scenarios. Depending on the scenario, either a random solution injection strategy (DNSGA-II-A) or a Gaussian mutation strategy (DNSGA-II-B) is dynamically applied to adjust overcurrent protection settings and time delays, ensuring real-time alignment with grid conditions. Hard constraints such as sensitivity, selectivity, and misoperation rate are embedded to guarantee compliance with relay protection standards. Additionally, the convergence of the Pareto front change rate serves as the termination condition, reducing computational redundancy and avoiding local optima. Simulation tests on a 10 kV distribution network integrated with a wind farm validate the effectiveness of the proposed method. Full article

Detection of surface defects can significantly elongate mechanical service time and mitigate potential risks during safety management. Traditional defect detection methods predominantly rely on manual inspection, which suffers from low efficiency and high costs. Some machine learning algorithms and artificial intelligence models for defect detection, such as Convolutional Neural Networks (CNNs), present outstanding performance, but they are often data-dependent and cannot provide guarantees for new test samples. To this end, we construct a detection model by combining Mask R-CNN, selected for its strong baseline performance in pixel-level segmentation, with Conformal Risk Control. The former evaluates the distribution that discriminates defects from all samples based on probability. The detection model is improved by retraining with calibration data that is assumed to be independent and identically distributed (i.i.d) with the test data. The latter constructs a prediction set on which a given guarantee for detection will be obtained. First, we define a loss function for each calibration sample to quantify detection error rates. Subsequently, we derive a statistically rigorous threshold by optimization of error rates and a given guarantee significance as the risk level. With the threshold, defective pixels with high probability in test images are extracted to construct prediction sets. This methodology ensures that the expected error rate on the test set remains strictly bounded by the predefined risk level. Furthermore, our model shows robust and efficient control over the expected test set error rate when calibration-to-test partitioning ratios vary. Full article

Problem: Existing Human–Robot Collaboration (HRC) allocators cannot react at a sub-second scale while accounting for worker fatigue. Objective: We designed a fatigue-aware combinatorial auction executed every 100 ms. Method: A human and a FANUC robot submit bids combining execution time, predicted energy, and real-time fatigue; a greedy algorithm (≤1 ms) with a $1-1/e$ approximation guarantee and O (|Bids| log |Bids|) complexity maximizes utility. Results: In 1000 RoboDK episodes, the framework increases active cycles·min⁻¹ by 20%, improves robot utilization by +10.2 percentage points, reduces per cycle fatigue by 4%, and raises the collision-free rate to 99.85% versus a static baseline (p < 0.001). Contribution: We provide the first transparent, sub-second, fatigue-aware allocation mechanism for Industry 5.0, with quantified privacy safeguards and a roadmap for physical deployment. Unlike prior auction-based or reinforcement learning approaches, our model uniquely integrates a sub-second ergonomic adaptation with a mathematically interpretable utility structure, ensuring both human-centered responsiveness and system-level transparency. Full article

With the development of underground space in urban areas, the demand for tunneling through existing railways is increasing. The adverse effects of cut-blasting during the construction of tunnels under crossing existing railways are investigated. Combined with the principle of blasting seismic wave superposition, LS-DYNA numerical simulation is used to analyze the seismic wave superposition law under different superposition methods. This study also investigates the vibration reduction effect of millisecond blasting for cut-blasting under the different classes of surrounding rocks. The results show that the vibration reduction forms of millisecond blasting can be divided into separation and interference of waveform. Based on the principle of superposition of blasting seismic waves, vibration reduction through wave interference is further divided. At the same time, a new vibration reduction mode is proposed. This vibration reduction mode can significantly improve construction efficiency while improving damping efficiency. The new vibration reduction mode can increase the vibration reduction to 80% while improving construction efficiency. Additionally, there is a significant difference in the damping effect of different classes of surrounding rock on the blasting seismic wave. Poor-quality surrounding rock enhances the attenuation of seismic wave velocity and peak stress in the surrounding rock. In the Zhongliangshan Tunnel, a tunnel cut-blasting construction at a depth of 42 m, the best vibration reduction plan of Class III is 3 ms millisecond blasting, in which the surface points achieve separation vibration reduction. The best vibration reduction plan of Class V is 1 ms millisecond blasting, in which the surface points achieve a new vibration reduction mode. During the tunnel blasting construction process, electronic detonators are used for millisecond blasting of the cut-blasting. This method can reduce the vibration effects generated by blasting. The stability of the existing railway is ultimately guaranteed. This can improve construction efficiency while ensuring construction safety. This study can provide significant guidance for the blasting construction of the tunnel through the railway. Full article

Blockchain’s immutability, while enhancing transparency and trust, presents challenges when erroneous or sensitive content must be modified. To address this, we propose a novel cryptographic primitive called Time-Modifiable Chameleon Hash (TMCH), enabling controlled and time-modifiable data redaction in blockchain systems. TMCH enhances traditional chameleon hashes by embedding a time parameter into hash generation and verification, and it supports class-adaptive collision computation via a new Update algorithm. We formally define the security model for TMCH and analyze the relationships among uniqueness, indistinguishability (IND), and collision resistance (CR). This paper presents a concrete construction based on the Boneh–Boyen signature scheme and completes its security analysis and proof. Finally, the efficiency and practicality of the proposed scheme are validated through experimental comparisons with existing algorithms. Our results show that relaxing the uniqueness requirement does not compromise overall security, while enabling traceable and time-aware redactions. The proposed TMCH achieves strong security guarantees and practical efficiency, making it well suited for applications such as compliant data redaction and time-sensitive blockchain transactions. Full article