Search Results (686)

This study explores the application of federated learning (FL) in security camera surveillance systems to overcome the structural limitations inherent in traditional centralized artificial intelligence (AI) training approaches, while simultaneously enhancing operational efficiency and data security. Conventional centralized AI models require the transmission of raw surveillance data from individual security camera units to a central server for model training, which poses significant challenges, including network congestion, a heightened risk of personal data leakage, and inadequate adaptation to localized environmental characteristics. These limitations are particularly critical in high-security environments such as military bases and government facilities, where reliability and real-time processing are paramount. In contrast, FL enables decentralized training by retaining data on local devices and sharing only model parameters with a central aggregator, thereby improving privacy preservation, reducing communication overhead, and facilitating adaptive, context-aware learning. This paper does not present a new federated learning algorithm or original experiment. Instead, it synthesizes existing research findings and applies the Analytic Hierarchy Process (AHP) to evaluate and prioritize critical factors for deploying FL in surveillance systems. By combining literature-based evidence with structured expert judgment, this study provides practical guidelines for real-world application. This paper identifies four key performance metrics—detection accuracy, false alarm rate, response time, and network load—and conducts a comparative analysis of FL and centralized AI-based approaches in the recent literature. In addition, the AHP is employed to evaluate expert survey data, quantitatively prioritizing eight critical factors for effective FL implementation. The results highlight detection accuracy and data security as the most significant concerns, indicating that FL presents a promising solution for future smart surveillance infrastructures. This research contributes to the advancement of AI-powered surveillance systems that are both high-performing and resilient under stringent privacy and operational constraints. Full article

Reusing cached data is a widely adopted technique for improving network and system performance. Future Internet architectures such as Named Data Networking (NDN) leverage intermediate nodes—such as proxy servers and routers—to cache and deliver data, reducing latency and alleviating load on original data sources. However, a fundamental challenge of this approach is the lack of trust in intermediate nodes, as users cannot reliably identify and verify them. To address this issue, many systems adopt data-oriented verification rather than sender authentication, using Merkle Hash Trees (MHTs) to enable users to verify both the integrity and authenticity of received data. Despite its advantages, MHT-based authentication incurs significant redundancy: identical hash values are often recomputed, and witness data are repeatedly transmitted for each segment. These redundancies lead to increased computational and communication overhead, particularly in large-scale data publishing scenarios. This paper proposes a novel scheme to reduce such inefficiencies by enabling the reuse of previously verified node values, especially transmitted witnesses. The proposed scheme improves both computational and transmission efficiency by eliminating redundant computation arising from repeated calculation of identical node values. To achieve this, it stores and reuses received witness values. As a result, when verifying 2ⁿ segments (n > 8), the proposed method achieves more than an 80% reduction in total hash operations compared to the standard MHT. Moreover, our method preserves the security guarantees of the MHT while significantly optimizing its performance in terms of both computation and transmission costs. Full article

Dynamic line rating (DLR) is an effective technique for real-time assessments on current-carrying capacities of overhead lines (OHLs), improving efficiencies and preventing overloads of transmission networks. Most research related to DLR forecasting mainly translates predictions of weather conditions into DLR forecasts or directly trains artificial intelligence models from DLR observations. Less attention has been given to the predictability of effective wind speeds (EWS) that describe overall convective cooling effects of varying weather conditions along OHLs, which could increase the reliability of DLR forecasts. To assess the effectiveness of EWS concepts in improving DLR predictions, this paper develops an EWS-based method for convective cooling predictions which are critical parameters dominating DLRs of overhead conductors. The EWS is first calculated from actual measurements of wind speeds and directions relative to OHL orientation based on the thermal model of overhead conductors. Then, an autoregressive model along with the Fourier series is employed to predict ultra-short-term EWS variations for up to three 10-min steps ahead, which are eventually converted into predictions of convective cooling effects along OHLs. The proposed EWS-based method is tested based on wind condition measurements in proximity to an OHL. Furthermore, to examine the impacts of angles between wind directions and line orientation on EWS estimation and thus EWS-based convective cooling predictions, the forecasting performance is assessed in the context of different line orientations. Results demonstrate that EWS-based ultra-short-term convective cooling predictions consistently outperform traditional forecasts from original wind conditions across all the tested line orientations. This highlights the significance of the EWS concept in reducing the complexity of DLR forecasting caused by the circular nature of wind directions, and in enhancing the accuracy of convective cooling predictions. Full article

In the context of secure and efficient data transmission over the future Internet, particularly for remote sensing and geospatial applications, reversible data hiding (RDH) in encrypted hyperspectral images (HSIs) has emerged as a critical technology. This paper proposes a novel RDH scheme specifically designed for encrypted HSIs, offering enhanced embedding capacity without compromising data security or reversibility. The approach introduces a multi-layer block labeling mechanism that leverages the similarity of most significant bits (MSBs) to accurately locate embeddable regions. To minimize auxiliary information overhead, we incorporate an Extended Run-Length Encoding (ERLE) algorithm for effective label map compression. The proposed method achieves embedding rates of up to 3.79 bits per pixel per band (bpppb), while ensuring high-fidelity reconstruction, as validated by strong PSNR metrics. Comprehensive security evaluations using NPCR, UACI, and entropy confirm the robustness of the encryption. Extensive experiments across six standard hyperspectral datasets demonstrate the superiority of our method over existing RDH techniques in terms of capacity, embedding rate, and reconstruction quality. These results underline the method’s potential for secure data embedding in next-generation Internet-based geospatial and remote sensing systems. Full article

Hymenopellis raphanipes is a high-value edible fungus with a short shelf life and high perishability, which poses significant challenges for quality control and safety assurance throughout its supply chain. Ensuring effective traceability is essential for improving production management, strengthening consumer trust, and supporting brand development. This study proposes a comprehensive traceability system tailored to the full lifecycle of Hymenopellis raphanipes, addressing the operational needs of producers and regulators alike. Through detailed analysis of the entire supply chain, from raw material intake, cultivation, and processing to logistics and sales, the system defines standardized traceability granularity and a unique hierarchical coding scheme. A multi-layered system architecture is designed, comprising a data acquisition layer, network transmission layer, storage management layer, service orchestration layer, business logic layer, and user interaction layer, ensuring modularity, scalability, and maintainability. To address performance bottlenecks in traditional systems, a multi-chain collaborative traceability model is introduced, integrating a mainchain–sidechain storage mechanism with an on-chain/off-chain hybrid management strategy. This approach effectively mitigates storage overhead and enhances response efficiency. Furthermore, data integrity is verified through hash-based validation, supporting high-throughput queries and reliable traceability. Experimental results from its real-world deployment demonstrate that the proposed system significantly outperforms traditional single-chain models in terms of query latency and throughput. The solution enhances data transparency and regulatory efficiency, promotes sustainable practices in green agricultural production, and offers a scalable reference model for the traceability of other high-value agricultural products. Full article

Reversible data hiding in encrypted point clouds presents unique challenges due to their unstructured geometry, absence of mesh connectivity, and high sensitivity to spatial perturbations. In this paper, we propose an efficient and secure reversible data hiding framework for encrypted point clouds, incorporating KD tree-based path planning, adaptive multi-MSB prediction, and a dual-model design. To establish a consistent spatial traversal order, a Hamiltonian path is constructed using a KD tree-accelerated nearest-neighbor algorithm. Guided by this path, a prediction-driven embedding strategy dynamically adjusts the number of most significant bits (MSBs) embedded per point, balancing capacity and reversibility while generating a label map that reflects local predictability. The label map is then compressed using Huffman coding to reduce the auxiliary overhead. For enhanced security and lossless recovery, the encrypted point cloud is divided into two complementary shares through a lightweight XOR-based (2, 2) secret sharing scheme. The Huffman tree and compressed label map are distributed across both encrypted shares, ensuring that neither share alone can reveal the original point cloud or the embedded message. Experimental evaluations on diverse 3D models demonstrate that the proposed method achieves near-optimal embedding rates, perfect reconstruction of the original model, and significant obfuscation of the geometric structure. These results confirm the practicality and robustness of the proposed framework for scenarios involving secure 3D point cloud transmission, storage, and sharing. Full article

Human Activity Recognition (HAR) using wearable sensors plays a critical role in applications such as healthcare, sports monitoring, and rehabilitation. Traditional approaches typically rely on centralized models that aggregate and process data from multiple sensors simultaneously. However, such architecture often suffers from high latency, increased communication overhead, limited scalability, and reduced robustness, particularly in dynamic environments where wearable systems operate under resource constraints. This paper proposes a distributed neural network framework for HAR, where each wearable sensor independently processes its data using a lightweight neural model and transmits high-level features or predictions to a central neural network for final classification. This strategy alleviates the computational load on the central node, reduces data transmission across the network, and enhances user privacy. We evaluated the proposed distributed framework using our publicly available multi-sensor HAR dataset and compared its performance against a centralized neural network trained on the same data. The results demonstrate that the distributed approach achieves comparable or superior classification accuracy while significantly lowering inference latency and energy consumption. These findings underscore the promise of distributed intelligence in wearable systems for real-time and energy-efficient human activity monitoring. Full article

The development and utilization of large-scale offshore wind power (OWP) are critical measures for achieving global energy transition. To address the demands of future large-scale OWP centralized development and transmission, this study systematically investigates the influencing factors and construction principles for topology selection in offshore wind power high-voltage direct current (HVDC) transmission systems delivering power to load centers. First, under the context of expanding the offshore wind power transmission scale, the necessity of transmitting OWP via HVDC overhead lines directly to load centers after landing is theoretically discussed. Five key topological influencing factors are then analyzed: offshore wind power collection schemes, multi-terminal HVDC network configurations, DC fault isolation mechanisms, offshore converter station architectures, and voltage source converter HVDC (VSC-HVDC) receiving terminal landing modes. Corresponding topology construction principles for direct HVDC transmission to load centers are proposed to guide system design. Finally, the feasibility of the proposed principles is validated through a case study of a multi-terminal HVDC system integrated into an actual regional power grid, demonstrating practical applicability. Full article

For the precise description of gas physical processes in high-voltage direct current (HVDC) transmission, an advanced and robust numerical framework for the simulation of transient particle densities in the course of corona discharges is developed in this work. The aim is the scalable and consistent modeling of the space charge density under realistic conditions. The core component of the framework is a discontinuous Galerkin method that ensures the conservative properties of the underlying hyperbolic problem. The space charge density at the electrode surface is imposed as a dynamic boundary condition via Lagrange multipliers. To increase the numerical stability and convergence rate, a homotopy approach is also integrated. For the experimental validation, a measurement concept was realised that uses a subtraction method to specifically remove the displacement current component in the signal and thus enables an isolated recording of the transient ion current with superimposed voltage stresses. The experimental results on a small scale agree with the numerical predictions and prove the quality of the model. On this basis, the framework is transferred to hybrid HVDC overhead line systems with a bipolar design. In the event of a fault, significant transient space charge densities can be seen there, especially when superimposed with new types of voltage waveforms. The framework thus provides a reliable contribution to insulation coordination in complex HVDC systems and enables the realistic analysis of electrohydrodynamic coupling effects on an industrial scale. Full article

The original Modbus specification for RS-485 and RS-232 buses supported broadcast transmission. As the protocol evolved into Modbus-TCP, to use the TCP transport, this useful feature was lost, likely due to the point-to-point nature of TCP connections. Later proposals did not restore the broadcast transmission capability, although they used UDP as transport and UDP, by itself, would have supported it. Moreover, they did not address the inherent lack of reliable delivery of UDP, leaving datagram loss detection and recovery to the application layer. This paper describes a novel redesign of Modbus-UDP that addresses the aforementioned shortcomings. It achieves a mean round-trip time of only 38% with respect to Modbus-TCP and seamlessly supports a previously published protocol based on Modbus broadcast. In addition, the built-in retransmission of Modbus-UDP reacts more efficiently than the equivalent Modbus-TCP mechanism, exhibiting 50% of its round-trip standard deviation when subject to a 1% two-way IP datagram loss probability. Combined with the lower overhead of UDP versus TCP, this makes the redesigned Modbus-UDP protocol better suited for a variety of Industrial Internet of Things systems with limited computing and communication resources. Full article

This paper presents a distributed measurement system intended to effectively monitor the health status of switchgears under varying temperature conditions. In particular, thermocouples are deployed as temperature sensors for the continuous monitoring of a medium-voltage (MV) switchgear. Then, by integrating a low-cost microcontroller unit, the proposed system can implement previously trained unsupervised learning techniques for health status evaluation. This approach enables the early detection of potential faults by identifying anomalous temperature patterns, thus supporting predictive maintenance and extending the lifespan of switchgears. The results show strong clustering performance with low execution times, highlighting the suitability of the method for resource-constrained hardware. Furthermore, onboard temperature processing eliminates the need for data transmission to remote servers, reducing latency and communication overhead while improving system responsiveness. The paper includes a numerical analysis on synthetic data as well as a validation on real measurements. Overall, the presented distributed measurement system offers a scalable and cost-effective solution to enhance the reliability and safety of MV switchgears. Full article

This paper presents the multi-triangular ring and self-routing protocol (MTRSRP), which is a new decentralized strategy designed to boost throughput and network efficiency in multiring scatternets. MTRSRP comprises two primary phases: leader election and scatternet formation, which collaborate to establish an effective multi-triangular ring topology. In the leader election phase, nodes exchange broadcast messages to gather neighbor information and elect coordinators through a competitive process. The scatternet formation phase determines the optimal number of rings based on the coordinator’s collected node information and predefined rules. The master nodes then send unicast connection requests to establish piconets within the scatternet, following a predefined role table. Intra- and inter-bridge nodes were activated to interconnect the piconets, creating a cohesive multi-triangular ring scatternet. Additionally, MTRSRP incorporates a self-routing addressing scheme within the triangular ring architecture, optimizing packet transmission paths and reducing overhead by utilizing master/slave relationships established during scatternet formation. Simulation results indicate that MTRSRP with dual-bridge connectivity outperforms the cluster-based on-demand routing protocol and Bluetooth low-energy mesh schemes in key network transmission performance metrics such as the transmission rate, packet delay, and delivery ratio. In summary, MTRSRP significantly enhances throughput, optimizes routing paths, and improves network efficiency in multi-ring scatternets through its multi-triangular ring topology and self-routing capabilities. 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

This paper proposes a low-latency and scalable architecture for Edge-Enabled Digital Twin networked control systems (E-DTNCS) aimed at multi-robot collision avoidance and remote control in dynamic and latency-sensitive environments. Traditional approaches, which rely on centralized cloud processing or direct sensor-to-controller communication, are inherently limited by excessive network latency, bandwidth bottlenecks, and a lack of predictive decision-making, thus constraining their effectiveness in real-time multi-agent systems. To overcome these limitations, we propose a novel framework that seamlessly integrates edge computing with digital twin (DT) technology. By performing localized preprocessing at the edge, the system extracts semantically rich features from raw sensor data streams, reducing the transmission overhead of the original data. This shift from raw data to feature-based communication significantly alleviates network congestion and enhances system responsiveness. The DT layer leverages these extracted features to maintain high-fidelity synchronization with physical robots and to execute predictive models for proactive collision avoidance. To empirically validate the framework, a real-world testbed was developed, and extensive experiments were conducted with multiple mobile robots. The results revealed a substantial reduction in collision rates when DT was deployed, and further improvements were observed with E-DTNCS integration due to significantly reduced latency. These findings confirm the system’s enhanced responsiveness and its effectiveness in handling real-time control tasks. The proposed framework demonstrates the potential of combining edge intelligence with DT-driven control in advancing the reliability, scalability, and real-time performance of multi-robot systems for industrial automation and mission-critical cyber-physical applications. Full article

Massive MIMO has emerged as a key enabler in modern wireless communication, delivering unparalleled spectral efficiency and connectivity. Yet, as antenna arrays become larger, significant obstacles arise in handling channel state information (CSI) feedback and the computational burden. This paper proposes a groundbreaking angular-domain transmission method that transitions from the conventional time–frequency domain to the angular domain. By employing projection-based transforms, akin to the FFT-based OFDMA model that introduced frequency-domain transmission with subcarriers, this technique exploits the inherent sparsity of massive MIMO channels in the angular domain, enabling data flows to be seamlessly mapped onto physical paths or rays. The resulting sparsity reduces signaling overhead and streamlines system complexity, making massive MIMO viable for next-generation networks. Simulation and empirical studies highlight how angular-domain strategies reduce feedback requirements, support Tera-bps data rates, and facilitate scalable designs for ultra-large-scale MIMO. Full article