Search Results (270)

This paper presents the design and implementation of a mobile robotic platform modelled as a layered Cyber–Physical System (CPS). Inspired by architectures commonly used in industrial Distributed Control Systems (DCSs) and large-scale scientific infrastructures, the proposed system incorporates modular hardware, distributed embedded control, and multi-level coordination. The robotic platform, named MapBot, is structured according to a five-layer CPS model encompassing component, control, coordination, supervisory, and management layers. This structure facilitates modular development, system scalability, and integration of advanced features such as a digital twin. The platform is implemented using embedded computing elements, diverse sensors, and communication protocols including Ethernet and I2C. The system operates within the ROS2 framework, supporting flexible task distribution across processing nodes. As a use case, two localization techniques—Adaptive Monte Carlo Localization (AMCL) and pose graph SLAM—are deployed and evaluated, highlighting the performance trade-offs in map quality, update frequency, and computational load. The results demonstrate that CPS-based design principles offer clear advantages for robotic platforms in terms of modularity, maintainability, and real-time integration. The proposed approach can be generalised for other robotic or mechatronic systems requiring structured, layered control and embedded intelligence. Full article

Attackers can use Ethernet or WiFi sniffers to capture smart home device traffic and identify device events based on packet length and timing characteristics, thereby inferring users’ home behaviors. To address this issue, traffic obfuscation techniques have been extensively studied, with common methods including packet padding, packet segmentation, and fake traffic injection. However, existing research predominantly utilizes non-real-time traffic to verify whether traffic obfuscation techniques can effectively reduce the recognition rate of traffic analysis attacks on smart home devices. It often overlooks the potential impact of obfuscation operations on device connectivity and functional integrity in real network environments. To address this limitation, an online experimental framework for three fundamental traffic obfuscation techniques is proposed: packet padding, packet segmentation, and fake traffic injection. Experimental results demonstrate that the proposed framework maintains the continuous connectivity and functional integrity of smart home devices with a low system overhead, achieving an average CPU usage rate of less than 0.4% and an average memory occupancy rate of less than 2%. Evaluation results based on the random forest classification method show that the device event recognition accuracy for injected fake traffic exceeds 89%. In this context, a higher recognition accuracy indicates that attackers are more effectively deceived by the injected fake traffic. Conversely, the recognition accuracy for packet padding and packet segmentation methods is nearly zero, and a lower recognition accuracy in these cases implies a more effective implementation of those obfuscation techniques. Further evaluation results based on the deep learning classification method reveal that the packet segmentation approach significantly reduces device recognition accuracy for certain devices to below 5%, while simultaneously increasing the false recognition rate for other devices to over 95%. In contrast, fake traffic injection achieves a device recognition accuracy exceeding 90%. Moreover, the obfuscation effect of the packet padding method is found to be suboptimal, a finding consistent with existing literature suggesting that no single obfuscation technique can effectively withstand all types of traffic analysis attacks. Full article

Digital substation technology adhering to the IEC 61850 standard has provided several opportunities and flexibility for the rapid growth and complexity of the present and future electrical grid. The communication infrastructure allows complete interoperability between legacy and modern devices. The emergence of 5G wireless communication and its utilization in substation operation presents significant advantages in terms of cost and scalability, while also introducing challenges. This paper identifies research gaps in the literature and offers valuable insights for future analysis by providing a simulation study using an empirical latency dataset of a 5G network to illustrate three aspects of substation operational challenges: coordination of protection schemes, sequential reception of packet data streams, and time synchronization processes. The findings show a mean latency of 8.5 ms for the 5G network, which is significantly higher than that of a wired Ethernet network. The results also indicate that the high latency and jitter compromise the selectivity of protection schemes. The variability in latency disrupts the sequence of arriving data packets such that the packet buffering and processing delay increases from around 1.5 ms to 11.0 ms and the buffer size would need to increase by 6 to 10 times to handle out-of-sequence packets. Additionally, a time synchronization success rate of 14.3% within a 0.1 ms accuracy range found in this study indicates that the IEEE 1588 protocol is severely affected by the latency fluctuations. Full article

Modern networked industrial applications often require low-latency communication. Some applications evolve over time, however, are tied to yet existing infrastructures, like power grids spanning across large areas. For instance, medium voltage direct current (MVDC) grids are evolving to a promising alternative to traditional medium voltage alternating current (MVAC) grids due to their efficiency and suitability for novel use cases like electric mobility. MVDC grids, however, require an active control and fault handling strategy. Some strategies demand for a continuous state exchange of the converter substations via a low-latency communication channel with less than 1 millisecond. While some communication approaches for MVDC grids are described in the literature, none of them is inherently designed to be secure. In this paper, we present a protocol for ultra-low-latency secure state exchange (PULLSE) based on conventional non-deterministic Ethernet and AES-GCM. We chose Ethernet in order to not limit the approaches usability in terms of hardware requirements or communication patterns. PULLSE is designed to prevent traffic eavesdropping, replay, and manipulation attacks. Full article

As the automotive industry continues to evolve rapidly, there is a growing demand for high-throughput reliable communication systems within vehicles. This paper presents the implementation and verification of a fault-tolerant Ethernet-based communication protocol tailored for automotive applications operating at 1 Gbps and above. The proposed system introduces a multi-port Network Interface Controller (NIC) architecture that supports real-time communication and robust fault handling. To ensure adaptability across various in-vehicle network (IVN) scenarios, the system allows for configurable packet sizes and transmission rates and supports diverse data formats. The architecture integrates cyclic redundancy check (CRC)-based error detection, real-time recovery mechanisms, and file-driven data injection techniques. Functional validation is performed using Verilog HDL simulations, demonstrating deterministic timing behavior, modular scalability, and resilience under fault injection. This paper presents a fault-tolerant Network Interface Controller (NIC), architecture incorporating CRC-based error detection, real-time recovery logic, and file-driven data injection. The system is verified through Verilog HDL simulation, demonstrating correct timing behavior, modular scalability, and robustness against injected transmission faults. Compared to conventional dual-port NICs, the proposed quad-port architecture demonstrates superior scalability and error tolerance under injected fault conditions. Experimental results confirm that the proposed NIC architecture achieves stable multi-port communication under embedded automotive environments. This study further introduces a novel quad-port NIC with an integrated fault injection algorithm and evaluates its performance in terms of error tolerance. 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 research addresses the enhancement of operational efficiency in apple-picking robots through the design of a bimanual spatial configuration enabling obstacle avoidance in contemporary orchard environments. A parallel coordinated harvesting paradigm for dual-arm systems was introduced, leading to the construction and validation of a six-degree-of-freedom bimanual apple-harvesting robot. Leveraging the kinematic architecture of the AUBO-i5 manipulator, three spatial layout configurations for dual-arm systems were evaluated, culminating in the adoption of a “workspace-overlapping Type B” arrangement. A functional prototype of the bimanual apple-harvesting system was subsequently fabricated. The study further involved developing control architectures for two end-effector types: a compliant gripper and a vacuum-based suction mechanism, with corresponding operational protocols established. A networked communication framework for parallel arm coordination was implemented via Ethernet switching technology, enabling both independent and synchronized bimanual operation. Additionally, an intersystem communication protocol was formulated to integrate the robotic vision system with the dual-arm control architecture, establishing a modular parallel execution model between visual perception and motion control modules. A coordinated bimanual harvesting strategy was formulated, incorporating real-time trajectory and pose monitoring of the manipulators. Kinematic simulations were executed to validate the feasibility of this strategy. Field evaluations in modern Red Fuji apple orchards assessed multidimensional harvesting performance, revealing 85.6% and 80% success rates for the suction and gripper-based arms, respectively. Single-fruit retrieval averaged 7.5 s per arm, yielding an overall system efficiency of 3.75 s per fruit. These findings advance the technological foundation for intelligent apple-harvesting systems, offering methodologies for the evolution of precision agronomic automation. Full article

This paper presents a novel metascheduling algorithm to enhance communication efficiency in off-chip time-triggered multi-processor system-on-chip (MPSoC) platforms, particularly for safety-critical applications in aerospace and automotive domains. Time-triggered communication standards such as time-sensitive networking (TSN) and TTEthernet effectively enable deterministic and reliable communication across distributed systems, including MPSoC-based platforms connected via Ethernet. However, their dependence on static resource allocation limits adaptability under dynamic operating conditions. To address this challenge, we propose an offline metascheduling framework that generates multiple precomputed schedules corresponding to different context events. The proposed algorithm introduces a selective communication strategy that synchronizes context information exchange with key decision points, thereby minimizing unnecessary communication while maintaining global consistency and system determinism. By leveraging knowledge of context event patterns, our method facilitates coordinated schedule transitions and significantly reduces communication overhead. Experimental results show that our approach outperforms conventional scheduling techniques, achieving a communication overhead reduction ranging from 9.89 to 32.98 times compared to a two-time-unit periodic sampling strategy. This work provides a practical and certifiable solution for introducing adaptability into Ethernet-based time-triggered MPSoC systems without compromising the predictability essential for safety certification. Full article

The evolution of the internet towards supporting time-critical applications, such as industrial cyber-physical systems (CPSs) and autonomous systems, has created an urgent demand for networks capable of providing deterministic, low-latency communication. Autonomous vehicles represent a particularly challenging use case within this domain, requiring both reliability and determinism for massive data streams—a requirement that traditional Ethernet technologies cannot satisfy. This paper addresses this critical gap by proposing a comprehensive scheduling framework based on Time-Aware Shaping (TAS) within the Time-Sensitive Networking (TSN) standard. The framework features two key contributions: (1) a novel baseline scheduling algorithm that incorporates a sub-flow division mechanism to enhance schedulability for high-bandwidth streams, computing Gate Control Lists (GCLs) via an iterative SMT-based method; (2) a separate heuristic-based computation acceleration algorithm to enable fast, scalable GCL generation for large-scale networks. Through extensive simulations, the proposed baseline algorithm demonstrates a reduction in end-to-end latency of up to 59% compared to standard methods, with jitter controlled at the nanosecond level. The acceleration algorithm is shown to compute schedules for 200 data streams in approximately one second. The framework’s effectiveness is further validated on a real-world TSN hardware testbed, confirming its capability to achieve deterministic transmission with low latency and jitter in a physical environment. This work provides a practical and scalable solution for deploying deterministic communication in complex autonomous and cyber-physical systems. Full article

Reverse engineering of internal vehicle communication is a crucial discipline in vehicle benchmarking. The process presents a time-consuming procedure associated with high manual effort. Car manufacturers use unique signal addresses and encodings for their internal data. Accessing this data requires either expensive tools suitable for the respective vehicles or experienced engineers who have developed individual approaches to identify specific signals. Access to the internal data enables reading the vehicle’s status, and thus, reducing the need for additional test equipment. This results in vehicles closer to their production status and does not require manipulating the vehicle under study, which prevents affecting future test results. The main focus of this approach is to reduce the cost of such analysis and design a more efficient benchmarking process. In this work, we present a methodology that identifies signals without physically manipulating the vehicle. Our equipment is connected to the vehicle via the On-Board Diagnostics (OBD)-II port and uses the Unified Diagnostics Service (UDS) protocol to communicate with the vehicle. We access, capture, and analyze the vehicle’s signals for future analysis. This is a holistic approach, which, in addition to decoding the signals, also grants access to the vehicle’s data, which allows researchers to utilize state-of-the-art methodologies to analyze their vehicles under study by greatly reducing necessary experience, time, and cost. Full article

Time-Sensitive Networking (TSN) is an advance Ethernet paradigm designed to provide low delay, low jitter, and deterministic transmission time. The Cycling Queuing and Forwarding (CQF) mechanism is introduced in TSN as a scheduler to achieve precise communication. Multi-CQF, as an extension of CQF, supports the transmission of various traffic types by assigning different cycle lengths to each queue group. In its original form, Multi-CQF-based scheduling algorithms do not account for flow sorting, leading to increased transmission delays and reduced network efficiency as a network dynamically changes. To enhance the performance of Multi-CQF, this paper initially utilizes queuing theory to analyze and manage traffic, providing foundation solutions. Subsequently, Mixed Integer Programming (MIP) and the Variable Neighborhood Search Genetic Algorithm (VNS-GA) are employed to optimize transmission delay in small- and large-traffic TSN networks, respectively. MIP quickly seeks out the optimal scheduling solution for small-traffic TSN networks using branch-and-bound and linear programming techniques, while the VNS-GA improves efficiency and performance for large-traffic ones by continuously adjusting the search neighborhood strategy. Comparing with other existing schemes, computer simulation reveals that MIP reduces delay by approximately 13% on average in small-traffic TSN networks, while the VNS-GA achieves an average delay reduction of 7% in large-traffic ones. Full article

Time-triggered Ethernet combines time-triggered and event-triggered communication, and is suitable for fields with high real-time requirements. Aiming at the problem that the traditional scheduling algorithm is not effective in scheduling event-triggered messages, a message scheduling algorithm based on multi-agent reinforcement learning (MADDPG, Multi-Agent Deep Deterministic Policy Gradient) and a hybrid algorithm combining SMT (Satisfiability Modulo Theories) solver and MADDPG are proposed. This method aims to optimize the scheduling of event-triggered messages while maintaining the uniformity of time-triggered message scheduling, providing more time slots for event-triggered messages, and reducing their waiting time and end-to-end delay. Through the designed scheduling software, in the experiment, compared with the SMT-based algorithm and the traditional DQN (Deep Q-Network) algorithm, the new method shows better load balance and lower message jitter, and it is verified in the OPNET simulation environment that it can effectively reduce the delay of event-triggered messages. Full article

The accurate prediction of autonomous vessel CO₂ emissions is critical for achieving IMO 2050 carbon neutrality and optimizing low-carbon maritime operations. Traditional models face limitations in real-time multi-source data analysis and dynamic cross-variable dependency modeling, hindering data-driven decision-making for sustainable autonomous shipping. This study proposes a Multi-scale Channel-aligned Transformer (MCAT) model, integrated with a 5G–satellite–IoT communication architecture, to address these challenges. The MCAT model employs multi-scale token reconstruction and a dual-level attention mechanism, effectively capturing spatiotemporal dependencies in heterogeneous data streams (AIS, sensors, weather) while suppressing high-frequency noise. To enable seamless data collaboration, a hybrid transmission framework combining satellite (Inmarsat/Iridium), 5G URLLC slicing, and industrial Ethernet is designed, achieving ultra-low latency (10 ms) and nanosecond-level synchronization via IEEE 1588v2. Validated on a 22-dimensional real autonomous vessel dataset, MCAT reduces prediction errors by 12.5% MAE and 24% MSE compared to state-of-the-art methods, demonstrating superior robustness under noisy scenarios. Furthermore, the proposed architecture supports smart autonomous shipping solutions by providing demonstrably interpretable emission insights through its dual-level attention mechanism (visualized via attention maps) for route optimization, fuel efficiency enhancement, and compliance with CII regulations. This research bridges AI-driven predictive analytics with green autonomous shipping technologies, offering a scalable framework for digitalized and sustainable maritime operations. Full article

The Internet of Vehicles plays a crucial role in advancing intelligent transportation systems, with In-Vehicle Ethernet serving as the fundamental backbone network of the new generation of in-vehicle communication. However, In-Vehicle Ethernet faces various network security threats, including data theft, data tampering, and malicious attacks. This study focuses on network intrusion and security issues in In-Vehicle Ethernet, by analyzing the data characteristics of Audio Video Transport Protocol and potential network attack means. We innovatively propose a network intrusion detection method based on a weighted histogram algorithm. This method aims to enhance the security of In-Vehicle Ethernet. Experimental results show that the anomaly detection rate of the proposed weighted histogram algorithm in this study is 99.7%, which shows an improvement of 15.8% compared with the traditional Bayesian algorithm, and 6.9% higher than the decision tree algorithm. Thus, our approach enhances the stability and anti-attack ability of In-Vehicle Ethernet, providing a solid network security for In-Vehicle Networks. Full article

With the rapid development of intelligent driving technology, in-vehicle bus networks face increasingly stringent requirements for real-time performance and data transmission. Traditional bus network technologies such as LIN, CAN, and FlexRay are showing significant limitations in terms of bandwidth and response speed. In-Vehicle Ethernet, with its advantages of high bandwidth, low latency, and high reliability, has become the core technology for next-generation in-vehicle communication networks. This study focuses on bandwidth waste caused by guard bands and the limitations of Frame Pre-Emption in fully utilizing available bandwidth in In-Vehicle Ethernet. It aims to optimize TSN scheduling mechanisms by enhancing scheduling flexibility and bandwidth utilization, rather than modeling system-level vehicle functions. Based on the Time-Sensitive Networking (TSN) protocol, this paper proposes an innovative Adaptive Frame Segmentation (AFS) algorithm. The AFS algorithm enhances the performance of In-Vehicle Ethernet message transmission through flexible frame segmentation and efficient message scheduling. Experimental results indicate that the AFS algorithm achieves an average local bandwidth utilization of 94.16%, improving by 4.35%, 5.65%, and 30.48% over Frame Pre-Emption, Packet-Size Aware Scheduling (PAS), and Improved Qbv algorithms, respectively. The AFS algorithm demonstrates stability and efficiency in complex network traffic scenarios, reducing bandwidth waste and improving In-Vehicle Ethernet’s real-time performance and responsiveness. This study provides critical technical support for efficient communication in intelligent connected vehicles, further advancing the development and application of In-Vehicle Ethernet technology. Full article