Search Results (36)

Advancing environmental monitoring through coordinated autonomous systems is central to sustainable smart region governance and data-driven territorial management. The article presents an engineering-oriented architecture and deployment methodology for an integrated wildfire monitoring and response system that combines unmanned aerial vehicles (UAVs), unmanned ground vehicles (UGVs), and stationary sensor networks (SNs). We formalise hub-and-spoke infrastructure placement as a mixed-integer optimisation problem that accounts for platform types, endurance, travel times and logistical constraints, and propose a practical pre-processing pipeline (confidence scoring, resampling, Kalman/median filtering, strategy fusion) for heterogeneous telemetry and imagery. The system couples multimodal neural network processing (image backbones, clustering and time-series models) with online resource-allocation and mission-planning mechanisms to prioritise UAV/UGV sorties and dynamically select launch sites. The article describes scenario-driven operational modes (early warning, alarm verification, autonomous local extinguishing, post-fire recovery, sensor-gap compensation, and inter-hub reinforcement), defines validation protocols (synthetic experiments, precision/recall/F1, and hardware-in-the-loop testing), and proposes KPIs to assess environmental, social, and economic impacts for smart regions. The contribution is a reproducible, deployment-focused blueprint that bridges conceptual UAV–UGV–SN research and practical implementation, highlighting trade-offs in reliability, communication redundancy, and sustainability, and outlining directions for simulation, field pilots and algorithmic refinement. Full article

As mobile networks evolve toward Beyond 5G and 6G architectures, energy efficiency and sustainability have become increasingly critical due to growing traffic volumes, denser base station deployments, and the rising number of connected devices. Supporting Ultra-Reliable Low-Latency Communication (URLLC) services is particularly challenging, as their stringent requirements for both high reliability and minimal latency can lead to a significant increase in energy consumption within the radio access network. This paper examines slot structure mechanisms for concurrently servicing URLLC and enhanced Mobile Broadband (eMBB) traffic within the 5G Advanced framework, with a focus on improving energy efficiency and optimizing radio resource utilization. We propose an adaptive algorithm for managing radio interface time resources, which dynamically allocates sub-slots based on current network load and radio channel conditions. The system model is implemented in Simulink and incorporates URLLC and eMBB traffic generation, signal-to-noise ratio estimation, and a priority-based scheduling mechanism. Simulation results demonstrate that the proposed approach meets URLLC latency and reliability requirements while reducing redundant transmissions and enhancing the energy efficiency of the radio access network. These findings position the proposed method as a promising solution for the design of energy-efficient, next-generation mobile networks. Full article

Prompt 3D mapping of grain storage is essential for effective management. However, standard mapping algorithms encounter a number of challenges, with the typical granary environment containing dust, grain piles, and narrow aisles. A single robotic agent is not able to provide complete area coverage, and most multi-robot approaches involve re-scanning the same areas due to a lack of explicit viewpoint-based task allocation processes. In order to overcome the above issues, we propose an air–ground collaborative exploration system for complex multi-grain pile scenarios. Exploration redundancy can be reduced by estimating the advantages of viewpoints through ray tracing and assigning the tops of the grain piles to aerial robots with ground vehicles in lower regions and narrow aisles. In order to manage dense dust (5–15 mg/m³), the quality-aware fusion strategy evaluates the reliability of the distance and point density of the sensing to reduce the influence of degraded aerial depth data. Moreover, mapping relies on LiDAR data to ensure mapping quality. A mechanism for re-scanning to enable coverage-driven exploitation of insufficiently explored regions is subsequently proposed. The simulation results show that the design achieved a grain pile coverage of 97.2%, with the total exploration time reduced by 20.1% over single-robot baselines. The results indicate that viewpoint-aware task allocation and dust-sensitive perception fusion can offer a practical solution for autonomous inspection in GPS-restricted, dust-rich industrial environments, such as granary facilities. Full article

As the development of High-Speed Railways (HSRs) shifts from scale expansion to quality and efficiency, high-density timetables face increasing challenges regarding operational stability. Traditional capacity metrics often prioritize volume over service quality, neglecting the economic and service implications of delays. To reconcile theoretical capacity with practical reliability, this paper proposes a novel Reliable Value (RV)-oriented framework for HSR timetabling. We construct a Reserve Capacity Incremental Heuristic Optimization Framework that employs a synergetic integrated stochastic optimization strategy. This methodology treats reserve capacity as a systematically varied analytical parameter rather than a static constant, integrating redundancy layout planning with dynamic recovery adjustments under stochastic delay scenarios. The RV metric quantitatively combines efficiency (Expected Running Time) and robustness (Indirect Capacity Loss). A case study on the Beijing–Shanghai high-speed railway corridor demonstrates a non-linear relationship between reserve capacity allocation and system value. The results identify an optimal saturation interval of 5 to 14 min, where the marginal gains in reliability maximize the overall system value without excessively compromising operational efficiency. These findings provide theoretical support for transitioning from static capacity planning to proactive, value-based resilience engineering through optimized redundancy allocation. Full article

This paper addresses the high-precision transportation control of a large space flexible truss using four space robots, with a focus on dynamic modeling and control strategy design. The system’s dynamic model is derived based on Kane’s method, which facilitates efficient modeling of the complicated rigid–flexible dynamics. Considering the truss’s flexible vibration as a key disturbance source, a nonlinear disturbance observer (NDO) is designed to achieve effective disturbance estimation. Then, to ensure high-precision trajectory tracking of such a complicated dynamics system, an integral sliding mode control (ISMC) strategy is developed based on NDO. Furthermore, leveraging the system’s actuator redundancy, the actuator inputs are weighted and allocated by accounting for individual actuator performance, which enhances the operational reliability. The effectiveness of the proposed control strategy is verified through theoretical analysis and numerical simulations. Full article

Multi-robot systems have become integral to a wide range of practical applications, requiring efficient and reliable task allocation to maintain operational performance, particularly in dynamic and unpredictable environments such as disaster response, industrial automation, and autonomous exploration. However, unforeseen task failures due to robot malfunctions or communication breakdowns significantly impact system stability and efficiency. Therefore, we developed a fault-tolerant framework for dynamic task reassignment to ensure minimal disruption in multi-robot operations. System resilience is enhanced by integrating real-time failure detection with an adaptive task reallocation mechanism. The developed framework employs a recovery-driven task reassignment algorithm that redistributes failed tasks among available robots using robust coordination mechanisms and adaptive scheduling strategies. Communication between robots and the central coordination system is facilitated using the message queuing telemetry transport protocol, which offers a lightweight and efficient publish–subscribe communication model optimized for real-time data exchange that ensures low-latency and energy-efficient messaging in constrained environments. Additionally, the framework incorporates actuator redundancy and adaptive control allocation to recover from partial hardware failures without reassigning tasks. Simulations were conducted to evaluate the model’s performance in handling mid-task failures under varying operational conditions. Experimental results indicate that the framework significantly reduces downtime, improves task completion rates, and enhances overall system resilience and highlighting the framework’s potential for deployment in critical real-world applications, such as disaster response, industrial automation, and autonomous exploration, where reliability and adaptability are essential. Full article

The Internet of Vehicles (IoV) can transmit the status information of vehicles and roads through single-hop or multi-hop broadcast communication, which is a key technology for building intelligent transportation systems and enhancing road safety. However, in dense traffic environments, broadcasting Emergency messages via vehicles can easily trigger massive forwarding redundancy, leading to channel resource selection conflicts between vehicles and affecting the reliability of inter-vehicle communication. This paper analyzes the forwarding near the single-hop transmission radius boundary of the sending node in a probability-based inter-vehicle multi-hop forwarding scheme, pointing out the existence of the boundary forwarding redundancy problem. To address this problem, this paper proposes two probability-based schemes with boundary optimization: (1) By optimizing the forwarding probability distribution outside the transmission radius boundary of the sending node, the forwarding nodes outside the boundary can be effectively utilized while effectively reducing the forwarding redundancy they bring. (2) Additional forwarding backoff timers are allocated to nodes outside the transmission radius boundary of the sending node based on the distance to further reduce the forwarding redundancy outside the boundary. Experimental results show that, compared with the reference schemes without boundary forwarding probability optimization, the proposed schemes significantly reduce forwarding redundancy of Emergency messages while maintaining good single-hop and multi-hop transmission performance. When the reference transmission radius is 300 m and the vehicle density is 0.18 veh/m, compared with the probability-based forwarding scheme without boundary optimization, the proposed schemes (1) and (2) improve the single-hop packet delivery ratio by an average of about 5.41% and 11.83% and reduce the multi-hop forwarding ratio by about 18.07% and 36.07%, respectively. Full article

This paper proposes a cooperative framework for resource allocation in multi-access edge computing (MEC) under a partial task offloading setting, addressing the joint challenges of learning performance and system efficiency in heterogeneous edge environments. In the proposed architecture, selected users act as edge servers (SEs) that collaboratively assist others alongside a central server (CS). A joint optimization problem is formulated to integrate model training with resource allocation while accounting for data freshness and spatial correlation among user tasks. The correlation-aware formulation penalizes outdated and redundant data, leading to improved robustness against non-i.i.d. distributions. To solve the NP-hard problem efficiently, a projected gradient descent (PGD) method is developed. The simulation results demonstrate that the proposed cooperative approach achieves a balanced delay of $0.042$ s, close to edge-only computing ($0.033$ s) and $30\%$ lower than the CS-only mode, while improving clustering accuracy to $99.2\%$ (up to $15\%$ higher than the baseline). Moreover, it reduces the central server load by nearly half, ensuring scalability and latency compliance within 3GPP limits. These findings confirm that cooperation between SEs and the CS substantially enhances reliability and performance in distributed Edge-AI system. Full article

This work proposes a photonics-aided W-band integrated sensing and communication (ISAC) system using photonics-aided frequency multiplication to suppress phase noise. Conventional dual-laser architectures suffer from phase noise accumulation, degrading both communication reliability and sensing resolution. To address this, we integrate photonics-aided frequency multiplication with orthogonal frequency-division multiplexing (OFDM), enabling a unified signal structure that simultaneously encodes communication data and radar waveforms without redundant resource allocation. Theoretical analysis reveals phase noise cancellation through coherent beating of symmetrically filtered sidebands in the photodetector (PD). Results demonstrate concurrent delivery of probability shaping (PS)-256QAM OFDM signals with a symbol error rate below 4.2 × 10⁻² and radar sensing with a 13.6 dB peak-to-sidelobe ratio (PSLR). Under a 1 MHz laser linewidth, the system achieves a 3.2 dB PSLR improvement over conventional methods, validating its potential for high-performance ISAC in beyond-5G networks. Full article

This paper addresses a well-known research topic in the design of complex systems, specifically within the class of reliability optimization problems (ROPs). It focuses on optimal reliability–redundancy allocation problems (RRAPs) for large binary systems with hybrid structures. Two main objectives are considered: (1) to maximize system reliability under cost and volume constraints, and (2) to achieve the required reliability at minimal cost under a volume constraint. The system reliability model includes components with only two states: normal operating or failed. High reliability can result from directly improving component reliability, allocating redundancy, or using both approaches together. Several redundancy strategies are covered: active, passive, hybrid standby with hot, warm, or cold spares, static redundancy such as TMR and 5MR, TMR structures with control logic and spares, and reconfigurable TMR/Simplex structures. The proposed method uses a zero–one integer programming formulation that applies log-transformed reliability functions and binary decision variables to represent subsystem configurations. The experimental results validate the approach and confirm its efficiency. Full article

The Internet of Things (IoT) aims to wirelessly connect billions of physical things to the IT infrastructure. Although there are several radio access technologies available, few of them meet the needs of Internet of Things applications, such as long range, low cost, and low energy consumption. The low data rate of low-power wide-area network (LPWAN) technologies, particularly SigFox, makes them appropriate for Internet of Things applications since the longer the radio link’s useable distance, the lower the data rate. Network reliability is the primary goal of SigFox technology, which aims to deliver data messages successfully through redundancy. This raises concerns about SigFox’s scalability and leads to one of its flaws, namely the high collision rate. In this paper, the goal is to prevent collisions by switching to time division multiple access (TDMA) from SigFox’s Aloha-based medium access protocol, utilizing only orthogonal channels, and eliminating redundancy. Consequently, during a designated time slot, each node transmits a single copy of the data message over a particular orthogonal channel. To achieve this, a multi-agent, off-policy reinforcement learning (RL) Q-Learning technique will be used on top of SigFox. In other words, the objective is to increase SigFox’s scalability through the use of Reinforcement Learning based time slot allocation (RL-SCAP). The findings show that, especially in situations with high node densities or constrained communication slots, the proposed protocol performs better than the basic SCAP (Slot and Channel Allocation Protocol) by obtaining a higher Packet Delivery Ratio (PDR) in average of 60.58%, greater throughput in average of 60.90%, and a notable decrease in collisions up to 79.37%. Full article

Multi-level coded modulation (MLCM) enhances data transmission by allocating error correction more effectively to bits with higher error probabilities, thus optimizing redundancy and improving performance. Despite MLCM’s advantages over traditional bit-interleaved coded modulation (BICM) systems in certain scenarios, its integration with hybrid automatic repeat request (HARQ) systems remains underexplored. HARQ, which combines the benefits of forward error correction (FEC) and automatic repeat request (ARQ), significantly increases resilience to interference and fading, enhancing overall system reliability. This paper bridges the gap by integrating HARQ techniques into the MLCM framework, which was specifically adapted to the layered nature of MLCM. We present tailored hybrid retransmission strategies for each layer of MLCM, demonstrating substantial gains in retransmission efficiency and overall transmission performance. Full article

This study introduces a novel dual-battery architecture with intelligent auto-switching control, designed to ensure uninterrupted operation of agricultural sensing systems in environments with unpredictable energy availability. The proposed system integrates Lithium-Sulphur (Li-S) and Lithium-Ion (Li-Ion) batteries with advanced switching algorithms—specifically, the Dynamic Load Balancing–Power Allocation Optimisation (DLB–PAO) and Dynamic Load Balancing–Genetic Algorithm (DLB–GA)—tailored to maximise sensor operational longevity. By optimizing the dual-battery configuration for real-world deployment and conducting comparative evaluations across multiple system designs, this work advances an innovative engineering solution with significant practical implications for sustainable agriculture and remote sensing applications. Unlike conventional single-battery systems or passive redundancy approaches, the architecture introduces active redundancy, adaptive energy management, and fault tolerance, substantially improving operational continuity. A functional prototype was experimentally validated using realistic load profiles, demonstrating seamless battery switching, extended uptime, and enhanced energy reliability. To further assess long-term performance under continuous Internet of Things (IoT) operation, a simulation framework was developed in MATLAB/Simulink, incorporating battery degradation models and empirical sensor load profiles. The experimental results reveal distinct performance improvements. A baseline single-battery system sustains 28 h of operation with 31.2% average reliability, while a conventional dual-battery configuration extends operation to 45 h with 42.6% reliability. Implementing the DLB–PAO algorithm elevates the average reliability to 91.7% over 120 h, whereas the DLB–GA algorithm achieves near-perfect reliability (99.9%) for over 170 h, exhibiting minimal variability (standard deviation: 0.9%). The integration of intelligent auto-switching mechanisms and metaheuristic optimisation algorithms demonstrates a marked enhancement in both reliability and energy efficiency for soil nutrient monitoring systems. This method extends the lifespan of electronic devices while ensuring reliable energy storage over time. It creates a practical foundation for sustainable IoT agricultural systems in areas with limited resources. Full article

In modern aerospace systems, effective sensor optimisation is essential for ensuring reliable diagnostics, efficient resource allocation, and proactive maintenance. This paper presents Normalised Diagnostic Contribution Index (NDCI) integration into the Multi-Objective Sensor Optimisation Framework (MOSOF) to address application-specific performance nuances. Building on previous work, the proposed approach leverages a multi-objective genetic algorithm to optimise key criteria, including performance, cost, reliability management, and compatibility. NDCI is derived from simulation data obtained via the Boeing 737-800 Environmental Control System (ECS) using the SESAC platform, where degradation level readings across four fault modes are analysed. The framework evaluates sensor performance from the perspectives of Original Equipment Manufacturers (OEM), Airlines, and Maintenance Repair Overhaul (MRO) organisations. Validation against the Minimum Redundancy Maximum Relevance (mRMR) method highlights the distinct advantage of NDCI by identifying an optimal set of three sensors compared to mRMR’s six-sensor solution, and MOSOF’s multi-objective insertion enhances sensor deployment for different stakeholders. This integration not only expands the feasible solution space for sensor-pair configurations but also emphasises diagnostic value over redundancy. Overall, the enhanced NDCI-MOSOF offers a scalable, multi-stakeholder approach for next-generation sensor optimisation and predictive maintenance in complex aerospace systems. The results demonstrate significant improvements in diagnostics efficiency for stakeholders. Full article

Wind power, as a pivotal renewable energy source, is anticipated to play a critical role in ensuring the reliability, security, and stability of the global energy supply system. Accurate prediction of wind power density (WPD) holds significant practical importance for wind farms, grid operators, and the entire wind power industry, as it facilitates informed decision-making, optimized resource allocation, and enhanced system performance. This paper proposes a novel WPD forecasting model based on RF-DBO-VMD feature selection and BiGRU optimized by an attention mechanism. The proposed model consists of three main stages. First, critical physical features relevant to WPD are identified using random forest (RF), effectively eliminating data redundancy and enhancing prediction efficiency. Second, the variational mode decomposition (VMD) parameters are optimized via the dung beetle optimizer (DBO) algorithm to extract independent intrinsic mode functions (IMFs), which, alongside the original data, serve as temporal feature inputs. Finally, an attention mechanism is employed to identify important information from the outputs of the BiGRU model, and the Grid Search (GS) method is used to optimize the BiGRU-Attention model, yielding optimal predictions. The experimental results demonstrate the model’s high predictive accuracy, evidenced by an R² value of 0.9754. Notably, the Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Mean Squared Error (MSE) are substantially minimized compared to alternative models. These results highlight the model’s potential to provide effective forecasting insights for future applications, such as energy trading and power system management, which will be further explored in real-world scenarios. Full article