As wireless communication technology advances, the convergence of the Internet of Vehicles (IoV) and edge computing is increasingly underpinning the progression of intelligent driving systems. Despite these advances, challenges persist in the stability and efficiency of vehicle-edge collaborative computing frameworks. This study introduces a service availability and task complexity-aware scheduling framework for intelligent driving tasks (SATCAS-IDTs), ensuring the reliability of task scheduling between user vehicles and in-vehicle computing platforms (IVCPs) during high-speed mobility. Initially, a task complexity quantification model for intelligent driving is constructed, incorporating metrics such as information entropy, image contrast, energy, and color space. Subsequently, a vehicle mobility model tailored for high-speed multilane environments is designed, upon which the mobility and service availability aware IVCP initial screening algorithm (MSAIF-IVCP) is developed, preselecting IVCPs that meet mobility and service availability requirements. Lastly, the double deep recurrent reinforcement learning task scheduling algorithm with multiobservation hybrid encoder (DDRRL-MOHE) is proposed. By intelligently balancing task delay and processing accuracy, this algorithm dynamically adapts edge computing resources, effectively reducing response times and enhancing overall system performance. Experimental validation shows that our proposed method improves the task processing accuracy by about 1.70% on average and reduces the average latency by about 1.97% over the baseline method.

In the domain of Industrial Internet of Things (IIoT) applications, methodologies such as meta computing are essential for ensuring timely and efficient data acquisition within intricate environments characterized by multiple edge devices and isolated sensor networks. The Age of Information (AoI), a key metric for evaluating data timeliness and relevance, has emerged as a focal point for improving decision-making and system responsiveness. However, managing these systems in resourcelimited edge environments is challenging, as traditional methods struggle to link sink node selection with data collection path planning, leading to inefficiency and poor AoI performance. This paper develops a collaborative optimization framework based on meta computing, which dynamically coordinates distributed computational resources as a unified virtual system. It combines self-organizing mapping (SOM) with reinforcement learning (RL) to optimize sink node selection and data collection paths using AoI metrics. Experimental results show the method significantly reduces AoI, improves data freshness, and optimizes energy use, highlighting meta computings potential for scalable, efficient IIoT solutions.

Offshore drilling platforms (ODPs) are critical infrastructure for exploring and developing marine oil and gas resources. As these platforms' capabilities expand, deploying intelligent surveillance services to ensure safe production has become increasingly important. However, the unique geographical locations and harsh environmental conditions of ODPs pose significant challenges for processing large volumes of video data, complicating the implementation of efficient surveillance systems. This study proposes a Cloud-Edge Large-Tiny Model Collaborative (CELTC) architecture grounded in deep reinforcement learning to optimize the processing and decision-making of surveillance data in offshore drilling platform scenarios. CELTC architecture leverages edge-cloud computing, deploying complex, high-precision large models on cloud servers and lightweight tiny models on edge devices. This dual deployment strategy capitalizes on tiny models' rapid response and large cloud models' high-precision capabilities. Additionally, the architecture integrates a deep reinforcement learning algorithm designed to optimize the scheduling and offloading of computational tasks between large and tiny models in the cloud-edge environment. The efficacy of the proposed architecture is validated using real-world surveillance data from ODPs through simulations and comparative experiments.

Achieving real-time processing of tasks has become a crucial objective in the Internet of Vehicles (IoV) field. During the online generation of tasks in IoV systems, many dependency tasks arrive randomly within continuous time frames, and it is impossible to predict the number of arriving tasks and the dependencies between sub-tasks. Offloading dependent tasks, which are quantity-intensive and have complex dependencies, to appropriate vehicle edge servers (VESs) for online processing of large-scale tasks remains a challenge. Firstly, we innovatively propose a VES task parallel processing framework incorporating a multi-level feedback queue to enhance the cross-slot parallel processing capabilities of the IoV system. Secondly, to reduce the complexity of problem-solving, we employ the Lyapunov optimization method to decouple the online task offloading control problem into single-stage mixed-integer nonlinear programming problem. Finally, we design an online task decision-making algorithm based on multi-agent reinforcement learning to achieve real-time task offloading decisions in complex dynamic IoV environments. To validate our algorithm’s superiority in dynamic IoV systems, we compare it with other online task offloading decision-making algorithms. Simulation results show that ours significantly reduces the all-task processing latency of IoV system by 15% compared to the comparison algorithms, and the task average latency time is reduced by 14%.

As smart cities evolve, rising computational demands strain infrastructures. Offloading tasks to edge cloud data centers offers potential but faces challenges like high latency, energy use, and data leakage, especially in dense urban areas. This paper presents a low-latency, energy-efficient digital twin (DT) architecture tailored for smart cities, integrating edge computing (EC) and multiple s (IRS) to enhance communication. Dynamic voltage and frequency scaling (DVFS) technology is considered for user devices to reduce energy consumption. To mitigate the risk of user privacy leakage during task offloading, we address sensitive user location data that may be exposed by proposing a perturbed sliding task queue (PSTQ) algorithm based on differential privacy (DP), and demonstrate the effectiveness of the algorithm. To optimize task processing time and energy efficiency, we decompose the complex problem using block coordinate descent and propose an intelligent scheduling for energy sustainability (ISES) algorithm based on Karush-Kuhn-Tucker conditions and deep reinforcement learning (DRL). Experimental results demonstrate that our proposed architecture and algorithms achieve over 90% improvement in key optimization objectives, alleviating the computational pressure on existing devices while significantly enhancing task processing efficiency and energy sustainability.

The rapid increase in camera installations on off-shore drilling platforms has intensified the challenge of high-concurrency video data processing. Traditional single-cloud server video analysis is becoming inadequate, leading to heightened processing latency and bandwidth overuse. In response, we propose an edge-cloud collaborative video object detection architecture based on a GRU-Enhanced Double Deep Q-Network (GE-DDQN). Our architecture utilizes the YOLOv8 algorithm for object detection and incorporates a GE-DDQN model for efficient task offloading between edge and cloud computing. A token bucket mechanism is employed to regulate data offloading rates from edge devices, optimizing collaborative efficiency for high-performance detection. Comparative experiments on real offshore drilling platform video data underscore the superiority of our method in managing dynamic and complex video streams. The architecture demonstrates remarkable video analysis performance in this domain, achieving a precision of 90.2% and a processing speed of 25.45 FPS, marking a significant advancement in edge-cloud video analytics.

With the evolution of 6G technology, mobile edge computing is rapidly advancing as a crucial application scenario. This research presents an innovative method for vehicle-edge task offloading decision-making, leveraging real-time data inputs such as channel conditions, image entropy, and detector confidence levels. We propose a collaborative task processing framework for vehicle-edge computing that effectively combines lightweight and heavyweight models to cater to varying demands, ensuring efficient task execution. Additionally, the study introduces a custom-designed reinforcement learning algorithm aimed explicitly at optimizing offloading scheduling. This algorithm boosts decision-making accuracy and efficiency and features a comprehensive reward system to achieve a balanced trade-off between detection performance and latency. The frameworks efficacy is thoroughly evaluated in complex driving scenarios using the SODA10M dataset. Our results indicate the frameworks capability to achieve convergence, enhance precision, ensure stability, and maintain a lightweight operation, emphasizing its suitability for real-world implementation. This work provides practical and efficient strategies for intelligent driving task scheduling to meet the requirements of contemporary dynamic environments.