Edge-Cloud Collaborative Data Processing and Adaptive Optimization Methods for Intelligent Sensing Systems

Task offloading and resource allocation are the major elements of edge computing. A reasonable task offloading strategy and resource allocation scheme can reduce task processing time and save system energy consumption. Most of the current studies on the task migration of edge computing only consider the resource allocation between terminals and edge servers, ignoring the huge computing resources in the cloud center. In order to sufficiently utilize the cloud and edge server resources, we propose a coarse-grained task offloading strategy and intelligent resource matching scheme under Cloud-Edge collaboration. We consider the heterogeneity of mobile devices and inter-channel interference, and we establish the task offloading decision of multiple end-users as a game-theory-based task migration model with the objective of maximizing system utility. In addition, we propose an improved game-theory-based particle swarm optimization algorithm to obtain task offloading strategies. Experimental results show that the proposed scheme outperforms other schemes with respect to latency and energy consumption, and it scales well with increases in the number of mobile devices.

Mobile IoT networks face major problems in completing efficient task offloading and allocating resources effectively, as all major actions require ample energy consumption and service delays. This paper proposes a solution in the form of a reinforcement learning-based framework that is capable of dynamically optimizing these activities with the help of Group Relative Policy Optimization (GRPO). The new method outcompetes traditional heuristic-nometric methods by being able to adjust device inactivity, network activity, and available resources. Devices are structured into mobility groups and task offloading is defined as an MDP or Markov Decision Process. An ample amount of offloading strategies can be obtained by introducing declaration of tasks and corresponding computers, network throughput, and queueing delays. Additionally, GRPO aids in the allocation of computing power, network bandwidth, or storage to maximize resource allocation without breaking task deadlines and other resource limitations. Comparing the GRPO-based method to traditional methods via simulation showed increased performance and reduced service delay. The results clearly demonstrate that GRPO helps reduce service delay and consumption of energy significantly in high load with high dynamism conditions. With the inclusion of decision-making processes along with constraint-tolerance and reinforcement learning, this research aids in IoT frameworks that require low energy output with less delay. The recommended method helps build a strong base for efficient task delegation alongside increased performance and scalability, which subsequently enhances AI edge computing and smart management of IoT systems.

In medical vehicular networks, medical vehicles can serve as efficient mobile medical service points to provide necessary and critical medical services for patients while in motion. The delay requirement is very vital for medical services to guarantee service quality and save the lives of patients. Mobile Edge Computing (MEC), as an emerging network paradigm, enables the computation extensive tasks to be offloaded to edge servers, efficiently reducing the delay and bandwidth demands. MEC technology is a promising solution to provide high-quality medical services for users in medical vehicular networks. However, task offloading and resource allocation incurs additional service delay and energy consumption, affecting the overall service performance and Quality of Experience (QoE) of users. Thus, realizing the optimal task offloading and resource allocation in MEC-enabled medical vehicular networks, to reduce task completion time and energy consumption, becomes a potential challenge. To address the challenge, we investigate the joint task offloading and resource allocation problem in MEC-enabled medical vehicular networks to improve the QoE of users. Considering the resource requirements and QoS constraint, we formulate a multi-objective optimization model, with the target of average task completion time and average energy consumption minimization. On this basis, we propose a MOEAD-based task offloading and resource allocation (IMO) algorithm to solve it. Furthermore, in order to obtain the optimal solution and speed up the algorithm convergence, we design a greedy strategy-based population initialization algorithm. The extensive simulations demonstrate that compared to existing algorithms, our proposed IMO algorithm can obtain a smaller average completion time, and achieve better tradeoff between task completion time and energy consumption.

Mobile edge computing (MEC) can effectively overcome the shortcomings of high-latency in mobile cloud computing (MCC) by deploying the cloud resources, e.g., storage and computational capability, to the edge. However, the limited computation capability of the MEC restricts the scalability of offloading. Therefore, the basic requirements of the MEC system are to explore effective offloading decisions and resource allocation methods. To address it, we develop a collaborative computing system composed of local computing (mobile device), MEC (edge cloud) and MCC (central cloud). Based on the proposed collaborative computing system, we design a novel Q-learning based computation offloading (QLCOF) policy to achieve the optimal resource allocation and offloading scheme by prescheduling the computation side for each task from a global perspective. Specifically, we first model the offloading decision process as a Markov decision process (MDP) and design a state loss function (STLF) to measure the quality of experience (QoE). After that, we define the cumulation of STLFs as the system loss function (SYLF) and formulate an SYLF minimization problem. Due to the difficulty to directly solve the formulated problem, we decompose it into multiple subproblems and preferentially optimize the transmission power and computation frequency of the edge cloud by the quasi-convex bisection and polynomial analysis method, respectively. Based on the precalculated offline transmission power and edge cloud computation frequency, we develop a Q-learning based offloading (QLOF) scheme to minimize the SYLF by optimizing offloading decisions. Finally, the numeral results show that the proposed QLOF scheme effectively reduces the SYLF under different parameters.

With the advent of the 5G era, the demands for features such as low latency and high concurrency are becoming increasingly significant. These sophisticated new network applications and services require huge gaps in network transmission bandwidth, network transmission latency, and user experience, making cloud computing face many technical challenges in terms of applicability. In response to cloud computing's shortcomings, edge computing has come into its own. However, many factors affect task offloading and resource allocation in the edge computing environment, such as the task offload latency, energy consumption, smart device mobility, end-user power, and other issues. This paper proposes a dynamic multi-winner game model based on incomplete information to solve multi-end users' task offloading and edge resource allocation. First, based on the history of end-users storage in edge data centers, a hidden Markov model can predict other end-users' bid prices at time t. Based on these predicted auction prices, the model determines their bids. A dynamic multi-winner game model is used to solve the offload strategy that minimizes latency, energy consumption, cost, and to maximizes end-user satisfaction at the edge data center. Finally, the authors designed a resource allocation algorithm based on different priorities and task types to implement resource allocation in edge data centers. To ensure the prediction model's accuracy, the authors also use the expectation-maximization algorithm to learn the model parameters. Comparative experimental results show that the proposed model can better results in time delay, energy consumption, and cost.

Digital Twin (DT) and mobile edge computing are two promising solutions for achieving latency-sensitive and computing-intensive applications in Industrial Internet of Things (IIoT). However, existing task offloading schemes with DT empowerment are faced with challenges, such as the spatio-temporal heterogeneity of edge server (ES) resources, resource-constrained ESs, and the explosive growth of data in emerging applications. This paper investigates the issues of task offloading and resource allocation under the assistance of DT and multiple ESs parallel collaboration. One novel scheme, abbreviated as Mes-PCORA, is proposed. With comprehensive information within the digital space, the Mes-PCORA scheme dynamically adjusts task allocation ratios across multiple ESs to achieve collaborative task offloading. The offloading request is formulated as a non-convex problem. To make the non-convex problem solvable in polynomial time, the original problem transformed into a bilevel optimization problem. Then, a bilevel iterative optimization approach is proposed. Specifically, the upper-level optimization problem is formulated as a multi-agent Markov Decision Process, and a deep reinforcement learning-based resource allocation algorithm is designed to solve it. Subsequently, for the lower-level optimization problem, it is solved by the interior point method. Numerical results demonstrate that the proposed scheme reduces the average task completion latency by 27.16%–63.44% and decreases the task offloading failure rate by 35.83%–73.95%, compared to state-of-the-art baselines.

To further improve the speed and accuracy of object detection, especially small targets and occluded objects, a novel and efficient detector named YOLO-ACN is presented. The detector model is inspired by the high detection accuracy and speed of YOLOv3, and it is improved by the addition of an attention mechanism, a CIoU (complete intersection over union) loss function, Soft-NMS (non-maximum suppression), and depthwise separable convolution. First, the attention mechanism is introduced in the channel and spatial dimensions in each residual block to focus on small targets. Second, CIoU loss is adopted to achieve accurate bounding box (BBox) regression. Besides, to filter out a more accurate BBox and avoid deleting occluded objects in dense images, the CIoU is applied in the Soft-NMS, and the Gaussian model in the Soft-NMS is employed to suppress the surrounding BBox. Third, to significantly reduce the parameters and improve the detection speed, standard convolution is replaced by depthwise separable convolution, and hard-swish activation function is utilized in deeper layers. On the MS COCO dataset and infrared pedestrian dataset KAIST, the quantitative experimental results show that compared with other state-of-the-art models, the proposed YOLO-ACN has high accuracy and speed in detecting small targets and occluded objects. YOLO-ACN reaches a mAP50 (mean average precision) of 53.8% and an APs (average precision for small objects) of 18.2% at a real-time speed of 22 ms on the MS COCO dataset, and the mAP for a single class on the KAIST dataset even reaches over 80% on an NVIDIA Tesla K40.

In order to solve the consistency and integration about process and process data that is produced in digital product collaborative development, digital collaborative design process model is presented, by detailed analyzing the integration requirement of collaborative design process and data. Based on this model, the CAX/DFX tools which are used in collaborative design are distributed and granule encapsulated by Federal Intelligent Product EnviRonment(FIPER); collaborative design process data produced by CAX/DFX tools is encapsulated based on the logic of Product Data Management (PDM) system’s workflow and is published as Webservice. FIPER is used to realize the collaborative design process flow that uses data Webservice to data exchange between PDM and uses encapsulated CAX/DFX tools to realize the design function. An example which realizes the integration of process design flow of a part is presented at the end of this paper. The collaborative design process flow is used to organize the collaborative process planning and to encapsulate the collaborative design data with Webservice in this example. The realization of this example manifested that the digital collaborative design product model has good practicability and the method of encapsulation for CAX/DFX tools and data Webservice is feasibility.

Federated deep reinforcement learning for task offloading and resource allocation in mobile edge computing-assisted vehicular networks

With the development of artificial intelligence technology and intelligent devices, people show great interest in intelligent applications and services, but it is impossible to complete these compute-intensive AI tasks locally, especially video analysis tasks. Edge computing is regarded as an appropriate solution to these problems. In this paper, we study the multi-user multi-server edge-end collaboration video analytics task offloading problem aiming at minimizing the overall delay for each device to finish its task. Each device chooses whether to execute the task locally or to offload the task to an edge server, and which edge server to select. At the theoretical level, we model the joint problem of task offloading and resource allocation as a mixed integer programming problem. We first determine the optimal resource allocation policy with a given task offloading decision profile. Then, task offloading problem is modeled as a congestion game and propose a decentralized mechanism to achieve a Nash equilibrium. Moreover, experimental results demonstrate that the proposed method is efficient and can significantly and steadily improve the system performance, reducing the overall delay by 33.96% on average, compared with other algorithms.

The purposes are to improve the server deployment capability under Mobile Edge Computing (MEC), reduce the time delay and energy consumption of terminals during task execution, and improve user service quality. After the server deployment problems under traditional edge computing are analyzed and researched, a task resource allocation model based on multi-stage is proposed to solve the communication problem between different supporting devices. This model establishes a combined task resource allocation and task offloading method and optimizes server execution by utilizing the time delay and energy consumption required for task execution and comprehensively considering the restriction processes of task offloading, partition, and transmission. For the MEC process that supports dense networks, a multi-hybrid intelligent algorithm based on energy consumption optimization is proposed. The algorithm converts the original problem into a power allocation problem via a heuristic model. Simultaneously, it determines the appropriate allocation strategy through distributed planning, duality, and upper bound replacement. Results demonstrate that the proposed multi-stage combination-based service deployment optimization model can solve the problem of minimizing the maximum task execution energy consumption combined with task offloading and resource allocation effectively. The algorithm has good performance in handling user fairness and the worst-case task execution energy consumption. The proposed hybrid intelligent algorithm can partition tasks into task offloading sub-problems and resource allocation sub-problems, meeting the user’s task execution needs. A comparison with the latest algorithm also verifies the model’s performance and effectiveness. The above results can provide a theoretical basis and some practical ideas for server deployment and applications under MEC.

With the rapid development and convergence of the mobile Internet and the Internet of Things (IoT), the diverse IoT applications with requirements of ultra-low latency and heterogeneous connectivity have been increasing sharply in recent years. In this work, we consider the problem of diverse task offloading and computation resource allocation in the dynamic and heterogeneous MEC-enable network, wherein heterogeneous characterized by the MEC servers with different computing capacities and multi-type applications with different computation requirements. Firstly, to drive the long-term system-wide effectiveness towards a near-optimum, joint task offloading and computation resource allocation is proposed by invoking the Lyapunov optimization theory, and we analyze the tradeoff between task offloading revenue and latency. Besides, a heuristic task offloading methodology based on a search tree is developed to get the optimal offloading strategy in a heterogeneous environment. Moreover, to improve processing efficiency and reduce unnecessary communication overhead, the Offloading Priority Selection Criterion (OPSC) is designed. Finally, the effectiveness and rationality of the algorithm are verified by experimental simulations.

Accurate object detection in underwater environments is severely challenged by light attenuation, wavelength-dependent color distortion, and scattering-induced turbidity, which create a substantial domain gap between terrestrial and underwater imagery. Conventional Generative Adversarial Network(GAN)-based translation models, such as CycleGAN, attempt to mitigate this gap but often suffer from instability and unrealistic color shifts due to their black-box design. To address these limitations, we propose JTA-GAN (Joint Turbidity–Attenuation GAN), a physics-informed generative framework that explicitly disentangles underwater image formation into scene radiance (J, derived from the physical imaging model), transmission (T), and ambient light (A). By enforcing a simplified physical imaging model within the generator architecture, JTA-GAN enables spatially coherent haze and attenuation synthesis without requiring ground-truth depth supervision. An asymmetric architecture stabilizes reverse mapping, while Learned Perceptual Image Patch Similarity(LPIPS)-based perceptual loss further improves reconstruction realism. Using the JTA-GAN network, we generated 65,153 physically plausible synthetic images for training You Only Look Once(YOLO)-based detectors. Evaluation on the SUIM benchmark demonstrates consistent performance improvements; specifically, YOLOv8s trained with synthetic data from JTA-GAN achieves 17.3% mAP(mean Average Precision), outperforming the land-only baseline (13.2%) and CycleGAN-based augmentation (10.8%). These results confirm that physics-informed generative modeling provides a theoretically grounded and effective solution for underwater domain adaptation under the high-turbidity and low-light conditions represented in the study.

Object detection is a fundamental problem in computer vision, with applications spanning self-driving cars, surveillance systems, medical imaging, robotics, and smart cities. Among the myriad of algorithms developed for this task, the You Only Look Once (YOLO) family stands out for its ability to perform real-time and accurate object detection. This article provides a comprehensive analysis of the YOLO algorithm series, from YOLOv1 to YOLOv8, evaluating them across key performance metrics, including precision, recall, mean Average Precision (mAP), frames per second (FPS), and overall effectiveness. Unlike traditional two-stage detectors such as R-CNN, YOLO formulates object detection as a single regression problem: a single pass over the image simultaneously predicts bounding boxes and class probabilities. This end-to-end design enables YOLO to achieve high speed while maintaining a competitive accuracy-efficiency trade-off. We examine architectural innovations across YOLO versions, including batch normalization, anchor boxes, residual blocks, feature pyramid networks, and attention mechanisms, and discuss their impact on performance. Lightweight models (e.g., YOLOv5-Nano, YOLOv8-Small) are explored with a focus on their suitability for mobile and embedded systems, highlighting YOLO’s adaptability to resource-constrained environments. Challenges such as small object detection, occlusion, and domain-specific tuning are also addressed. This article serves as a practical guide for researchers, developers, and practitioners aiming to leverage YOLO for real-world object detection tasks.

Object detection and recognition is an essential task in computer vision with numerous real-world applications such as surveillance, self-driving cars, and robotics. In recent years, deep learning-based approaches have significantly improved the accuracy and speed of object detection and recognition. The You Only Look Once version 3 (YOLOv3) algorithm is a popular deep learning-based approach that can detect and recognize objects in real-time. The Common Objects in Context (COCO) dataset is a large-scale dataset with over 330,000 labeled images and more than 2.5 million object instances, making it a popular choice for object detection and recognition tasks. In this paper, we propose a deep learning-based approach for real-time object detection and recognition using the YOLOv3 architecture and COCO dataset. We evaluate our approach based on several performance metrics, including mean average precision (mAP), frames per second (FPS), total object detection time, object detection accuracy, false positive rate, number of detected objects, and mean intersection over union (mIoU). Our results show that our approach achieves a mean average precision of 0.76 on the COCO dataset and a real-time performance of 40 frames per second on a single GPU. Additionally, our approach achieves an object detection accuracy of 93.5%, a false positive rate of 6.5%, and a mean intersection over union of 0.65. Our proposed approach shows promising results for real-time object detection and recognition and can be applied to various real-world applications.