Cloud-assisted resource-constrained and widely used outdoor IoT devices deploying highly robust convolutional neural network (CNN) models to provide high-quality visual services have attracted widespread attention from industry and academia. Most existing methods suffer from two limitations: (i) large amount of parameter transmission, and (ii) low robustness in handling data affected by the environment. To this end, this paper proposes a transmission-friendly and robust CNN design method called NonLinear transformation generation Filter with NonLearnable hyperparameters, namely NL2Filter. In NL2Filter, some filters are first designated as seed filters, whose parameters are learnable, that is, updated as the model is trained. Other filters in this layer are generated based on the seed filters and nonlinear transformation function (NLF). The hyperparameters of the NLF in NL2Filter are randomly initialized and remain unchanged, so they can be saved and reproduced using random seed. After the cloud server trains the NL2Filter-CNN model, it only needs to send a small number of learnable parameters and random seed to reproduce the CNN model trained by the cloud server. Compared with sending the complete CNN model, sending only a small number of learnable parameters and random seed can significantly reduce the number of model parameters sent by the cloud server. On the other hand, NL2Filter groups seed filters and then uses NLFs with different hyperparameters to generate diverse filters on demand for each group, thereby improving the model's ability to capture feature diversity without increasing the complexity of the model, thereby improving the model's robustness in processing data affected by the environment. Experimental results on CIFAR-10, CIFAR-10-C, and Icons-50 datasets demonstrate that the proposed NL2Filter outperforms other state-of-the-art methods. Specifically, using the ResNet-101 architecture, on the CIFAR-10-C, NL2Filter's accuracy is about 3.9% higher than that of MonoCNN; On the Icons-50, NL2Filter's accuracy is about 2.7% higher than that of MonoCNN and about 2.3% higher than that of the standard ResNet-101.

This study investigates the trade-off between system stability and offloading cost in collaborative edge computing. While collaborative offloading among multiple edge servers enhances resource utilization, existing methods often overlook the role of queue stability in overall system performance. To address this, a multi-hop data transmission model is developed, along with a cost model that captures both energy consumption and delay. A time-varying queue model is then introduced to maintain system stability. Based on Lyapunov optimization, a dynamic offloading algorithm (LDSO) is proposed to minimize offloading cost while ensuring long-term stability. Theoretical analysis and experimental results verify that the proposed LDSO achieves significant improvements in both cost efficiency and system stability compared to the state-of-the-art.

To address the challenges of small target features being less prominent, susceptibility to background interference, and sample imbalance in road traffic sign detection, which leads to insufficient model detection accuracy, as well as the high complexity of current object detection models that struggle to operate efficiently on resource-constrained edge devices, we propose a traffic sign detection model based on GCA-YOLO. By adding small target detection layers and removing large target detection layers, the model enhances its small target detection capabilities and reduces its parameter size. The introduction of the T-BiFPN (Tiny-BiFPN) structure improves multi-scale feature fusion, while the C2f-CP module increases computational efficiency on edge devices. The GCA (Global Coordinate Attention) mechanism enhances feature extraction, and the Focaler-CIoU loss function enables the model to focus more on difficult samples and accelerate the convergence of bounding boxes. Experimental results show the superiorities of the proposed GCA-YOLO that compared to YOLOv8n, GCA-YOLO improves precision, recall, mAP@50, and mAP@50:95 by 8.6%, 6.1%, 8.7%, and 6.2%, respectively, while reducing the model's parameter count and size by 38.57% and 33.21%, respectively.

Using pruning techniques to prune redundant weights in large language models (LLMs) for model size reduction, enabling deployment on devices to deliver high-quality services, has garnered significant attention from industry and academia. However, most existing pruning methods suffer from two major problems: (1) they rely on operations with high computational complexity, such as Hessian matrix calculation, and (2) high pruning rate leads to a significant decrease in model accuracy. To this end, this paper proposes a low resource requirement and low accuracy loss post-training pruning approach, namely the compensated post-training pruning method (CPP) for on-device LLM services. First, CPP employs singular value decomposition on weight matrices, sorting the decomposed singular values in descending order. Based on the pruning ratio, it retains the principal eigenvector corresponding to the larger singular values. To maintain consistent output feature distributions, CPP applies orthogonal transformations to input data and weight matrices, leveraging the principle of matrix orthogonality invariance. Compared with pruning approaches based on the Hessian matrix, CPP does not need to iteratively calculate the second derivative, thereby avoiding a high computational overhead. Second, the CPP incorporates bias compensation to use valuable information in pruned weights to improve the accuracy of the model. It constructs mapping relationships between input features and pruned weights through tensor decomposition techniques to generate bias compensation terms. These terms fine-tune the output of each layer, reducing pruning-induced errors from ratio-based pruning, and consequently improving model accuracy. Finally, CPP is validated through comprehensive experiments on nine benchmark datasets (e.g., WikiText-2 and PIQA) using large language models (i.e., LLaMA and OPT). Specifically, compared with the FLAP, CPP demonstrates 6.68% reduction in average pruning time. At pruning ratios of 10% and 20%, CPP achieves 2. 14% and 1.45% reductions in perplexity, respectively, along with 1.22% to 3.43% improvement in zeroshot inference accuracy.

Task offloading can meet users’ demands for the latency and energy consumption by offloading tasks from resource-constrained Internet of Things devices to relatively resource-rich edge servers. Traditional task offloading usually makes use of fixed base stations or servers as edge servers. This would lead to limited range of services and increased costs due to large-scale deployment of edge servers. Therefore, deploying unmanned aerial vehicles (UAVs) as mobile edge servers for task offloading in complex terrains (e.g., forest, desert, etc.) is a worthwhile research problem. To this end, this article proposes a UAV-assisted task offloading mechanism. The mechanism aims to minimize the weighted sum of latency and energy consumption through jointly optimizing resource allocation, offloading decision, and UAV trajectory. We first transform the nonconvex optimization problem into convex optimization subproblems to obtain the optimal resource allocation. Second, we use an improved particle swarm optimization algorithm to find the optimal offloading decision. Finally, we present the deep determination policy gradient algorithm to optimize the UAV trajectory which is a kind of deep reinforcement learning algorithm. Through simulation experiments, we show that the proposed mechanism can efficiently reduce the weighted sum of latency and energy consumption.

Vehicle Edge Computing (VEC) can effectively improve the efficiency of vehicle task calculation and offloading by integrating edge computing and the Internet of Vehicles. However, current research in VEC mostly focuses on device collaboration within single-hop or two-hop ranges, limiting the additional performance gain and load balancing provided by multi-hop device collaboration. In this study, an Edge Digital Twin assisted Multi-hop Task Offloading Scheme (EDT_MTOS) is proposed to enhance the execution efficiency of vehicle tasks by establishing an edge digital twin layer for virtual mapping of vehicles and edge servers. Firstly, the multi-hop task offloading problem is transformed into a cost optimization problem related to delay and energy consumption under the constraint of load balancing. Secondly, a dynamic collaboration knowledge graph based on digital twin knowledge mapping is introduced to select collaborative device sets for the upload and return links within the multi-hop range. Then, a value iteration algorithm based on the maximum link quality is proposed to realize the dynamic collaboration knowledge graph. Furthermore, a task offloading solution algorithm is proposed based on Dynamic collaboration Knowledge Graph and Double Deep Q-Network (DKG_DDQN). Finally, the simulation results demonstrate that the proposed offloading algorithm can reduce vehicle task processing costs by 33.77% and 26.53% in an idle scenario, and by 27.60% and 26.21% in a busy scenario, compared to state-of-the-art collaborative algorithms such as COOR and DRL-COMV, respectively.

This paper concentrates on the synergetic effect of the hybrid multicast/unicast/device-to-device (D2D) transmission for downlink cell-free massive multiple-input multiple-output (MIMO) Internet-of-Things (IoT) systems. By leveraging on the acquired imperfect channel state information (CSI) both for multicast, unicast, D2D links, particularly, we first derive the closed-form solutions for the multicast, unicast, and D2D transmission links, respectively. After that, based on the practical power consumption model, the achievable sum energy efficiency (EE) analysis is conducted as well by exploiting the achievable sum rate. At last, extensive simulation results are provided to validate the performance of the proposed framework. The obtained results are given to provide promising preliminary insights on the potential of deploying multicast/unicast/D2D in the cell-free massive MIMO topology. It is revealed by the above analysis that some guiding rules of the practical deployment of future. It is noteworthy that when we select the 5, 6, and 8 bits, we can achieve the maximum sum EE, the tradeoff from the total power consumption to the sum rate, and the maximum sum rate, respectively.

As one of the most critical tasks currently, it is essential to excavate the status update performance. In this paper, we combine access control and pilot design to minimize average age of information (AoI) in cell-free (CF) massive multiple-input multiple-output (MIMO) system with grant-free (GF) random access, where the positions of both the access points (APs) and machine-type communication devices (MTCDs) are geographically distributed as independent Poisson point processes (PPPs). Based on the establish two-disk computation model, we frist formulate the access successful probability by leveraging the stochastic geometry for GF transmission. Then, the closed-form expression of the average AoI is approximately derived. The derived results enable us precisely quantify the impact of network parameters on the system-level performance. To minimize the average AoI, a joint access control and pilot design scheme is proposed. Finally, simulation results are provided to furnish invaluable insight into system performance and certificate the validity of the proposed algorithm.

Aiming at solving the problems of high latency, data transmission bandwidth limitation and privacy security faced by traditional object detection methods in the Internet of Things, a cloud-edge collaborative system for object detection based on KubeEdge is proposed. It takes advantage of cloud-edge collaboration technology and edge computing platform to perform tasks on edge devices to achieve faster response times. Firstly, through the deployment of KubeEdge edge computing platform, the cloud edge collaboration function is realized. Then, the object detection model is trained on the cloud server, and the trained model is deployed on the edge device to perform the model inference task. Finally, the edge device transmits the inference results to a cloud server, which stores the results for further analysis. The system has significant advantages in realizing low delay calculation, collaborative assurance, and privacy protection, etc. Taking mask detection as an example, it validated the practicality and reliability of the system, which provides strong support for the application of cloud-edge collaborative technology in the field of object detection and holds significant importance in meeting the growing demands of edge computing.

Device to Device(D2D) caching employs direct communication technology to facilitate content sharing among adjacent users, effectively harnessing the storage resource of numerous terminal devices. Integrating user request diversity, resource limitation and content recommendation on user request probability, a joint optimization strategy for recommendation and collaborative cache is proposed to maximize the cache hit rate in D2D network. Firstly, the concept of regional user request probability is introduced, and the impact of content recommendation on user request probability is analyzed. And then, an initial regional user request model based on multi-level round-by-round iteration heuristic and long short-term memory algorithm is proposed to predict content popularity within collaborative clusters. Finally, combining recommendation and collaboration, the regional user request model is reconstructed, and an optimization algorithm is proposed to address the problem of content caching in D2D networks. Compared with other studies under different recommendation acceptance probabilities and cache capacities, the results show that the proposed algorithm improves the local cache hit rate by at least 24% and 9%, respectively.

When offloading computing tasks of sensory data to the edge network, it is necessary to consider whether the idle resources such as CPU frequency and memory, meet the task processing requirements. However, even if edge collaboration is used to improve offloading performance, most studies assume homogeneity in hardware configuration across all edge servers, discarding the impact of the differentiated resource allocation among heterogeneous edge servers. Therefore, resource allocation and offloading decisions in a collaborative heterogeneous edge network are comprehensively considered in this study. Firstly, the offloading problem of heterogeneous edge servers is expressed as a joint optimization problem associated with delay and energy consumption constrained by CPU frequency and storage resources. Secondly, dynamic collaboration clusters are constructed based on distance, position and workload correlation to identify distinct collaboration regions and balance the load within edge servers. And then, a distributed alternating direction multiplier method (ADMM) based on constraint projection and variable splitting is proposed to solve the optimization problem. Additionally, a cooperative path selection algorithm, which takes into account length and throughput of return paths, is proposed to alleviate network congestion and minimize energy consumption loss. Finally, the proposed algorithm for Collaborative offloading and resource Configuration (CooCo) is demonstrated to be effective and rapidly converging based on a real dataset from Shanghai Telecom. The simulation results also show that compared to the DRAOA, no-cooperation, single-hop, and other state-of-the-art collaborative algorithm, CooCo can significantly reduce the sum of the system costs by 26%, 35%,11% and 8%, respectively.

It has become mainstream for mobile devices to offload latency-sensitive applications to edge servers for execution to meet low-latency requirements. However, the existing related studies lack the consideration of application hit ratio, which makes them unable to meet the increasingly complex offloading of multi-applications including multi-tasks. To this end, this paper proposes a Multi-task offloading and Service placement optimization (MSO) method with the goal of maximizing the application hit ratio to provide high-quality service. The proposed MSO is constructed with Improved Multi-Agent Q-Learning (IMAQL) and load-balancing algorithms. IMAQL aims to learn an optimal service placement policy by using Q-learning techniques. Next, the load-balancing algorithm is designed to offload tasks according to the service placement policy. To verify the effectiveness of the MSO method, we conduct extensive experiments on a publicly available dataset. The experimental results show that the proposed method can improve the application hit ratio by appropriately 2.6% to 9% compared with other methods.

Mobile Edge Computing (MEC) can provide the user with computation resources close to the user. In order to cope with stringent requirements of applications on latency and reduce energy consumption, MEC enables to offload highly computation demanding tasks to the edge servers. In this paper, we present a brief overview of user-oriented computation offloading in MEC. We first describe the definition of computation offloading decision, quality of service metric and research scenario. Then we review the existing user-oriented computation offloading approaches from the single user scenario and multiple user scenarios. Finally, we conclude the paper.

This article studies the capacity of edge caching systems. Capacity is analyzed from the perspective of node mobility, caching, content popularity, etc., neglecting the influence of edge node cooperation on system performance. However, cooperation among edge nodes has been shown to substantially improve the system performance at the expense of a cooperation cost. Therefore, we attempt to maximize the capacity of mobile edge computing (MEC) subject to a budget constraint on the cooperation cost. We first transform the capacity maximization problem into a transmission distance minimization problem; then, we explore the average transmission distance of each source-to-destination pair under the assumption that the locations of the edge nodes follow a Poisson point process (PPP). We find that both the average transmission distance and the cooperation cost are associated with a key parameter, i.e., the number of content copies. We use the Lagrangian multiplier method to calculate the optimal copy number and propose a file allocation algorithm to store these copies. Finally, we analyze the influence of various parameters on the system capacity. The numerical results verify the efficiency of our solution compared with classic works.

Node buffer size has a big influence on performance of Mobile Opportunistic Networks (MONs). This is mainly because each node should temporarily cache packets to deal with the intermittently connected links. In this paper, we study fundamental bounds on node buffer size below which the network system can not achieve the expected performance. Given the condition that each link has the same probability p to be active, and q to be inactive during each time slot, there exits a critical value pcfrom a percolation perspective. If p > pc, the network is in the supercritical case, there is an achievable upper bound on the buffer size of nodes, independent of the inactive probability q. When pc, the network is in the subcritical case, and there exists a closed-form solution for buffer occupation, which is independent of the size of the network.