关键词： Internet of vehicles   Mobile edge computing   Offloading strategy   Resource allocation   Deep reinforcement learning  

摘要： To meet the stringent requirements for real-time performance of computing tasks on the Internet of Vehicles (IoV) scenario, the Mobile Edge Computing (MEC) technique is introduced to combine edge servers with vehicles that have storage and computing resources, thereby reducing latency. However, successfully accessing the channel and completing the offloading computation within the specified time remains a big challenge in scenarios with multitasking vehicles and multibase stations equipped with multiple channels. To address this problem, we propose the Multi-Agent Deep Deterministic Policy Gradient-based Offloading and Resource Allocation (MADDPG-RA) algorithm. First, a sub-optimal offloading strategy is determined using the MADDPG algorithm to minimize the sum of system latency and energy consumption. Specifically, this strategy determines whether each vehicle should perform local or offload computation, which MEC server to choose, and which channel to access if offloaded. Based on the above, a closed form expression for the optimal computational resource allocation for MEC is derived using Lagrange multipliers. The simulation results demonstrate that the proposed MADDPG-RA algorithm can effectively reduce the total system latency and energy consumption compared to the existing algorithms.

关键词： Autonomous driving   Task offloading   Mobile edge computing   INTERNET  

摘要： With the increasing complexity of autonomous driving tasks, the computational demands on single vehicular computing units have escalated, more and more tasks need to be offloaded to the edge. These tasks vary in latency sensitivity: real-time tasks, critical for passenger safety, require strict deadline adherence, whereas the latency of standard tasks mainly affects the user experience and has more flexible constraints. Addressing the challenge of selecting suitable edge computing nodes to enhance the offloading success rate of real-time tasks amidst a vast and heterogeneous cluster becomes crucial. This paper introduces the adaptive priority-based hierarchical task offloading (APHTO) algorithm, which optimizes task offloading strategies by accounting for the diverse latency constraints of different task types. Experiments demonstrate that under optimal performance conditions, APHTO significantly outperforms existing algorithms such as Min-Min, Max-Min, CUS, and FMS in reducing task latency by 20.31%, increasing offloading success rates by 35.83%, and improving resource utilization by 30.21%, marking a substantial advancement in task offloading strategies for autonomous driving integrated with MEC.

关键词： Mobile edge computing   Non-orthogonal multiple access   Privacy outage probability   Resource allocation   Partial offload   NONORTHOGONAL MULTIPLE-ACCESS   EFFICIENCY   NETWORKS   COMPUTATION  

摘要： Mobile edge computing can use the wireless access network to provide the services required by telecom users and cloud computing functions nearby, thereby creating a service environment with high performance, low latency and high bandwidth. Computational task offloading is a core issue in mobile edge computing. In order to improve the security when computing tasks are partially offloaded in non-orthogonal multiple access-based mobile edge computing systems, this paper studies the physical layer security of MEC networks considering the presence of eavesdroppers. We employ the secrecy outage probability to measure the secrecy performance of computation offloading. Under the premise of comprehensively considering the joint constraints of transmit power, local task calculation and confidentiality outage probability, we introduce the energy consumption weight factor to balance the transmission energy consumption and calculation energy consumption, and finally achieve the minimum weighted sum of system energy consumption. On the premise of satisfying two user priorities, in order to reduce system overhead, we design a joint task offloading and resource allocation mechanism. The mechanism uses binary search-based iterative optimization algorithm to seek the optimal solution after problem transformation, and obtain optimal task offloading and power allocation. A large number of simulation results show that the algorithm proposed in this paper can effectively reduce system energy consumption.

关键词： Mobile edge computing   Deep learning   Light-weight computing   Image semantic segmentation   INDEX NDWI  

摘要： In the field of remote sensing image interpretation, automatically extracting water body information from high-resolution images is a key task. However, facing the complex multi-scale features in high-resolution remote sensing images, traditional methods and basic deep convolutional neural networks are difficult to effectively capture the global spatial relationship of the target objects, resulting in incomplete, rough shape and blurred edges of the extracted water body information. Meanwhile, massive image data processing usually leads to computational resource overload and inefficiency. Fortunately, the local data processing capability of edge computing combined with the powerful computational resources of cloud centres can provide timely and efficient computation and storage for high-resolution remote sensing image segmentation. In this regard, this paper proposes PMNet, a lightweight deep learning network for edge-cloud collaboration, which utilises a pipelined multi-step aggregation method to capture image information at different scales and understand the relationships between remote pixels through horizontal and vertical spatial dimensions. Also, it adopts a combination of multiple decoding branches in the decoding stage instead of the traditional single decoding branch. The accuracy of the results is improved while reducing the consumption of system resources. The model obtained F1-score of 90.22 and 88.57 on Landsat-8 and GID remote sensing image datasets with low model complexity, which is better than other semantic segmentation models, highlighting the potential of mobile edge computing in processing massive high-resolution remote sensing image data.

关键词： Authentication   Threshold signature   Authorization update   Mobile edge computing  

摘要： The widespread adoption of fifth-generation mobile networks has spurred the rapid advancement of mobile edge computing (MEC). By decentralizing computing and storage resources to the network edge, MEC significantly enhances real-time data access services and enables efficient processing of large-scale dynamic data on resource-limited devices. However, MEC faces considerable security challenges, particularly in cross-domain service environments, where every device poses a potential security threat. To address this issue, this paper proposes a secure cross-domain authentication scheme based on a threshold signature tailored to MEC's multi-subdomain nature. The proposed scheme employs a (t,n) threshold mechanism to bolster system resilience and security, catering to large-scale, dynamic, and decentralized MEC scenarios. Additionally, the proposed scheme features an efficient authorization update function that facilitates the revocation of malicious nodes. Security analysis confirmed that the proposed scheme satisfies unforgeability, collusion resistance, non-repudiation and forward security. Theoretical evaluation and experimental simulation verify the effectiveness and feasibility of the proposed scheme. Compared with existing schemes, the proposed scheme has higher computational performance while implementing secure authorization updates.

关键词： Mobile edge computing   unmanned aerial vehicles   service caching and task offloading   online algorithm   machine learning  

摘要： Unmanned Aerial Vehicles (UAVs) have gained increasing attention by both academic and industrial communities, due to their flexible deployment and efficient line-of-sight communication. Recently, UAVs equipped with base stations have been envisioned as a key technology to provide 5G network services for mobile users. In this article, we provide timely services on the data streams of mobile users in a UAV-aided Mobile Edge Computing (MEC) network, in which each UAV is equipped with a 5G small-cell base station for communication and data processing. Specifically, we first formulate a flow-time minimization problem by jointly caching services and offloading tasks of mobile users to the UAV-aided MEC with the aim to minimize the flow time, where the flow time of a user request is referred to the time duration from the request issuing time point to its completion point, subject to resource and energy capacity on each UAV. We then propose a spatial-temporal learning optimization framework. We also devise an online algorithm with a competitive ratio for the problem based upon the framework, by leveraging the round-robin scheduling and dual fitting techniques. Finally, we evaluate the performance of the proposed algorithms through experimental simulation. The simulation results demonstrate that the proposed algorithms outperform their comparison counterparts, by reducing the flow time no less than 19% on average.

关键词： Mobile edge computing   integrity   data right confirmation   consensus   merkle tree  

关键词： Artificial intelligence   Class estimation   Data imbalance   Distributed machine learning   Federated learning   Edge devices   Mobile edge computing   Privacy preserving algorithms  

摘要： In Mobile Edge Computing, the framework of federated learning can enable collaborative learning models across edge nodes, without necessitating the direct exchange of data from edge nodes. It addresses significant challenges encompassing access rights, privacy, security, and the utilization of heterogeneous data sources over mobile edge computing. Edge devices generate and gather data, across the network, in non-IID (independent and identically distributed) manner leading to potential variations in the number of data samples among these edge networks. A method is proposed to work in federated learning under edge computing setting, which involves AI techniques such as data augmentation and class estimation and balancing during training process with minimized computational overhead. This is accomplished through the implementation of data augmentation techniques to refine data distribution. Additionally, we leveraged class estimation and employed linear regression for client-side model training. This strategic approach yields a reduction in computational costs. To validate the effectiveness of the proposed approach, it is applied to two distinct datasets. One dataset pertains to image data (FashionMNIST), while the other comprises numerical and textual data concerning stocks for predictive analysis of stock values. This approach demonstrates commendable performance across both dataset types and approaching more than 92% of accuracy in the paradigm of federated learning.

关键词： computing resource allocation   mobile edge computing   satellite-terrestrial networks   task offloading decision  

摘要： Mobile edge computing(MEC)-enabled satellite-terrestrial networks(STNs)can provide Internet of Things(IoT)devices with global computing services.Sometimes,the network state information is uncertain or unknown.To deal with this situation,we investigate online learning-based offloading decision and resource allocation in MEC-enabled STNs in this paper.The problem of minimizing the average sum task completion delay of all IoT devices over all time periods is formulated.We decompose this optimization problem into a task offloading decision problem and a computing resource allocation problem.A joint optimization scheme of offloading decision and resource allocation is then proposed,which consists of a task offloading decision algorithm based on the devices cooperation aided upper confidence bound(UCB)algorithm and a computing resource allocation algorithm based on the Lagrange multiplier method.Simulation results validate that the proposed scheme performs better than other baseline schemes.

关键词： Mobile edge computing   STAR-RISs   Multi-cell communication systems   Secure communications   MADRL   DDPG   BEAMFORMING DESIGN   INTELLIGENT   TRANSMISSION   OPTIMIZATION   RESOURCE  

摘要： The poor propagation environment between user equipments (UEs) and mobile edge computing (MEC) servers may lead to UEs' high energy consumptions and task delay. Reconfigurable intelligent surface (RIS) can significantly improve the quality of wireless propagation environment by intelligently adjusting the phase-shifts and amplitudes of its reflecting elements. However, the passive or active RISs can only reflect signals, i.e., the transmitters and receivers must be located on the same side of RISs. Simultaneous transmitting and reflecting (STAR) RIS, which can transmit and reflect incident signals simultaneously, has been recognized a promising solution to solve the above problem. For STAR-RIS-aided multi-cell secure communications MEC systems, we first formulate an optimization problem to minimize the sum of weighted delay and energy consumptions of all UEs, by jointly optimizing the phase-shifts and amplitudes of STAR-RISs and system spectrum and computation resource allocations. Second, under the practical coupled phase-shift model of STAR-RIS, we develop a multi-agent deep reinforcement learning (MADRL) based scheme, where the access point (AP) in each cell acts an agent to decide phase-shifts, amplitudes, computation task offloading proportions, etc, for the STAR-RIS and UEs within its coverage based on deep deterministic policy gradient (DDPG), by taking into account interference among multiple cells and possible information leakage of UEs. Finally, we verify and evaluate the performance of the proposed scheme through extensive simulations. The simulation results show that our proposed MADRL-based scheme outperforms the existing benchmarks by employing multiple agents and considering co-channel interference among cells. Moreover, simulation results also show that the performance of our developed scheme can indeed be improved by using STAR-RISs.