关键词： Quality of service   Servers   Digital twins   Consumer electronics   Real-time systems   Optimization   Task analysis   Mobile edge computing   consumer electronics   diversified top-k service composition   digital twin  

摘要： Mobile Edge Computing (MEC) stands as an indispensable technology in the facilitation of 5G networks, enabling the deployment of widely-used services on edge servers situated in close proximity to consumer electronics. Within the MEC framework, a central role is attributed to edge service composition (ESC), pivotal in bolstering functionality and ameliorating user experiences. Presently, prevailing methods for ESC predominantly center on the prioritization of Quality of Service (QoS) optimization, presenting a solitary optimal composite service for consumer electronics invocation. Regrettably, this approach sidelines the significance of solution diversity within composite services, potentially resulting in service overload and the suboptimal utilization of edge resources. To surmount these challenges, this study integrates digital twin (DT) technology and diversified search mechanisms into the MEC domain, offering an innovative diversified top- ${k}$ service composition methodology known as DSC-DT. By harnessing the capabilities of DT technology, DSC-DT enables the emulation and assessment of diverse composite service solutions within a virtual space. Specifically, the proposed methodology models the procedure of service composition within a DT environment as an issue of subgraph isomorphism. This is succeeded by the configuration of the diversification process as an independent set predicament within an undirected graph, efficiently resolved through a greedy algorithmic paradigm. It is noteworthy that DSC-DT accommodates a gamut of query types, including normal queries, constraint queries, and optimal queries. The efficacy and efficiency of the proposed approach are corroborated through comprehensive experiments conducted upon authentic datasets.

关键词： Heterogeneous task replication   mobile edge computing   convex relaxation   interior point method   distributed ADMM-based framework  

摘要： Mobile edge computing (MEC) is expected to support real-time services at wireless networks, where task replication is applied to guarantee job completion within a strict deadline through replicating multiple copies to different edge servers. Most of previous works focused on guaranteeing the reliability of individual task in MEC-based networks with the assumption of homogeneous task execution distribution. Further, these algorithms cannot suit dynamic network scales, due to overhigh communication or retraining overhead. Therefore, this article formulates the problem of heterogeneous task replication in a finer level by modeling outage probability of individual replication, where the decisions of all tasks are jointly optimized within the constraints of both mobile users and MEC servers for minimizing job outage probability. To adapt to varying network scales, we develop centralized and distributed algorithms, respectively. The centralized algorithm is developed based on Interior Point Method, which obtains the optimal solution of relaxed model and then approximates to the solution of original problem. Further, the distributed algorithm decomposes the HTR into multiple subproblems and parallelly compute each local solution based on Distributed ADMM. Finally, we build a simulation model and conduct comprehensive results, which demonstrates that the proposed algorithms can achieve high-accuracy solution with fast convergence.

关键词： Secure video offloading   Industrial Internet of Things   resource orchestration   mobile edge computing   multi-cell federated deep reinforcement learning  

摘要： Wireless video offloading in mobile-edge-computing (MEC)-enabled Industrial Internet of Things imposes a risk of exposing users' private data to eavesdroppers. It is difficult for existing secure video offloading schemes to simultaneously guarantee security, reduce latency and energy consumption in privacy-sensitive multi-cell scenarios where users are unwilling to offload data to other cells. In this paper, a secure video offloading scheme based on multi-cell federated deep reinforcement learning (DRL) is proposed to facilitate a secure, real-time and efficient MEC network by efficient orchestration of limited resources. We formulate a collaborative optimization problem of video frame resolution and resources to minimize latency and energy consumption while maximizing the security rate subject to analytic accuracy and limited resources. To solve the formulated NP-hard problem, a multi-cell federated DRL algorithm based on the frameworks of multi-cell horizontal federated learning (FL) and hierarchical reward function-based twin delayed deep deterministic policy gradient (TD3) is proposed. First of all, hierarchical reward function-based TD3 is employed to solve the collaborative optimization NP-hard problem formulated for each single cell, where the optimal solution can be efficiently approached by the agent under the guidance of the innovatively designed hierarchical reward function. Then, multi-cell horizontal FL is applied on TD3 to obtain a model with higher model quality by averagely aggregating multiple individual TD3 models. Simulation results reveal that the proposed algorithm outperforms comparison algorithms in terms of utility, cost, latency, energy consumption and security rate.

关键词： Internet of Vehicles   mobile edge computing   object detection   image compression  

摘要： The prevalence of intelligent transportation systems in alleviating traffic congestion and reducing the number of traffic accidents has risen in recent years owing to the rapid advancement of information and communication technology (ICT). Nevertheless, the increase in Internet of Vehicles (IoV) users has led to massive data transmission, resulting in significant delays and network instability during vehicle operation due to limited bandwidth resources. This poses serious security risks to the traffic system and endangers the safety of IoV users. To alleviate the computational load on the core network and provide more timely, effective, and secure data services to proximate users, this paper proposes the deployment of edge servers utilizing edge computing technologies. The massive image data of users are processed using an image compression algorithm, revealing a positive correlation between the compression quality factor and the image's spatial occupancy. A performance analysis model for the ADHOC MAC (ADHOC Medium Access Control) protocol is established, elucidating a positive correlation between the frame length and the number of service users, and a negative correlation between the service user rate and the compression quality factor. The optimal service user rate, within the constraints of compression that does not compromise detection accuracy, is determined by using the target detection result as a criterion for effective compression. The simulation results demonstrate that the proposed scheme satisfies the object detection accuracy requirements in the IoV context. It enables the number of successfully connected users to approach the total user count, and increases the service rate by up to 34%, thereby enhancing driving safety, stability, and efficiency.

关键词： Satellites   Task analysis   Low earth orbit satellites   Space-air-ground integrated networks   Aircraft   Servers   Delays   Mobile edge computing   space-air-ground integrated network   collaborative offloading   online learning   6G system  

摘要： In 6G systems, space-air-ground integrated network (SAGIN), relying on space/aerial communications to complement terrestrial networks, is developed to achieve worldwide connectivity and multi-service access especially in remote areas. However, the heavy workload of the resource-limited satellites may raise an important issue about reducing service coverage and quality. In this article, we propose a collaborative edge computing framework for SAGIN-aided 6G system, in which the LEO satellites are considered as both "servers" and "users". Excepting provide services for ground users/devices, the LEO satellites is able to offload the tasks to nearby aircrafts via one-hop link or offload them to the cloud server along multi-hop satellite path. To minimize the long-term task completion delay of LEO satellites, a stochastic optimization problem is formulated by considering the variation of the space/aerial environment. The Lyapunov-based optimization method is developed to solve the problem and the delayed online learning technique is adapted to predict dynamic task arrival and queue length of satellites and aircrafts. Numerical results confirm the effectiveness of the proposed collaborative offloading scheme for reducing tasks completion delay of LEO satellites while guaranteeing computation efficiency.

关键词： Mobile edge computing   base station selection   resource allocation   double auction   revenue maximization  

摘要： In mobile edge computing (MEC), task offloading and resource allocation are two important issues that are inextricably linked. However, existing studies have either ignored the mobility of mobile users (MUs) during task offloading or the allocation of profits between two parties during the allocation of limited resources (i.e., the resource competition). In this paper, we jointly optimized these two problems. First, to reduce the task offloading delay and the service interruption due to movement, we develop a mobility-aware model, based on which we propose the MWBS algorithm to select the appropriate offloading base station (BS) for MUs. Second, considering the resource competition and the delay constraint of the task, we develop a double auction model and then propose the DARA algorithm, which efficiently allocates the BS resources and maximizes the total system revenue (i.e., social welfare) through a multi-session auction. Finally, we combine MWBS and DARA to propose the BS resource allocation algorithm called MD-BSRA in mobile scenarios. Simulation results show that MD-BSRA can effectively improve task offload success rate, total system revenue and resource utilization while reducing offload delay and service interruption.

关键词： Resource allocation   offloading decision   mobile edge computing   unmanned aerial vehicle   D2D  

摘要： In this paper, we consider the resource allocation and offloading decisions of device-to-device (D2D) cooperative UAV-assisted mobile edge computing (MEC) system, where the device with task request is served by unmanned aerial vehicle (UAV) equipped with MEC server and D2D device with idle resources. On the one hand, to ensure the fairness of time-delay sensitive devices, when UAV computing resources are relatively sufficient, an optimization model is established to minimize the maximum delay of device computing tasks. The original non-convex objective problem is decomposed into two subproblems, and the suboptimal solution of the optimization problem is obtained by alternate iteration of two subproblems. On the other hand, when the device only needs to complete the task within a tolerable delay, we consider the offloading priorities of task to minimize UAV computing resources. Then we build the model of joint offloading decision and power allocation optimization. Through theoretical analysis based on KKT conditions, we elicit the relationship between the amount of computing task data and the optimal resource allocation. The simulation results show that the D2D cooperation scheme proposed in this paper is effective in reducing the completion delay of computing tasks and saving UAV computing resources.

关键词： Quantum healthcare model   Smart IoT   Mobile edge computing   5G networks   Edge-IoT ecosystem  

摘要： Edge computing (EC) aided Internet of Things (IoT) based applications require real-time processing as well as high-volume data-intensive services as 5G networks evolve. It is difficult to fit IoT services into available edge nodes (ENs) while maintaining performance measures on quality of service (QoS) because of the heterogeneity, restricted resources, and changing resource demand of IoT applications. This study aims to examine a quantum healthcare model that is built on mobile edge computing networks linked with smart IoT. Serverless computing with edge computing may handle quick or small-scale tasks effectively at edge devices, lowering latency. Moreover, serverless edge computing now faces significant hurdles from security and processing performance. In order to provide trustworthy and secure edge serverless services, it is possible to use blockchain technology to boost processing speed and quantum computing to enhance security. In terms of decreasing dependency on IoT cloud analytics or storage facilities, the test results were promising. Lastly, we cover the many aspects that impact the viability of integrating an edge-IoT ecosystem, such as design, capability requirements, functional challenges, and selection criteria.

关键词： Mobile edge computing   Non-orthogonal multiple access   Reconfigurable intelligent surface   Deep reinforcement learning   Twin delayed deep deterministic policy   gradient  

摘要： Reconfigurable intelligent surface (RIS) is expected to enhance task offloading performance in non-line-of -sight mobile edge computing (MEC) scenarios. This paper aims at reducing the long-term energy consumption for task offloading in a MEC system assisted by double RISs while considering non-orthogonal multiple access (NOMA). Due to the non-convexity of the formulated problem, we propose an action-constrained deep reinforcement learning (DRL) framework based on twin delayed deep deterministic policy gradient (TD3) algorithm that includes inner and outer optimization processes, which greatly reduces the action space size of the DRL agent, making it easy to implement and achieve fast convergence. During the inner optimization phase, by utilizing theoretical derivations, we propose a low-complexity method to optimally derive the transmit power, the local computing frequency, and the computation resource allocation in the base station. In the outer optimization phase, building on the solution derived from the inner optimization, we further use the TD3 algorithm to jointly determine the phase shifting of RIS elements, the offloading ratio, and the transmission time for each time slot. Experiment results demonstrate that the proposed algorithm achieves rapid convergence performance. In comparison to single RIS assisted offloading, the double RISs assisted offloading scheme proposed in this paper can reduce energy consumption by 42.8% on average.

关键词： Mobile edge computing  

摘要： As digital technology advances, the proliferation of connected devices poses significant challenges and opportunities in mobile crowdsourcing and edge computing. This narrative review focuses on the need for privacy protection in these fields, emphasizing the increasing importance of data security in a data-driven world. Through an analysis of contemporary academic literature, this review provides an understanding of the current trends and privacy concerns in mobile crowdsourcing and edge computing. We present insights and highlight advancements in privacy-preserving techniques, addressing identity, data, and location privacy. This review also discusses the potential directions that can be useful resources for researchers, industry professionals, and policymakers. © 2024, CC BY.