关键词： Mobile edge computing   Computing offloading   Multi-objective algorithm   Immune algorithm  

摘要： Mobile edge computing (MEC) technology enables mobile devices in communication network systems offloading tasks to edge servers, effectively reducing response time and energy consumption. However, task offloading decisions become a significant difficulty when the system's mobile and service device count rises. In this paper, the problem of response time and energy consumption of the system is modeled as a multi-objective optimization problem, and we design an improved evolutionary algorithm based on immune algorithm, which can effectively obtain a set of solutions between response time and energy consumption. The simulation results show that our scheme can meet the response time requirements and obtain a lower energy consumption strategy when compared to the offloading scheme in the existing literature.

关键词： Task analysis   NOMA   Roads   Propagation losses   Decoding   Servers   Probability density function   Mobile edge computing   non-orthogonal multiple access   vehicular networks   task offloading latency   speed  

摘要： In vehicular networks, task offloading performance for mobile edge computing (MEC) is highly coupled with the mobility of vehicle terminal (VT), as the VT speed and arrival rate determine the number of VTs and their sojourn time on each road segment. Generally, the communication and computation resources of the MEC server are limited, and thus more VTs leads to less resources for each VT. In this paper, we develop an analytical non-orthogonal multiple access (NOMA) assisted MEC model to characterize VT mobility and analyze the impact of VT mobility on task offloading performance. In this model, each VT with random speed starts its offloading at any time and hopefully completes the task offloading before leaving the current road segment. The expressions of average latency and task loss probability are derived and validated by simulation results. Furthermore, the impact of VT mobility, especially the average VT speed, on latency performances is analyzed. It is also shown that task loss probability is mainly affected by VT arrival rate, and is approximately independent of average VT speed.

关键词： Interference   Decoding   Resource management   Delays   Autonomous aerial vehicles   Tin   Receivers   Rate-splitting   central cloud   mobile edge computing   hybrid networks   unmanned aerial vehicles  

摘要： This paper proposes, and evaluates the benefits of, a hybrid central cloud (CC) and mobile edge computing (MEC) platform, especially introduced to balance the network resources for joint communication and computation. The transmission is further empowered by splitting the users' messages into private and common parts, to mitigate the interference within the CC and MEC platforms. While several power-hungry, computationally-limited unmanned aerial vehicles (UAVs) are deployed at the cell-edge to boost the CC connectivity and relieve part of its computation burden, the CC connects to the base-stations via capacity-limited fronthauls. The paper then considers the problem of maximizing the weighted sum-rate subject to fronthaul and computation capacity, achievable rates, power, delay, and data-split constraints. Thereby determining the beamforming vectors associated with the private and common messages, the computation allocations, and the data-split factors. Such intricate non-convex optimization problem is tackled using an iterative algorithm that relies on well-chosen discrete relaxation, successive convex approximation, and fractional programming, and can be compellingly implemented in a distributed fashion. The simulations illustrate the proposed algorithm's capabilities for empowering joint communication and computation, and highlight the pronounced role of rate-splitting and common message decoding in alleviating large-scale interference in hybrid CC/MEC networks.

关键词： 6G communication network   non-orthogonal multiple access   mobile edge computing   artificial intelligence  

摘要： Mobile edge computing (MEC) supported by non-orthogonal multiple access (NOMA) has recently gained a lot of interest due to its improved ability to lessen power consumption and MEC offload delay. In recent decades, the need for wireless communications has increased tremendously. Fifth-generation (5G) communications will soon be widely used and offer much more functionality than a fourth generation (4G). Between 2027 and 2030, an innovative wireless communication paradigm is known as the sixth generation (6G) system is projected to be introduced with the full help of artificial intelligence (AI). Advanced system capacity, higher data rate, lower latency, advanced security, and improved quality of service (QoS) than 5G systems are a few of the main challenges to resolve with 5G. The growing need for data rates in the sixth generation (6G) communication networks are being met by extraordinary technologies such as NOMA, Soft Computing (SC), and MEC. Owing to the massive attention to the NOMA-enabled MEC, there has been a significant spike in the number of papers published in this area, while more comprehensive studies and classifications are still needed. Using the preferred reporting items for systematic reviews and meta-analysis (PRISMA) guidelines, the investigation reports a systematic literature review (SLR) of NOMA-enabled MEC. This survey also evaluates numerous pieces of literature prudently chosen over a multi-step procedure and meets the selection criteria described in the paper summarizing our review.

关键词： computation offloading   mobile edge computing   resource allocation   delay minimum   WPR  

摘要： Computation offloading has received a significant amount of attention in recent years, with many researchers proposing joint offloading decision and resource allocation schemes. However, although existing delay minimization schemes achieve minimum delay costs, they do so at the cost of losing possible further maximization of the number of serviced requests. Furthermore, the asymmetry between uplink and downlink poses challenges to resource allocation in edge computing. This paper addresses this issue by formulating the joint computation offloading and edge resource allocation problem as a mixed-integer nonlinear programming (MINLP) problem in an edge-enabled asymmetrical network. Leveraging the margin between a delay-minimum scheme and a near-deadline scheme, a water pumping and refilling (WPR) algorithm is proposed to maximize the number of accepted requests. The WPR algorithm can function both as a supplementary algorithm to a given offloading scheme and as a standalone algorithm to obtain a resource allocation scheme following a customizable refilling policy. The simulation results demonstrated that the proposed algorithm outperforms delay-minimum schemes in achieving a high acceptance ratio.

关键词： task offloading   mobile edge computing   meta reinforcement learning   IoT devices  

摘要： Developing an effective task offloading strategy has been a focus of research to improve the task processing speed of IoT devices in recent years. Some of the reinforcement learning-based policies can improve the dependence of heuristic algorithms on models through continuous interactive exploration of the edge environment;however, when the environment changes, such reinforcement learning algorithms cannot adapt to the environment and need to spend time on retraining. This paper proposes an adaptive task offloading strategy based on meta reinforcement learning with task latency and device energy consumption as optimization targets to overcome this challenge. An edge system model with a wireless charging module is developed to improve the ability of IoT devices to provide service constantly. A Seq2Seq-based neural network is built as a task strategy network to solve the problem of difficult network training due to different dimensions of task sequences. A first-order approximation method is proposed to accelerate the calculation of the Seq2Seq network meta-strategy training, which involves quadratic gradients. The experimental results show that, compared with existing methods, the algorithm in this paper has better performance in different tasks and network environments, can effectively reduce the task processing delay and device energy consumption, and can quickly adapt to new environments.

关键词： UAVs   services selection   game theory   Stackelberg   mobile edge computing  

摘要： Nowadays, mobile devices are expected to perform a growing number of tasks, whose complexity is also increasing significantly. However, despite great technological improvements in the last decade, such devices still have limitations in terms of processing power and battery lifetime. In this context, mobile edge computing (MEC) emerges as a possible solution to address such limitations, being able to provide on-demand services to the customer, and bringing closer several services published in the cloud with a reduced cost and fewer security concerns. On the other hand, Unmanned Aerial Vehicle (UAV) networking emerged as a paradigm offering flexible services, new ephemeral applications such as safety and disaster management, mobile crowd-sensing, and fast delivery, to name a few. However, to efficiently use these services, discovery and selection strategies must be taken into account. In this context, discovering the services made available by a UAV-MEC network, and selecting the best services among those available in a timely and efficient manner, can become a challenging task. To face these issues, game theory methods have been proposed in the literature that perfectly suit the case of UAV-MEC services by modeling this challenge as a Stackelberg game, and using existing approaches to find the solution for such a game aiming at an efficient services' discovery and service selection. Hence, the goal of this paper is to propose Stackelberg-game-based solutions for service discovery and selection in the context of UAV-based mobile edge computing. Simulations results conducted using the NS-3 simulator highlight the efficiency of our proposed game in terms of price and QoS metrics.

关键词： Mobile edge computing  

摘要： Federated Learning (FL) plays a pivotal role in enabling artificial intelligence (AI)-based mobile applications in mobile edge computing (MEC). However, due to the resource heterogeneity among participating mobile users (MUs), delayed updates from slow MUs may deteriorate the learning speed of the MEC-based FL system, commonly referred to as the straggling problem. To tackle the problem, this work proposes a novel privacy-preserving FL framework that utilizes homomorphic encryption (HE) based solutions to enable MUs, particularly resource-constrained MUs, to securely offload part of their training tasks to the cloud server (CS) and mobile edge nodes (MENs). Our framework first develops an efficient method for packing batches of training data into HE ciphertexts to reduce the complexity of HE-encrypted training at the MENs/CS. On that basis, the mobile service provider (MSP) can incentivize straggling MUs to encrypt part of their local datasets that are uploaded to certain MENs or the CS for caching and remote training. However, caching a large amount of encrypted data at the MENs and CS for FL may not only overburden those nodes but also incur a prohibitive cost of remote training, which ultimately reduces the MSP’s overall profit. To optimize the portion of MUs’ data to be encrypted, cached, and trained at the MENs/CS, we formulate an MSP’s profit maximization problem, considering all MUs’ and MENs’ resource capabilities and data handling costs (including encryption, caching, and training) as well as the MSP’s incentive budget. We then show that the problem is convex and can be efficiently solved using an interior point method. Extensive simulations on a real-world human activity recognition dataset show that our proposed framework can achieve much higher model accuracy (improving up to 24.29%) and faster convergence rate (by 2.86 times) than those of the conventional FedAvg approach when the straggling probability varies between 20% and 80%. Moreover, the proposed

关键词： Double auction   Mobile edge computing   Economic benefits   Energy allocation   Budget balance   Individual rationality  

摘要： By transferring power-hungry huge data centers to lightweight Internet of Things (IoT) mobile devices, mobile edge computing (MEC) has completely changed the IoT. For MEC, to optimize economic gains and motivate profit-oriented entities, the joint resource allocation and network economics problem must be solved, and the joint issue is limited by local constraints, namely, the edge server only serves multiple nearby mobile devices, which is restricted by its available energy. The article studies the jointly issue of network economics and energy allocation in MEC, where mobile device apply for offloading at a purported bid and an edge server supplies its restricted serving at an asking price. In particular, this paper puts forward two dynamic pricing double auction strategies in the MEC system, i.e., a double auction according to the break-even mechanism (DABM) and a more practical double auction based on dynamic pricing mechanism (DADPM) to decide the matching between mobile devices and edge servers, and the pricing strategy for high-priced economic profit in the case of local restricts. Theoretical analysis shows that the proposed two algorithms have properties such as budget balance, individual rationality, economic benefit, authenticity. Extensive simulation experiments evaluate the efficiency of the system, and results verify that the proposed two schemes will greatly make better the economic benefits of MEC.

关键词： Mobile edge computing   Load balancing   Average transmission delay   Task offloading  

摘要： As a new type of computing paradigm closer to service terminals, mobile edge computing (MEC), can meet the requirements of computing-intensive and delay-sensitive applications. In addition, it can also reduce the burden on mobile terminals by offloading computing. Due to cost issues, results in the deployment density of mobile edge servers (MES) is restricted in real scenario, whereas the suitable MES should be chosen for better performance. Therefore, this article proposes a task offloading strategy under the sparse MES density deployment scenario. Commonly, mobile terminals may reach MES through varied access points (AP) based on multi-hop transmitting mode. The transmission delay and processing delay caused by the selection of AP and MES will affect the performance of MEC. For the purpose of reducing the transmission delay due to system load balancing and superfluous multi-hop, we formulated the multi-objective optimization problem. The optimization goals are the workload balancing of edge servers and the completion delay of all task offloading. We express the formulated system as an undirected and unweighted graph, and we propose a hybrid genetic particle swarm algorithm based on two-dimensional genes (GA-PSO). Simulation results show that the hybrid GA-PSO algorithm does not outperform state-of-the-art GA and NSA algorithms in obtaining all task offloading delays. However, the workload by standard deviation approach is about 90% lower than that of the GA and NSA algorithms, which effectively optimizes the performance of load balancing and verifies the effectiveness of the proposed algorithm.