关键词： Mobile edge computing   Computation offloading   Unmanned aerial vehicle   Digital twin   Deep reinforcement learning   Federated learning  

摘要： In the industrial Internet, Mobile Edge Computing (MEC) can provide the ability to transfer a large number of delay-sensitive and compute-intensive tasks to MEC servers, thus improving Quality of Service (QoS). Considering Unmanned Aerial Vehicles (UAVs) have the advantages of wide communication coverage and low deployment cost, UAVs have great potential to be employed as aerial base stations to provide computation resources for Intelligent Mobile Devices (IMDs). Due to the limited computation resources and energy of IMDs, we designed a multi-UAV-assisted MEC system in ground cells. To minimize the weighted sum of task completion delay and energy consumption, and ensure the QoS requirements of IMDs, we jointly consider the dynamic channel state, renewable energy utilization, UAVs trajectory, and tasks offloading ratio. To solve the non-convexity problem of complex high-dimensional states, we propose a model-free Deep Reinforcement Learning (DRL) offloading scheme based on the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm. Moreover, we adopt Federated Learning (FL) to train DRL models to enhance the robustness of the model and the security of IMDs data. Meanwhile, the real environment is modeled as Digital Twin (DT) to monitor network changes and train the local DRL model, and the central cloud server can obtain the local model in real-time to aggregate the global model. Extensive experimental numerical results show that the proposed algorithm improves the system energy efficiency and reduces task completion delay.

关键词： Mobile edge computing   Green communications   Mixed-integer programming   Resource allocation  

摘要： We investigate the green resource allocation to minimize the energy consumption of the users in mobile edge computing systems,where task offloading decisions,transmit power,and computation resource allocation are jointly *** considered energy consumption minimization problem is a non-convex mixed-integer nonlinear programming problem,which is challenging to ***,we develop a joint search and Successive Convex Approximation(SCA)scheme to optimize the non-integer variables and integer variables in the inner loop and outer loop,***,in the inner loop,we solve the optimization problem with fixed task offloading *** to the non-convex objective function and constraints,this optimization problem is still non-convex,and thus we employ the SCA method to obtain a solution satisfying the Karush-Kuhn-Tucker *** the outer loop,we optimize the offloading decisions through exhaustive ***,the computational complexity of the exhaustive search method is greatly *** reduce the complexity,a heuristic scheme is proposed to obtain a sub-optimal *** results demonstrate the effectiveness of the developed schemes.

关键词： Virtual reality   Mobile edge computing   Multimedia communication   Distributed algorithms  

摘要： Virtual Reality (VR) is becoming an important use case for 5G wireless networks, and Mobile Edge Computing (MEC) servers are being explored as a way to reduce VR video latency. To address the limited cache space of a single MEC server, this paper proposes dividing geographically close MEC servers into collaborative domains. A new VR video transmission architecture is designed after analyzing video compression *** proposed architecture splits the VR video into equal-sized tile files to simplify the cache problem and improve caching efficiency. However, the large number of different tile files presents a challenge. To address this, the paper proposes an optimized k-shortest paths (OKSP) algorithm with a time complexity of O((K & BULL;M+N)& BULL;M & BULL;log N), where K is the number of tiles that all M MEC servers can cache in the collaboration domain and N is the number of tile files. For extremely large-scale data cases, a greedy-based approximation algorithm is also proposed. The numerical results demonstrate the OKSP algorithm's excellent performance in solving large-scale data, outperforming other caching algorithms in experiments.

关键词： Mobile edge computing   Cloudlet deployment   Graph cut   Approximate kernel optimization  

摘要： Mobile edge computing provides a low-latency, high-bandwidth cloud computing environment for resource-constrained mobile devices by allowing mobile devices to offload tasks, but user task migration causes greater transmission delays. Cloudlets, a new component of mobile edge computing, can perform tasks offloaded by mobile users nearby to reduce the access latency and meet users' requirements for system response time. However, deploying cloudlets in large-scale wireless metropolitan area networks (WMANs) to improve the service quality of mobile applications is currently still difficult. To resolve this issue, we design a cloudlet deployment model based on approximate graph cut, which abstracts the wireless communication network into an undirected weighted graph, divides the graph according to the access point location attributes, and minimizes the user access delay of subgraphs to obtain optimal network area segmentation and cloudlet deployment locations. We also develop an efficient kernel method to optimize the objective function of graph cuts. The simulation experimental results demonstrate that our model has low time and space complexity;thus, it is suitable for large-scale cloudlet deployment and has valuable application prospects.

关键词： Mobile edge computing   Reinforcement learning   Task offloading   Fuzzy inference   Internet of vehicles  

摘要： With the rapid development of autonomous and intelligent techniques, vehicles are currently equipped with computation and communication modules for satisfying clients' on-vehicle computing requests. To meet the client's on-vehicle computation requests such as on-vehicle games and self-driving mechanisms, vehicles have to continuously generate computational tasks. However, due to the limited on-vehicle computation capacities, it is barely possible to handle the above requests by the vehicle itself. These requests are then offloaded to special devices such as roadside units or intelligent vehicles. With the fluid feature of the traffic, more requests are generated during the peak hours than at the low hours. Based on the above facts, two significant challenges arise in vehicular edge computing networks: (i) how to accurately determine whether the vehicular networks are in peak or low hours, and (ii) how to effectively offload the generated requests? In this paper, to tackle the above challenges, we investigate the problem of computational requests offloading under different vehicular networking scenarios. To handle the first challenge, we propose the fuzzy inference-based algorithm to identify the situation of the vehicular network (i.e., whether it is in peak hours or low hours). We employ the reinforcement learning-based algorithm for the second challenge to offload the computational requests effectively. Experiments show that our schemes outperform the benchmark by an average of 24.8% regarding resource utilization when satisfying the interests of both service providers and clients.

关键词： Computational capacity   disaster management   mobile edge computing   offloading   unmanned aerial vehicles  

摘要： Unmanned aerial vehicles (UAVs) are considered for disaster management to provide better coverage or help first responders provide advanced services. Furthermore, UAVs combined with mobile edge computing (MEC) capabilities, have recently emerged as a support for cloud computing by deploying UAV-assisted MEC at the network edge to reduce the latency and load of cloud computing data centers. We investigate the problem of maximizing the utility function by jointly optimizing the computing power, user associations, performance, duration, and location of user equipments (UEs) in UAV-assisted MEC networks in a disaster scenario. We considered several constraints including delay, quality of service, and coverage. The optimization problem is a mixed-integer non-linear programming problem. We propose a multi-stage offloading algorithm based on a learning algorithm and an interior-point method to obtain a viable solution. The simulation results obtained demonstrate the effectiveness of our proposed algorithm compared to existing schemes.

关键词： Task analysis   Cloud computing   Costs   Approximation algorithms   Heuristic algorithms   Stochastic processes   Servers   Mobile edge computing   service placement   task offloading   two-timescale lyapunov optimization  

摘要： As a new computing paradigm, mobile edge computing (MEC) pushes the centralized cloud resources close to the edge network, which significantly reduces the pressure of the backbone network and meets the requirements of emerging mobile applications. To achieve high performance of the MEC system, it is essential to design efficient task offloading and service placement schemes, which are responsible for offloading tasks to the edge servers while considering the heterogeneity and diversity of computation services. Our MEC system aims to maximize the long-term average network utility while maintaining the stability of the edge network. Considering that synchronous manner overlooks the scenarios endowed with asymmetric update frequencies for service placement and task offloading, we propose an online algorithm based on the two-timescale Lyapunov optimization in a stochastic network environment without requiring the future information. By making asynchronous decisions on service placement and task offloading with different control parameters $V$V, we can achieve a time-average sub-optimal solution that is close to the offline optimum. In addition, we introduce the varying control parameter $V(t)$V(t) and $\Omega$omega-additive approximation to enhance the robustness of the proposed algorithm within an error Omega. Finally, rigorous theoretical analysis and extensive trace-driven experimental results show that the proposed algorithm achieves the [O(1/V),O(V)] performance-backlog tradeoff and is more competitive than benchmarks.

关键词： Mobile edge computing   Wireless energy transfer   Intelligent reflecting surfaces   Unmanned aerial vehicle  

摘要： Mobile edge computing (MEC) and wireless energy transfer are two important paradigms to improve the computation bits and provide more cost-effective energy for low-power internet of things (IoT) devices. However, the performance of energy transfer and task offloading is significantly affected by propagation loss, and hence the links between IoT devices and transmitters are frequently blocked. To address this issue, in this paper, an intelligent reflecting surfaces (IRS) system is utilized to enhance the energy harvesting and task offloading performances in a multi-unmanned aerial vehicle (UAV)-assisted MEC system. Also, a power beacon (PB) is deployed in the coverage area to provide suitable and stable energy for UAVs via a laser charging system. The sum computation bits of all IoT devices are maximized by jointly optimizing transmit power, time allocation, the phase shift in the energy transfer stage, the phase shift in the task offloading stage, CPU frequency of IoT devices, and UAVs' trajectory. An alternate search method (ASM) is proposed to tackle the non-convexity of the formulated problem in which the closed-form expressions of transmit power, time allocation, the phase shift in the task offloading stage, and CPU frequency are derived. Also, the semidefinite relaxation (SDR) approach and successive convex approximation (SCA) methods are exploited to obtain the phase shifts in the energy transfer stage and UAVs' trajectory via CVX optimizer, respectively. The numerical evaluations confirm the performance gain of the proposed IRS-assisted multi-UAV MEC system compared to existing benchmark designs.& COPY;2023 Elsevier Inc. All rights reserved.

关键词： Smart factory   Mobile edge computing   Cloud-edge-end collaboration   Emergency task offloading  

摘要： The realization of smart factories is becoming increasingly possible due to the rapid development of edge computing technology and 5G communication technology. Smart devices in smart factories can perform tasks such as environmental monitoring, production scheduling, and autonomous mobility. These tasks cannot be completed on time depending only on the computing power of smart devices. Therefore, some tasks need to be performed on edge servers and cloud servers. However, events such as natural disasters, network attacks, and hardware failures will cause server computing resource failures. These threats bring hidden dangers to the normal operation of smart factories. This paper proposes an emergency offloading strategy based on cloud-edge-end collaboration for smart factories. In this strategy, the total task execution delay and the key task execution delay constitute an objective function, which is solved by a Fast Chemical Reaction Optimization (Fast-CRO) algorithm. The algorithm follows the principle of prioritizing the offloading of key tasks in emergency situations, and it quickly makes emergency offloading decisions for the system. Simulation results show that this strategy outperforms the other five baseline algorithms and existing priority based CRO algorithms.

关键词： Mobile edge computing   Unmanned aerial vehicle   Energy harvesting   Deep reinforcement learning  

摘要： In order to meet the double-sided challenges brought by the shortage of computation resources and energy of users, we investigate in this paper the optimization of energy efficiency (EE) in an unmanned aerial vehicle (UAV)-assisted wireless network, where UAV is functioned as a flying energy station and edge server to provide charging and computing services for ground users. We aim to maximize the average EE of the mobile edge computing network by the joint design of user transmit power, user computing frequency, UAV transmit power, bandwidth allocation, and UAV trajectory planning under strict energy and power constraints. In order to solve such challenging problem, we first elaborately construct a Markov decision process to model task offloading and resource allocation by learning from past experiences. Then, an average EE maximization method relying on deep reinforcement learning (DRL) is designed to efficiently adjust task offloading policy, where the policy of agent can be gradually improved by interacting with the environment and collecting the experience for learning. Finally, the EE-maximization proximal policy optimization (EE-PPO) algorithm is proposed to train the DRL agent and thereby solve this optimization problem. Numerical results are given to indicate that the proposed EE-PPO method has the properties of both fast convergence and well performance.