关键词： Mobile edge computing  

摘要： With the increasing demand for mobile computing, the requirement for intelligent resource management has also increased. Cloud computing lessens the energy consumption of user equipment, but it increases the latency of the system. Whereas edge computing reduces the latency along with the energy consumption, but it has limited resources and cannot process bigger tasks. To resolve these issues, a Priority-based Hybrid task Partitioning and Offloading (PHyPO) scheme is introduced in this paper, which prioritizes the tasks with high time sensitivity and offloads them intelligently. It also calculates the optimal number of partitions a task can be divided into. The utility of resources is maximized along with increasing the processing capability of the model by using a hybrid architecture, consisting of mobile devices, edge servers, and cloud servers. Automated machine learning is used to identify the optimal classification models, along with tuning their hyperparameters, which results in adaptive boosting ensemble learning-based models to reduce the time complexity of the system to O(1). The results of the proposed algorithm show a significant improvement over benchmark techniques along with achieving an accuracy of 96.1% for the optimal partitioning model and 94.3% for the optimal offloading model, with both the results being achieved in significantly less or equal time as compared to the benchmark techniques. © 2023, The Authors. All rights reserved.

关键词： Collaborative Tasks Scheduling   Geo Clustering   Mobile Edge Computing   Particle Swarm Optimization   Resource Allocation  

摘要： Multi-access Edge Computing (MEC) has recently been suggested as an addition to cloud computing. MEC servers are placed close to the network's edge to reduce latency and relieve demand on cloud data centres. The cloud is more resourceful than the MEC server, which has less resource. Each MEC server cannot satisfy all computational and large data needs from user devices when operating independently. Collaborative edge computing (CEC) is a popular new concept in which devices on the edge work together by sharing data and computer resources. CEC must determine when and where each task is executed, making task offloading an important problem to address. However, it is challenging to solve task offloading in CEC because tasks can be offloaded to neighbouring devices, resulting in bandwidth contention among network flows. Most existing works do not jointly consider network flow scheduling, which can result in network congestion and inefficient completion time performance. This paper suggests a unique approach for planning, executing, and clustering MEC servers in a resource allocation model. Belief rule Geo clustering method used to cluster the MEC server based on real-time data of intensive tasks. For the simple sake, real-time monitoring application based on video surveillance is taken. The unsupervised cluster algorithm used to distribute the software components optimally among mobile devices and reduce cluster traffic in relation to the data center. The distributed MEC channel task allocation model is used to share tasks when the MEC server is busy receiving data, and a particle swarm-based joint optimization architecture is used to optimize clustered data. Extensive experiments have been performed on three datasets concerning energy utilization, delay, computational burden, throughput, and network Quality of service (QoS). The proposed technique achieved better results than benchmark solutions, which do not make a joint decision. © 2023 Little Lion Scientific.

关键词： Aerial edge computing (AEC)   mobile edge computing   Quality of Service (QoS)   wireless communication network  

摘要： In the beyond 5G/6G era, aerial edge computing (AEC) is expected to be used as significant components in Internet of Things. AEC brings flexible deployment with Line-of-Sight communication links for task offloading and computing services. In this article, we present a comprehensive survey of the AEC technology. First, we introduce a three-layer architecture of AEC, which includes the satellite, unmanned aircraft vehicles, and ground terminals. Second, we illustrate challenges in AEC, and summarize recent studies in terms of AEC performance metrics that include energy efficiency, latency, and operation cost to address challenges in AEC. Further, we introduce the advanced technologies applied in AEC management, e.g., artificial intelligence (AI) and the distributed optimization. Finally, the applications and the open issues with AEC are summarized and classified in the study.

关键词： connected and automated vehicles   cooperative perception   mobile edge computing   motion planning   surrogate safety assessment model   vehicle-to-everything communication  

摘要： This paper reports the implementation and results of a simulation-based analysis of the impact of cloud/edge-enabled cooperative perception on the performance of automated driving in unsignalized roundabouts. This is achieved by comparing the performance of automated driving assisted by cooperative perception to that of a baseline system, where the automated vehicle relies only on its onboard sensing and perception for motion planning and control. The paper first provides the descriptions of the implemented simulation model, which integrates the SUMO road traffic generator and CARLA simulator. This includes descriptions of both the baseline and cooperative perception-assisted automated driving systems. We then define a set of relevant key performance indicators for traffic efficiency, safety, and ride comfort, as well as simulation scenarios to collect relevant data for our analysis. This is followed by the description of simulation scenarios, presentation of the results, and discussions of the insights learned from the results.

关键词： Mobile edge computing   millimeter-wave communications   non-or thogonal multiple access   computation efficiency   resource allocation  

摘要： In this paper, the millimeter-wave (mmWave) communications and non-orthogonal multiple access (NOMA) are exploited for mobile edge computing (MEC) networks to improve the performance of task offloading. Aiming at improving the computation efficiency (CE) and ensuring the fairness among users, we study the CE optimization for mmWave-MEC with NOMA, where both the analog beamforming (ABF) and hybrid beamforming (HBF) architectures under the partial offloading mode are considered. First, according to the max-min fairness criterion, the CE optimization problem is formulated to jointly optimize the ABF at the base station and the local resource allocation of each user in mmWave-MEC with ABF. An efficient algorithm based on the penalized successive convex approximation is proposed to solve this non-convex problem. Then, the max-min CE optimization problem in mmWave-MEC with HBF is studied, where the joint design of the HBF at the BS and the local resource allocation of each user is carried out. By using the penalty function and the inexact block coordinate descent method, a feasible optimization algorithm is developed to tackle this challenging problem. Simulation results verify the convergence of the proposed algorithms and show that the proposed resource allocation schemes can improve the system CE effectively, and the mmWave-MEC with HBF scheme can obtain higher CE than that with ABF scheme. Besides, the NOMA scheme exhibits superior performance over the conventional orthogonal multiple access scheme in terms of CE.

关键词： Deep reinforcement learning   Internet of vehicles   Mobile edge computing   Scheduling optimization  

摘要： Driven by the construction of smart cities, networks and communication technologies are gradually infiltrating into the Internet of Things (IoT) applications in urban infrastructure, such as automatic driving. In the Internet of Vehicles (IoV) environment, intelligent vehicles will generate a lot of data. However, the limited computing power of in-vehicle terminals cannot meet the demand. To solve this problem, we first simulate the task offloading model of vehicle terminal in Mobile Edge Computing (MEC) environment. Secondly, according to the model, we design and implement a MEC server collaboration scheme considering both delay and energy consumption. Thirdly, based on the optimization theory, the system optimization solution is formulated with the goal of minimizing system cost. Because the problem to be resolved is a mixed binary nonlinear programming problem, we model the problem as a Markov Decision Process (MDP). The original resource allocation decision is turned into a Reinforcement Learning (RL) problem. In order to achieve the optimal solution, the Deep Reinforcement Learning (DRL) method is used. Finally, we propose a Deep Deterministic Policy Gradient (DDPG) algorithm to deal with task offloading and scheduling optimization in high-dimensional continuous action space, and the experience replay mechanism is used to accelerate the convergence and enhance the stability of the network. The simulation results show that our scheme has good performance optimization in terms of convergence, system delay, average task energy consumption and system cost. For example, compared with the comparison algorithm, the system cost performance has improved by 9.12% under different task sizes, which indicates that our scheme is more suitable for highly dynamic Internet of Vehicles environment.

关键词： Mobile edge computing   seagull optimization   deep belief network   resource management   parameter tuning  

摘要： Recently,computation offloading has become an effective method for overcoming the constraint of a mobile device(MD)using computationintensivemobile and offloading delay-sensitive application tasks to the remote cloud-based data *** city benefitted from offloading to edge *** a mobile edge computing(MEC)network in multiple *** comprise N MDs and many access points,in which everyMDhasM independent real-time *** study designs a new Task Offloading and Resource Allocation in IoT-based MEC using Deep Learning with Seagull Optimization(TORA-DLSGO)*** proposed TORA-DLSGO technique addresses the resource management issue in the MEC server,which enables an optimum offloading decision to minimize the system *** addition,an objective function is derived based on minimizing energy consumption subject to the latency requirements and restricted *** TORA-DLSGO technique uses the deep belief network(DBN)model for optimum offloading ***,the SGO algorithm is used for the parameter tuning of the DBN *** simulation results exemplify that the TORA-DLSGO technique outperformed the existing model in reducing client overhead in the MEC systems with a maximum reward of 0.8967.

关键词： UAV   Wireless networks   Aerial base station   Aerial relay   Mobile edge computing   Air-ground communications  

摘要： Due to their characteristics of mobility, flexibility and adaptive altitude, unmanned aerial vehicles (UAVs), also known as drones, have immense potential applications in wireless systems. In particular, UAVs can operate as flying mobile terminals within a cellular network, and such cellular-connected UAVs have enabled several applications ranging from real-time video streaming to item delivery. Furthermore, the deployment of UAVs in wireless networks as flying base stations (BSs), servers or relays is expected to enhance network performance in terms of coverage, capacity, reliability and energy-efficiency. In this paper, a comprehensive tutorial on the potential applications and benefits of UAVs is presented. Specifically, UAVs as aerial BSs collecting data from ground sensors, transmitting power to energy-constrained devices and broadcasting information to ground users are explored along with representative design objectives. Then, UAVs as relay nodes for coverage extension, pathloss compensation and interference mitigation are surveyed comprehensively. Besides, UAVs as mobile servers performing edge computing for ground users are introduced, and various aerial offloading modes are reviewed thoroughly. Furthermore, radio and computation resource optimization, as well as mathematical tools and developed algorithms, are described in UAV-assisted communicating, relaying, and computing systems. Finally, open problems and potential research directions pertaining UAV-assisted communications are presented. In a nutshell, this tutorial provides key guidelines on how to design, analyze and optimize UAV-assisted wireless communication systems.

关键词： Edge server placement   Dynamic movement   Game theory   Mobile edge computing   Query throughput  

摘要： Edge server placement plays a vital role in mobile edge computing to enable low-latency and high-throughput services by deploying edge servers at suitable geographical locations. Exiting work mainly focused on satisfying the QoS metrics of deployment while the impact of the dynamic movement of users is ignored. Dynamic movements result from different lifestyles and routines of mobile users whose points of interest thus changes over time which leads to significant unbalanced workload for edge servers. To address this challenge, we present a mobility-aware edge server placement method with two key features: (1) in the offline phase, we designed a fast heuristic algorithm to generate an edge server deployment strategy for massive data;(2) in the online phase, we designed a fine-tuning mechanism of deployment strategy based on cooperative game theory to adapt to users' dynamic movements. To demonstrate the utility of the proposed method, we have performed a comprehensive experimental evaluation on a large-scale dataset of over 1500 edge devices collected over a year. Experimental results show our method outperforms all baselines significantly in terms of query throughput and response time.

关键词： Mobile edge computing   Unmanned aerial vehicle   Online multi-agent proximal policy   optimization (O-MAPPO)   Energy efficiency  

摘要： The combination of mobile edge computing (MEC) systems with unmanned aerial vehicle (UAV) has gained a high profile in recent years due to its high flexibility and ability to handle intensive tasks. The computation offloading strategy and the resource allocation scheme affect the system performance significantly. Moreover, the system complexity increases with the numbers of users and servers exponentially. It is challenging to consider jointly the computation offloading and the resource allocation in MEC systems that have multiple users and multiple servers. This paper formulates the joint resources management in the UAV assisted MEC network as a partially observable markov decision process and proposes an online multi-agent proximal policy optimization (O-MAPPO) scheme to improve the energy efficiency while guaranteeing the requirements in task, power consumption, computation, and time. Specifically, users and servers are set as agents. All agents cooperatively make decisions of computation offloading and resource allocation to maximize the energy efficiency. Simulation results show that the O-MAPPO scheme significantly outperforms benchmark algorithms in robustness and stability.