关键词： mobile edge computing   convolutional neural network   deep reinforcement learning   wheat growth stages detection   dynamic migration algorithm  

摘要： The application of deep learning (DL) technology to the identification of crop growth processes will become the trend of smart agriculture. However, using DL to identify wheat growth stages on mobile devices requires high battery energy consumption, significantly reducing the device's operating time. However, implementing a DL framework on a remote server may result in low-quality service and delays in the wireless network. Thus, the DL method should be suitable for detecting wheat growth stages and implementable on mobile devices. A lightweight DL-based wheat growth stage detection model with low computational complexity and a computing time delay is proposed;aiming at the shortcomings of high energy consumption and a long computing time, a wheat growth period recognition model and dynamic migration algorithm based on deep reinforcement learning is proposed. The experimental results show that the proposed dynamic migration algorithm has 128.4% lower energy consumption and 121.2% higher efficiency than the local implementation at a wireless network data transmission rate of 0-8 MB/s.

关键词： Computational offloading   Mobile edge computing   Collaborative edge computing   Multi-access edge computing   Task offloading  

摘要： Many enterprise companies migrate their services and applications to the cloud to benefit from cloud computing advantages. Meanwhile, the rapidly increasing number of connected devices with the massive amount of generated data that use cloud services leads to high workload, congestion, and delay bottleneck in the centralized cloud architecture. Consequently, Mobile Edge Computing (MEC) is introduced as a new paradigm to expand cloud capabilities near the end devices. In addition, new technologies such as the Internet of Things (IoT), Autonomous Vehicles (AV), 5G, and Augmented Reality (AR) bring new demands and opportunities that MEC can make possible. Offloading delay-sensitive and computationally intensive tasks to nearby MEC nodes is an effective way that still is the most common open problem in MEC. The offloading problem in MEC has been widely studied in areas such as Vehicular Edge Computing (VEC), IoT, Radio Access Networks (RAN), and 5G but independently. Due to the high diversity of research areas, targeted issues, and adopted algorithms and tech-niques, finding the right research path in task offloading is highly demanding. To fill this gap, a comprehensive survey is conducted using the mixed-method systematic literature review involving qualitative and quantitative data from the studied papers. For each journal paper, the detailed information of the work area, targeted issue, formulation technique, optimization approach, adopted algorithms, evaluation techniques, performance matrices, dataset, utilized tools, and framework are extracted and analyzed using manual and automatic coding. Major offloading-related issues in MEC are investigated, and the taxonomy of journal papers based on adopted approaches is presented. For further future exploration, we suggest the potential areas of research, the contri-bution of the algorithms and technique, and the research direction in MEC. This review will give a quick and overall view of MEC's latest issues and solution

关键词： Mobile edge computing   Non-orthogonal multiple access   Successful computation probability   Genetic algorithm   Particle swarm optimization   Hybrid GA PSO  

摘要： Recent years have witnessed the computing process gradually move to the edge network, close to the physical data source, to serve applications that require large computations with very little latency. However, the termi-nal wireless devices' limited computing and energy resources pose obstacles to the practical implementation of these applications. Mobile Edge Computing (MEC) based non-orthogonal multiple access (NOMA) technology is solving this problem well thanks to its ability to serve many users with high data rates and spectrum utilization efficiency. This study investigates the performance and optimization of MEC surveillance systems using NOMA. Specifically, two camera units (CUs) perform the monitoring task to be accomplished by the MEC access point (AP) through Rayleigh fading wireless links. We then proposed the four-phase protocol for this system. Accordingly, we derive the closed-form exact expressions of the successful computation probability (SCP) and study the impact of the network parameters on the system performance. Furthermore, we propose and compare three meta-heuristic-based algorithms, namely MSCP-GA, MSCP-PSO, and MSCP-HGAPSO, to find the optimal parameters set to help the proposed system achieve the maximum SCP. The results show that the proposed algorithms can significantly improve the system's performance by 40% higher than when the optimal algorithm is not used. Insights into the pros and cons of different algorithms are also discussed in this study. Finally, we use the Monte-Carlo simulation to verify the correctness of this study.

关键词： Task analysis   Servers   Delay effects   Computational modeling   Routing   Delays   Cloud computing   Deep neural network (DNN)   computational offloading   vehicular networks   smart task division   routing policy   mobile edge computing  

摘要： Many advancements are being made in vehicular networks, such as self-driving, dynamic route scheduling, real-time traffic condition monitoring, and on-board infotainment services. However, these services require high computation power and precision and can be met using mobile edge computing (MEC) mechanisms for vehicular networks. MEC operates through the edge servers available at the roadside, also known as roadside units (RSU). MEC is very useful for vehicular networks because it has extremely low latency and supports operations that require near-real-time access to rapidly changing data. This paper proposes an efficient computational offloading, smart division of tasks, and cooperation among RSUs to increase service performance and decrease the delay in a vehicular network via MEC. The computational delay is further reduced by parallel processing. In the division of tasks, each task is divided into two sub-components which are fed to a deep neural network (DNN) for training. Consequently, this reduces the overall time delay and overhead. We also adopt an efficient routing policy to deliver the results through the shortest path to improve service reliability. The offloading, computing, division, and routing policies are formulated, and a model-based DNN approach is used to obtain an optimal solution. Simulation results prove that our proposed approach is suitable in a dynamic environment. We also compare our results with the existing state-of-the-art, showing that our proposed approach outperforms the existing schemes.

关键词： Mobile edge computing   Offloading decisions   Deep reinforcement learning   Dimension explosion   Clustering algorithm  

摘要： In many IoT scenarios, the resources of terminal devices are limited, and it is difficult to provide services with low latency and low energy consumption. Mobile edge computing is an effective solution by offloading computing tasks to edge server processing. There are some problems in the existing offloading decision algorithms: the offloading decision method based on heuristic algorithms cannot dynamically adjust the policy in the changing environment;the offloading algorithm based on deep reinforcement learning will lead to slow convergence and poor exploration effect due to the problem of dimension explosion. To solve the above problems, this paper designs an offloading decision algorithm to make dynamic decisions in a mobile edge computing network with multi-device access. The algorithm comprehensively considers the energy consumption of terminal equipment, offloading overhead, average delay and success rate of task completion, aiming to achieve the highest total revenue of the whole system in a period of time. In this work, the online offloading problem is abstracted as a Markov decision process. Based on the Double Dueling Deep Q-Network (D3QN) algorithm, the offloading decision is designed to adapt to the highly dynamic environment of the edge computing network and solve the problem of high state space complexity. In addition, this paper innovatively introduces a clustering algorithm into deep reinforcement learning (DRL) to preprocess the action space and solve the explosion problem of the action space dimension caused by the increase of terminal devices. The experimental results show that the proposed algorithm is superior to the baseline strategies such as Deep Q-Network (DQN) algorithm in convergence speed and total reward.

关键词： Mobile edge computing  

摘要： In this paper, we consider a Mobile Edge Computing (MEC) system with a source transmitter (ST) and multiple relay nodes (RNs). ST has sufficient computing resources, but lacks communication ability, while the situation of RN is the opposite. To tackle this resource imbalance problem, we propose a cooperative computation and communication resources sharing strategy using contract theory under complete and incomplete information scenarios. The ST offers a contract combining computation time and relay power, and the RNs forward the computational results for ST in exchange for the usage of computation capacity. For complete information scenario, we derive the optimal contract to maximize the ST’s utility and we show the existence and uniqueness of optimal contract. We further explore the properties of a feasible contract for incomplete information scenario where the channel gain and relay cost are the private information of RNs and not known by ST. We analyze the necessary and sufficient conditions for a contract to be feasible and propose a sequence optimization algorithm to obtain the optimal contract. Finally, we show the feasibility and optimality of the contract through simulation results. © 2023, The Author(s), under exclusive licence to Springer Nature B.V.

关键词： mobile edge computing   machine learning   resource allocation   reinforcement learning  

摘要： In the 5G era, the amount of network data has grown explosively. A large number of new computation-intensive applications have created demand for edge computing in mobile networks. Traditional optimization methods are difficult to adapt to the dynamic wireless network environment because they solve the problem online, which is not suitable in edge computing scenarios. Therefore, in order to obtain a mobile network with better performance, we propose a network frame with a resource allocation algorithm based on power consumption, delay and user cooperation. This algorithm can quickly realize the optimization of a network to improve performance. Specifically, compared with heuristic algorithms, such as particle swarm optimization, ant colony algorithm, etc., commonly used to solve such problems, the algorithm proposed in this paper can reduce some aspects of network performance (including delay and user energy consumption) by about 10% in a network dominated by downlink tasks. The performance of the algorithm under certain network conditions was demonstrated through simulations.

关键词： Mobile edge computing   Server placement   Service placement   Delay   Combining optimization  

摘要： With the rapid increase of applications in 5G and Internet of Things, mobile edge computing (MEC) has been proposed to reduce the burden of central cloud and decrease the users request delay by deploying edge servers and edge services close to users. Due to resources constraint of edge servers and disadvantage of standalone placement optimization of edge servers or edge services, the discussion focusing on the combining optimization of server placement and service placement has engendered. At present, the placement combination is studied with strict assumption constrains, such as homogeneous service and server with unlimited resources, which are not suitable for the reality scenarios. This paper proposes Placement Combination between Heterogeneous Services and heterogeneous capacitated Servers (PCHSS). PCHSS aims to minimize the delay in computation and transmission as well as to balance resources in edge servers. Because the placement combination optimization is a NP-hard problem, we propose two solution algorithms named FHPC and IUPC. Both algorithms have a two-layer iterative optimization structure with different convergence time and result performances. FHPC can converge to a good result quickly, and IUPC can achieve better results with a relatively higher computational complexity. Then we prove that both algorithms can converge in polynomial time. Extensive simulations demonstrate the significant effectiveness of the placement combination, and our algorithms can reduce the user request delay by up to 51% compared with baseline algorithms.

关键词： 5G   communication overhead   DDPG   mobile edge computing   reinforcement learning   resource allocation  

摘要： Distributed base station deployment, limited server resources and dynamically changing end users in mobile edge networks make the design of computing offloading schemes extremely challenging. Considering the advantages of deep reinforcement learning (DRL) in dealing with dynamic complex problems, this paper designs an optimal computing offloading and resource allocation strategy. Firstly, the authors consider a multi-user mobile edge network scenario consisting of Macro-cell Base Station (MBS), Small-cell Base Station (SBS) and multiple terminal devices, the communication overhead and calculation overhead generated are formulated and described in detail. Besides, combined with the deterministic delay of tasks, the optimization objective of this paper is clarified to comprehensively consider system energy consumption. Then, a learning algorithm based on Deep Deterministic Policy Gradient (DDPG) is proposed to minimize system energy consumption. Finally, simulation experiments show that the authors' proposed DDPG algorithm can effectively optimize the target value, and the total system energy consumption is only 15.6 J, which is better than other compared algorithms. It is also proved that the proposed algorithm has excellent communication resource allocation ability.

关键词： mobile edge computing   unmanned aerial vehicle   task migration   deep reinforcement learning   federated learning  

摘要： At present, with the intelligence that has been achieved in computer and communication technologies, vehicles can provide many convenient functions to users. However, it is difficult for a vehicle to deal with computationally intensive and latency-sensitive tasks occurring in the vehicle environment by itself. To this end, mobile edge computing (MEC) services have emerged. However, MEC servers (MECSs), which are fixed on the ground, cannot flexibly respond to temporal dynamics where tasks are temporarily increasing, such as commuting time. Therefore, research has examined the provision of edge services using additional unmanned aerial vehicles (UAV) with mobility. Since these UAVs have limited energy and computing power, it is more important to optimize energy efficiency through load balancing than it is for ground MEC servers (MECSs). Moreover, if only certain servers run out of energy, the service coverage of a MEC server (MECS) may be limited. Therefore, all UAV MEC servers (UAV MECSs) need to use energy evenly. Further, in a high-mobility vehicle environment, it is necessary to have effective task migration because the UAV MECS that provides services to the vehicle changes rapidly. Therefore, in this paper, a federated deep Q-network (DQN)-based task migration strategy that considers the load deviation and energy deviation among UAV MECSs is proposed. DQN is used to create a local model for migration optimization for each of the UAV MECSs, and federated learning creates a more effective global model based on the fact that it has common spatial features between adjacent regions. To evaluate the performance of the proposed strategy, the performance is analyzed in terms of delay constraint satisfaction, load deviation, and energy deviation.