关键词： Mobile edge computing   internet of things   UAVs   ground mobile users  

摘要： Mobile edge cloud networks can be used to offload computationally intensive tasks from Internet of Things(IoT)devices to nearby mobile edge servers,thereby lowering energy consumption and response time for ground mobile users or IoT *** of Unmanned Aerial Vehicles(UAVs)and the mobile edge computing(MEC)server will significantly benefit small,battery-powered,and energy-constrained devices in 5G and future wireless *** address the problem of maximising computation efficiency in U-MEC networks by optimising the user association and offloading indicator(OI),the computational capacity(CC),the power consumption,the time duration,and the optimal location planning *** is possible to assign some heavy tasks to the UAV for faster processing and small ones to the mobile users(MUs)*** paper utilizes the k-means clustering algorithm,the interior point method,and the conjugate gradient method to iteratively solve the non-convex multi-objective resource allocation *** to simulation results,both local and offloading schemes give optimal solution.

关键词： Demand response   mobile edge computing   energy consumption   game theory   potential game  

摘要： Mobile edge computing (MEC), as a key technology that facilitates 5G networks, provides a new and prospective mobile computing paradigm that allows the deployment of edge servers at base stations geographically close to mobile users to reduce their end-to-end network latency. Similar to cloud servers, edge servers running 24/7 in an MEC system consume a large amount of energy, contribute a significant proportion of global carbon emissions, and thus require demand response management. Demand response has been widely employed to reduce energy consumption at data centers. However, existing demand response approaches for data centers are rendered obsolete by the new and unique characteristics of MEC systems: 1) proximity constraint - mobile users can be served by neighbor edge servers only;2) latency constraint - mobile users' workloads should be processed by their neighbor edge servers to ensure low latency;and 3) capacity constraint - edge servers have limited computing and communication resources to serve mobile users. Demand response for MEC is further complicated by the non-orthogonal multiple access (NOMA) scheme - the emerging radio access scheme for 5G. Communication resources like channels and transmit power in the NOMA-based MEC system must be systematically considered with computing resources like CPU, memory and storage to fulfill mobile users' resource demands. This paper makes the first attempt to tackle this Edge Demand Response (EDR) problem. We first formulate this problem and prove its $\mathcal {NP}$NP-hardness. Then, we propose a two-phase game-theoretical approach, named EDRGame, to solve the EDR problem. Its performance is theoretically analyzed and experimentally evaluated against three baseline approaches and two state-of-the-art approaches on a widely-used real-world dataset. The results show that it solves the EDR problem effectively and efficiently.

关键词： Edge service placement   mobile edge computing   contextual multi-armed bandit   online learning  

摘要： Mobile Edge Computing (MEC), envisioned as a cloud extension, pushes cloud resource from the network core to the network edge, thereby meeting the stringent service requirements of many emerging computation-intensive mobile applications. Many existing works have focused on studying the system-wide MEC service placement issues, personalized service performance optimization yet receives much less attention. As motivated, in this paper we propose a novel adaptive user-managed service placement mechanism, which jointly optimizes a user's perceived-latency and service migration cost, weighted by user-specific preferences. We first formulate the user-managed dynamic service placement process with limited system information as a contextual multi-armed bandit learning problem. In particular, we investigate both cases without and with neighboring edge feedbacks, where the later considers edge information sharing for more informed decision making. For both cases, we design lightweight Thompson-sampling based online learning algorithms, which can efficiently assist the user to make adaptive service placement decisions. We further conduct a novel information-directed theoretical analysis on the regret bound of the proposed online learning algorithms and reveal the structural impact of edge information sharing. Extensive evaluations demonstrate the superior performance gain of the proposed adaptive user-managed service placement mechanism over existing learning schemes.

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   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： Multimodal semantic communication   Deep reinforcement learning   Content popularity   Bidirectional caching   Mobile edge computing  

摘要： Mobile Edge Computing (MEC) enables immersive XR with multimodal data by coordinating com-munication, computation, and caching (3C) resources upcoming 6G. The traditional communication constrained by Shannon's theorem cannot accommodate the user demands for ultra-reliability and high throughput. In contrast, the semantic communication improves the quality of service and user experience by exploiting the semantic features. This paper constructs a multi-user MEC structure based on multimodal semantic communication for interactive AR/VR games. We construct a bidirectional caching task model to achieve cache-enhanced computing. To minimize the system cost, including the user latency, energy consumption, and storage size, we propose a content popularity-based DQN (CP-DQN) algorithm to make caching decisions. Then the CP-DQN is extended to the cache-computation coordination optimization algorithm (CCCA) to achieve the 3C resources tradeoff. Simulation results demonstrate that the proposed algorithm outperforms existing algorithms in terms of caching hit ratio, computation cost and edge resource utilization.(c) 2022 Elsevier B.V. All rights reserved.

关键词： Roads   Servers   Task analysis   Computer architecture   Heuristic algorithms   Optimization   Image edge detection   Convex optimization   deep reinforcement learning   latency optimization   mobile edge computing   proximity detection  

摘要： In road networks, mobile users (including vehicles and pedestrians) need to know the proximity relationship with other users in real time, referred to as the problem of proximity detection which is very significant for autonomous driving. However, due to limited computing and storage resources of mobile users and real-time changes of road network status, it becomes a difficult task to calculate and update the proximity relationship between users in real time. Therefore, in this paper, we first propose a computation offloading scheme and a dynamic road network state update model for proximity detection in dynamic road networks based on Mobile Edge Computing (MEC), and formulate the latency optimization problem for proximity detection in the dynamic road network as a nonlinear optimization problem. Then we use the Sequential Least Squares Programming (SLSQP) algorithm to solve the latency optimization problem. In addition, to reduce the running time, we also use the deep reinforcement learning approach, i.e., the Deep Deterministic Policy Gradient (DDPG) algorithm, to address the latency optimization problem. Simulation results show that, compared with the SLSQP algorithm, the DDPG algorithm can effectively and efficiently reduce the computational time of the optimal latency each time by continuously adjusting the task allocation strategy, and the computational time of the DDPG algorithm is two orders of magnitude lower than the SLSQP algorithm.

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： Industrial Internet of Things   mobile edge computing   wireless power transfer   heterogeneous computing tasks   resource allocation  

摘要： Integrating wireless power transfer with mobile edge computing (MEC) has become a powerful solution for increasingly complicated and dynamic industrial Internet of Things (IIOT) systems. However, the traditional approaches overlooked the heterogeneity of the tasks and the dynamic arrival of energy in wirelessly powered MEC-enabled IIOT systems. In this article, we formulate the problem of maximizing the product of the computing rate and the task execution success rate for heterogeneous tasks. To manage energy harvesting adaptively and select appropriate computing modes, we devise an online resource allocation and computation offloading approach based on deep reinforcement learning. We decompose this approach into two stages: an offloading decision stage and a stopping decision stage. The purpose of the offloading decision stage is to select the computing mode and dynamically allocate the computation round length for each task after learning from the channel state information and the task experience. This stage allows the system to support heterogeneous computing tasks. Subsequently, in the second stage, we adaptively adjust the number of fading slots devoted to energy harvesting in each round in accordance with the status of each fading slot. Simulation results show that our proposed algorithm can better allocate resources for heterogeneous tasks and reduce the ratio of failed tasks and energy consumption when compared with several existing algorithms.

关键词： Servers   Cloud computing   Costs   Games   Collaboration   Bandwidth   Wireless communication   Computation offloading   cost optimization   mobile edge computing   cloud computing  

摘要： Cloud Computing (CC) is powerful for the computation offloading of services, promoting the implementation of various modern applications. Mobile Edge Computing (MEC) can provide low-latency services utilizing edge servers locating in proximity to users. The combination of MEC and CC can give play to the dual advantages of both. However, it is a challenging problem to offload service requests to the collaborative edge-cloud networks aiming at minimizing costs due to the resource limitation of edge servers and the online feature of services. To address this issue, we mathematically model the service requests with multiple inter-connected functions. Then, the problem of computation offloading of multi-function service requests in collaborative edge-cloud networks is formulated to be an Integer Linear Programming (ILP) and is proved to be NP-hard. Furthermore, a Cost-minimized Computation Offloading with Reconfiguration (CCOR) algorithm is proposed to minimize the total cost of online services. Finally, simulation results show that the proposed CCOR algorithm can effectively reduce the cost of computation offloading with higher resource utilization of edge cloud compared with baseline algorithms.