关键词： Mobile edge computing   Mobile augmented reality   Task offloading   Multi-cache placement  

摘要： As mobile augmented reality (MAR) applications continue to develop and spread, more and more data-intensive and computationally intensive tasks are sensitive to delay and energy consumption. The emergence of mobile edge computing provides an effective solution for the demand of low latency and low energy consumption. This paper studies the task offloading and multi-cache placement of MAR applications in a three-tier transmission system composed of edge servers, edge data centers and remote cloud. Under the constraint of computing resources and cache space, the task offloading and cache placement problems are formulated to maximize the energy efficiency function including task completion rate and energy consumption. To solve this problem, we design a task offloading and multi-cache placement algorithm based on block coordinate descent. Firstly, we generate the priority queue for the tasks. Then, caches are placed according to the popularity of each cache and the cache space ratio of each edge node. Tasks are offloaded according to the priority of each edge node. After the initialization, the task offloading strategy and cache placement strategy are optimized using block coordinate descent. Finally, we update the optimized cache placement strategy and task offloading strategy until the objective function converges. Simulation results show that our algorithm can significantly shorten service delay and reduce energy consumption compared with other algorithms.

关键词： Mobile edge computing   Deep reinforcement learning   Network function virtualization   Function parallelism  

摘要： This paper introduces a Deep Reinforcement Learning (DRL)-based framework to enhance Network Function Parallelism (NFP) in Mobile Edge Computing (MEC). Leveraging Network Function Virtualization (NFV), the proposed framework optimizes service delay by solving a fairness-aware throughput maximization problem for service function chain placement. It aims to maximize the long-term cumulative reward while satisfying Quality of Service (QoS) requirements. The framework also preserves resources for future requests by efficiently managing the initialized network functions distribution. Simulation results demonstrate the superior performance of the proposed framework across various metrics. Specifically, our framework improves the average delay and deployment rate by 1.2% and 2.4% compared to the existing best method. (c) 2024 The Author(s). Published by Elsevier B.V. on behalf of The Korean Institute of Communications and Information Sciences. This is an open access article under the CC BY license (http://***/licenses/by/4.0/).

关键词： Mobile edge computing   Industrial internet of things   Multiobjective optimization   Task offloading   Evolutionary multiobjective multitasking  

摘要： In mobile edge computing(MEC) scenarios, offloading computation-intensive and delay-critical tasks to nearby mobile edge servers with abundant computing resources can reduce the time delay(TD) of tasks and energy consumption(EC) of terminal devices, thus improving application performance and user experience. In order to reduce the TD of tasks and the EC of factory terminal devices(FTDs), MEC is introduced into the Industrial Internet of Things(IIoT). Because TD and EC are conflicting objectives, most existing research models the task offloading problem(TOP) as a multiobjective optimization problem to optimize both TD and EC. However, as the number of FTDs and tasks in the MEC network increases, the scale and computational cost of the TOP increases, and it is very challenging to obtain the optimal task offloading scheme through multiobjective optimization. Multitasking optimization can make use of knowledge transfer between related tasks to promote the solving efficiency of each task. Based on this, we model the TOP in IIoT as a multiobjective TOP (MTOP). A cheap task that is highly similar to MTOP is constructed, and a novel evolutionary multiobjective multitasking framework is developed, which uses the positive knowledge transfer between tasks to optimize TD and EC in the network. Then, an effective evolutionary multiobjective multitasking algorithm is proposed, which includes multi-functional knowledge transfer strategy(MFKT) and adaptive cheap task update strategy(ACTU). MFKT uses decision variables with different functions in the cheap tasks for positive knowledge transfer, so as to improve the performance of MTOP. ACTU dynamically updates the cheap task to maintain knowledge transfer between tasks. Indifferent test instances, the proposed algorithm is compared with the existing multiobjective and multitasking algorithms. The experimental results show that the proposed algorithm is more competitive in terms of TD and EC, it can guarantee the service quality of t

关键词： blockchain framework   energy efficient and secure data storage system   hybrid Battle Royale with Archimedes optimization algorithm   mobile edge computing   optimal elliptical curve cryptography   optimal location for data storing  

摘要： Internet of Things (IoT) devices is extensively employed to collect physiological health data and provide diverse services to end-users. Nevertheless, in recent applications, cloud computing-based IoT proves beneficial for standard data storage and ensuring high-security information sharing. Due to limitations in battery capacity, storage, and computing power, IoT devices are often considered resource-constrained. Consequently, data signing by IoT devices, aimed at ensuring data integrity and authentication, typically demands significant computational resources. Unsafe data storage and high latency are considered as the major issues in the IoT-based data storage mechanism for duplicating and misusing the information while it is stored in the cloud database. Hence, blockchain technologies are needed to provide high security over the stored data. Hence, the research aimed to implement an efficient blockchain-based data storage system in mobile edge computing, safeguarding data from unauthorized access. In this approach, it contains four layers that are cloud layer, the entity layer, the block-chain layer, and the edge computing layer. The user's data are stored in the optimal location in the entity layer, where the data storing location is find out using the proposed Hybrid Battle Royale with Archimedes Optimization Algorithm (HBRAOA). In the edge computing layer, an optimal key-based homomorphic encryption algorithm using Elliptic Curve Cryptography (ECC) is introduced to encrypt data with the most optimal key, ensuring secure storage. This encryption method leverages the same HBRAOA to enhance the efficiency. Next, the digital signature is demonstrated to give the authorization of the user, and it is distributed to the blockchain layer. Thus, the indexes of the shared data are stored in the blockchain layer to avoid fault tolerance and tamper-proofing. Finally, the cloud layer receives the valuable encrypted data, and the authenticated users with known encrypted key

关键词： Internet of vehicles   Mobile edge computing   Mobility-aware service migration   Hidden Markov process   Deep reinforcement learning  

摘要： The restricted coverage of edge servers in the Internet of Vehicles (IoV) results in service migration as vehicles traverse various regions, potentially escalating operational costs and diminishing service quality. However, existing service migration schemes inadequately address the dynamic attributes of high-speed mobile vehicles and the temporal variability of the network. To overcome this issue, we propose a mobility-aware deep reinforcement learning framework based on vehicle behavior prediction for service migration. Firstly, taking the service processing latency, migration latency, and energy consumption as metrics, a constrained model is established to minimize long-term costs. Given the considerable uncertainty in the associational behaviors between high-speed mobile vehicles and edge servers, a vehicle behavior prediction method utilizing the Hidden Markov Model (HMM) is then proposed. On this basis, we design a mobility-aware reinforcement learning service migration algorithm based on a Double Dueling Deep Q-Network (D3RLSM) incorporating a prioritized experience replay mechanism to extract vehicular state features accurately and optimize the training process. Compared with several baseline methods, D3RLSM shows its effectiveness in reducing service latency and energy consumption.

关键词： Cognitive semantic communication   Mobile edge computing   Unmanned aerial vehicle   Deep reinforcement learning  

摘要： Unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) networks offer a powerful solution for enhancing communication efficiency in resource-constrained environments and managing compute-intensive tasks. However, the inherent limitations of UAVs, such as restricted data storage, computation capability and battery capacity, hinder the maximum communication efficiency. For the first time, this paper investigates a semantic-aware mobile edge computing (SMEC) network, where task data is semantically compressed at the users and processed at edge computing servers. This approach aims to significantly reduce the transmission and storage overhead in UAV, and improve task performance in low signal-to-noise ratio (SNR). To further enhance transmission robustness and task performance, we incorporate a UAV-carried mobile intelligent reflecting surface (IRS). The objective is to minimize system costs while maintaining task performance, which requires the joint optimization of UAV trajectories, server pairings, user assignments, and IRS reflecting elements. This problem is NP-hard, posing significant computational challenges. To address the complexity of the formulated problem, we propose a novel cognitive UAV-IRS planning strategy based on deep reinforcement learning (DRL), where the UAV can infer the task intentions of the users. Simulation results demonstrate the effectiveness of our intelligent scheme, showing rapid convergence in solving the complex optimization problem. Comparative analysis with benchmark schemes reveals a substantial reduction in system costs and more robust task performance achieved by our proposed approach.

关键词： mobile edge computing   cover communication   unmanned aerial vehicle   deep deterministic policy gradient   prioritized experience replay  

摘要： Task offloading strategies for unmanned aerial vehicle (UAV) -assisted mobile edge computing (MEC) systems have emerged as a promising solution for computationally intensive applications. However, the broadcast and open nature of radio transmissions makes such systems vulnerable to eavesdropping threats. Therefore, developing strategies that can perform task offloading in a secure communication environment is critical for both ensuring the security and optimizing the performance of MEC systems. In this paper, we first design an architecture that utilizes covert communication techniques to guarantee that a UAV-assisted MEC system can securely offload highly confidential tasks from the relevant user equipment (UE) and calculations. Then, utilizing the Markov Decision Process (MDP) as a framework and incorporating the Prioritized Experience Replay (PER) mechanism into the Deep Deterministic Policy Gradient (DDPG) algorithm, a PER-DDPG algorithm is proposed, aiming to minimize the maximum processing delay of the system and the correct detection rate of the warden by jointly optimizing resource allocation, the movement of the UAV base station (UAV-BS), and the transmit power of the jammer. Simulation results demonstrate the convergence and effectiveness of the proposed approach. Compared to baseline algorithms such as Deep Q-Network (DQN) and DDPG, the PER-DDPG algorithm achieves significant performance improvements, with an average reward increase of over 16% compared to DDPG and over 53% compared to DQN. Furthermore, PER-DDPG exhibits the fastest convergence speed among the three algorithms, highlighting its efficiency in optimizing task offloading and communication security.

关键词： Surveys   Task analysis   Servers   Resource management   Real-time systems   Low latency communication   Tutorials   Reinforcement learning   mobile edge computing   offloading scheduling   content caching   communication  

摘要： Mobile Edge Computing (MEC) broadens the scope of computation and storage beyond the central network, incorporating edge nodes close to end devices. This expansion facilitates the implementation of large-scale "connected things" within edge networks. The advent of applications necessitating real-time, high-quality service presents several challenges, such as low latency, high data rate, reliability, efficiency, and security, all of which demand resolution. The incorporation of reinforcement learning (RL) methodologies within MEC networks promotes a deeper understanding of mobile user behaviors and network dynamics, thereby optimizing resource use in computing and communication processes. This paper offers an exhaustive survey of RL applications in MEC networks, initially presenting an overview of RL from its fundamental principles to the latest advanced frameworks. Furthermore, it outlines various RL strategies employed in offloading, caching, and communication within MEC networks. Finally, it explores open issues linked with software and hardware platforms, representation, RL robustness, safe RL, large-scale scheduling, generalization, security, and privacy. The paper proposes specific RL techniques to mitigate these issues and provides insights into their practical applications.

关键词： Cooperation   Edge server deployment   Mobile edge computing   Utility optimization  

摘要： In our exploration of Mobile Edge Computing (MEC) systems, we address the critical challenge of edge server deployment, aiming to enhance application responsiveness through optimized server placement and cooperation. Our study diverges from traditional approaches that prioritize server location, instead highlighting the untapped potential of server collaboration for sharing computing resources. This cooperative strategy not only boosts resource utilization and trims response times but also intricately complicates deployment strategies. We introduce an innovative Collaboration-Based Server Deployment (CBSD) algorithm that stands out by facilitating cooperative communication between edge servers via Base Stations (BSs), even under stringent resource constraints. This algorithm employs a dual-phase approach: initially utilizing a non-collaborative Gradient algorithm for resource allocation among cooperative regions, followed by a strategic distribution of resources based on regional demand. Our comprehensive simulations show that our proposed methodology improves system utility and throughput by 35% and 25%, respectively, while robustness reaches 90% compared to the baseline. These results represent improvement in managing limited edge resources effectively.

关键词： task offloading   Mobile edge computing   task offloading   asyn- chronous deep reinforcement learning  

摘要： With the rapid advancement of intelligent transportation systems, Road Side Units (RSUs) in the Internet of Vehicles (IoV) play a crucial role in sensing environments information for vehicles to make driving decisions and transportation management. By offloading computation tasks from RSUs to other nodes such as cloud servers, other RSUs, and vehicles, the network computation resources can be fully utilized, however, at the expense of increasing task delay. In multi-hop task offloading, tasks can be forwarded to vehicles outside the coverage area of the source RSU where tasks are generated. However, due to the movement of vehicles, stable transmission among nodes is not guaranteed, posing a challenge in determining the next node for task forwarding. In this paper, we ensure the connectivity among nodes by establishing a mobility model and design a mechanism for selecting forwarding vehicles. The goal is to find those vehicles that can communicate stably with the source RSU and determine the optimal communication path. We formulate task offloading as a 0-1 mathematical model with the objective of minimizing the task delay. Subsequently, we propose a solution based on asynchronous deep reinforcement learning A3C. Through extensive simulations, we validate the effectiveness of our approach.