关键词： Mobile edge computing  

摘要： Mobile edge computing (MEC) can reduce the latency of cloud computing successfully. However, the edge server may fail due to the hardware of software issues. When the edge server failure happens, the users who offload tasks to this server will be affected. How to recover the services for these affected users quickly and effectively is challenging. Moreover, considering that the server failure is continuous and temporary, and the failed server can be repaired, the previous works cannot handle this problem effectively. Therefore, in this paper, we propose the fast, online, distributed, and temporary failure recovery algorithm (FODT) for MEC. In FODT, when edge sever failure happens, only the affected APs recalculate their user-server allocation strategies and the other APs do not change their strategies. For the affected access points (Aps), the strategies before server failure are reused to reduce complexity and latency. When the failed server is repaired, the influenced APs reuse the strategies before server failure to offload task to this server. Based on this approach, the FODT can achieve better performance than previous works. To the best of knowledge, the FODT is the first failure recovery algorithm, and when compared with previous research, it has higher failure recovery efficiency and lower complexity with acceptable approximate ratio. © 2023, CC BY.

关键词： Mobile edge computing  

摘要： Splitting the inference model between device, edge server, and cloud can improve the performance of EI greatly. Additionally, the non-orthogonal multiple access (NOMA), which is the key supporting technologies of B5G/6G, can achieve massive connections and high spectrum efficiency. Motivated by the benefits of NOMA, integrating NOMA with model split in MEC to reduce the inference latency further becomes attractive. However, the NOMA based communication during split inference has not been properly considered in previous works. Therefore, in this paper, we integrate the NOMA into split inference in MEC, and propose the effective communication and computing resource allocation algorithm to accelerate the model inference at edge. Specifically, when the mobile user has a large model inference task needed to be calculated in the NOMA-based MEC, it will take the energy consumption of both device and edge server and the inference latency into account to find the optimal model split strategy, subchannel allocation strategy (uplink and downlink), and transmission power allocation strategy (uplink and downlink). Since the minimum inference delay and energy consumption cannot be satisfied simultaneously, and the variables of subchannel allocation and model split are discrete, the gradient descent (GD) algorithm is adopted to find the optimal tradeoff between them. Moreover, the loop iteration GD approach (Li-GD) is proposed to reduce the complexity of GD algorithm that caused by the parameter discrete. Additionally, the properties of the proposed algorithm are also investigated, which demonstrate the effectiveness of the proposed algorithms. © 2023, CC BY.

关键词： Mobile edge computing  

摘要： Mobile edge computing (MEC) is receiving more attention than centralized cloud computing due to the massive increase in transmission and compute requirements in 5G vehicle networks. It offers a significant amount of processing and storage resources to the edge of networks, offloading applications from vehicle terminals that are computation-intensive and delay-sensitive. For devices with limited resources, it uses edge resources to provide computationally heavy operations while conserving energy. This paper proposes a novel approach for computing offloading in MEC. To effectively optimize the MEC resources, this paper proposes a novel algorithm. First, the joint optimization and service cache decision subproblems were determined from continuous and discrete variables. Then, the near-optimal solution is determined from the subproblems through convex optimization and Karush-Kuhn-Tucker method. Simulation results show that the proposed algorithm has better computational offloading and resource allocation performance as compared to existing algorithms.

关键词： cache allocation policy   mobile augmented reality   mobile edge computing   reinforcement learning   user preference  

摘要： In mobile edge computing (MEC), due to the limited computing resources and power of mobile augmented reality (MAR) devices, cache identification results which can reduce power consumption and executing time of mobile devices are the solution to process MAR tasks. In this paper, we study an allocation cache problem in MAR systems. The allocation cache problem is formulated as maximizing the cache utility of cache hit ratio and user preference factor. To solve this problem, a cache resource allocation and cache space adjustment policy for edge computing systems is proposed. We also propose an improved double deep Q-network (DDQN) algorithm to learn this policy. Simulation results show that the policy greatly improves the cache hit ratio compared with the traditional caching policy.

关键词： Mobile edge computing  

摘要： This paper investigates the role and the impact of control operations for dynamic mobile edge computing (MEC) empowered by Reconfigurable Intelligent Surfaces (RISs), in which multiple devices offload their computation tasks to an access point (AP) equipped with an edge server (ES), with the help of the RIS. While usually ignored, the control aspects related to channel estimation (CE), resource allocation (RA), and control signaling play a fundamental role in the user-perceived delay and energy consumption. In general, the higher the resources involved in the control operations, the higher their reliability;however, this introduces an overhead, which reduces the number of resources available for computation offloading, possibly increasing the overall latency experienced. Conversely, a lower control overhead translates to more resources available for computation offloading but impacts the CE accuracy and RA flexibility. This paper establishes a basic framework for integrating the impact of control operations in the performance evaluation of the RIS-aided MEC paradigm, clarifying their trade-offs through theoretical analysis and numerical simulations. Copyright © 2023, The Authors. All rights reserved.

关键词： mobile edge computing   industrial internet of things   computation offloading   task queues   dueling deep Q-network  

摘要： Industrial Internet of Things (IIoT) is a promising mechanism of Industry 4.0. Mobile edge computing (MEC) is an emerging mechanism that is an enabler for IoT applications, which applies computation offloading mechanism to offload workload from IoT devices to MEC servers to enhance computing quality. This paper addresses the computation offloading problem under a heterogeneously loaded MEC-enabled IIoT network, which specifically applies one-hop offloading for devices and task queue for devices and servers. We first formulate our computation offloading problem as a Markov Decision Process, then design a dueling deep Q-network-based computation offloading method for better optimizing the value function for specific state and advantage function for specific action. Experimental results verify that our scheme reduces more energy consumption (30%), latency (50%) and task dropped rate (50%) of IIoT devices, compared with other popular methods.

关键词： PV system   WECS   ESS   edge computing enabled IoT   sharded blockchain  

摘要： An innovative cutting-edge smart grid system that integrates Internet of Things (IoT) with Blockchain technology is presented in this work. The smart grid harnesses the potential of Renewable Energy Sources (RESs) like Photovoltaic (PV) system and Wind Energy Conversion System (WECS) to meet the growing energy demands. The Edge computing enabled IoT technology and sharded blockchain is proposed to strengthen the functionality of smart grid. Moreover, the IoT infrastructure enables seamless communication and coordination between various grid components, facilitating optimal energy generation, consumption, and management. Furthermore, implementation of sharded blockchain architecture ensures secure and transparent transactions within smart grid ecosystem. The proposed smart grid system represents significant advancement in renewable energy integration, combining PV, WECS, and Energy Storage System (ESS) with edge computing enabled IoT and sharded blockchain. Through rigorous simulation and analysis, the remarkable advantages of these innovations are quantified. The sharded blockchain integration enhances transaction throughput by 300%, achieving 5000 transactions per second, while reducing confirmation latency to 3 s. It maintains 99.9% data integrity, achieves 98% privacy compliance via encryption, and exhibits robust resilience against cyber threats, securing energy-related data in the smart grid ecosystem.

关键词： Mobile edge computing  

摘要： The concept of the Metaverse has garnered growing interest from both academic and industry circles. The decentralization of both the integrity and security of digital items has spurred the popularity of play-to-earn (P2E) games, where players are entitled to earn and own digital assets which they may trade for physical-world currencies. However, these computationally-intensive games are hardly playable on resource-limited mobile devices and the computational tasks have to be offloaded to an edge server. Through mobile edge computing (MEC), users can upload data to the Metaverse Service Provider (MSP) edge servers for computing. Nevertheless, there is a trade-off between user-perceived in-game latency and user visual experience. The downlink transmission of lower-resolution videos lowers user-perceived latency while lowering the visual fidelity and consequently, earnings of users. In this paper, we design a method to enhance the Metaverse-based mobile augmented reality (MAR) in-game user experience. Specifically, we formulate and solve a multi-objective optimization problem. Given the inherent NP-hardness of the problem, we present a low-complexity algorithm to address it, mitigating the trade-off between delay and earnings. The experiment results show that our method can effectively balance the user-perceived latency and profitability, thus improving the performance of Metaverse-based MAR systems. Copyright © 2023, The Authors. All rights reserved.

关键词： IoT   Futuristic smart vertical networks   Ultra-dense IoT networks   Mobile edge computing   IoT mobile devices   Latency-sensitive tasks   Energy utilization  

摘要： As the Internet of Things (IoT) evolves and is integrated into cutting-edge Smart Vertical Networks-based IoT, a plethora of IoT mobile devices (IMD) must contend with the increasing processing demands of time-critical tasks. The dynamic nature of the environment raises novel challenges for networks that use mobile edge computing. As a proactive response to these issues, the concept of ultra-dense IoT with Mobile Edge Computing has emerged. Within this architecture, Integrated Mobile Devices (IMDs) can save power and preserve their internal processing resources by offloading compute-intensive tasks to servers located at the network's periphery (the "edge"). Nevertheless, the increased efficiency comes at the cost of greater transmission overhead, leading to an elevated delay. To achieve an ideal equilibrium between energy preservation and latency reduction, we propose a new optimization problem that focuses on minimizing both energy utilization and latency in ultra-dense IoT networks with multiple users and tasks. This issue entails the complex optimization of concurrent user (IMD) associations, computation offloading decisions, and resource allocations. To achieve a fair distribution of network load and maximize the utilization of computational resources, we integrate multi-step computation offloading methodologies into the issue formulation. Finally, the Adaptive Particle Swarm Optimization (PSO) technique is utilized as an intelligent way of solving the problem. Significantly, our methodology exhibits a noteworthy improvement over traditional Particle Swarm Optimization (PSO) techniques, resulting in a substantial decrease in overall expenses, encompassing reductions that span from 20 to 65%.

关键词： Mobile edge computing   Latency   Offloading reliability   Task offloading   Niching genetic algorithm  

摘要： In the field of mobile edge computing (MEC) research, many studies under common research scenarios focus on the optimization of energy consumption and processing delay. Meanwhile, some researchers only discussed the task offloading of terminals to a single server, without considering the collaborative execution of task offloading by multiple servers. Motivated by their observations, this paper conducted research in the scenario of offloading tasks from a single terminal to multiple edge servers. We formulated the multi-objective optimization problem of latency and offloading reliability, and proposed an intensive task offloading strategy based on the Sequential Waiting Model(SWM). The scheme allocates tasks according to the wireless channel environment and the computing power of the server. Besides, this approach minimized the combined cost consisting of latency and offloading failure probability. Since this kind of optimization problem has been proved to be an NP-hard problem. And we designed a Niching Preservation Genetic Algorithm (NPGA), which is based on Niching Genetic Algorithm (NGA). Besides, to obtain better system performance, we simulated the influence of different computing factors on the proposed algorithm NPGA and analyzed the convergence of the NPGA. Finally, the simulation results illustrated the proposed algorithm NPGA can effectively reduce the total latency of task completion and the probability of task offloading failure. Compared with the most advanced algorithms in the previous literature, the cost function value of the combined latency and offloading failure probability reduces 2%-16%. At the same time, we conducted simulation experiments using real base station locations, and also verified that the NPGA algorithm can achieve lower cost values under multiple edge server counts.