关键词： Mobile edge computing  

摘要： With the rapid advancement of devices requiring intensive computation—such as Internet of Things (IoT) devices, smart sensors, and wearable technology—the computational demands on individual platforms with limited resources have escalated, necessitating the offloading of the generated tasks by the devices to edge. These tasks are often real-time with strict response time requirements. Among these devices, autonomous vehicles present unique challenges due to their critical need for timely and accurate processing to ensure passenger safety. Selecting suitable servers in a heterogeneous mobile edge computing (MEC) architecture is vital to optimizing real-time task processing rates for such applications. To address this, we present an algorithmic solution to improve the allocation of heterogeneous servers to real-time tasks, aiming to maximize the number of processed tasks. By analyzing task and server characteristics in the MEC architecture, we develop the suitability-based adaptive resource selection (SARS) algorithm, which evaluates server suitability based on factors like time constraints and server capabilities. Additionally, we introduce the proactive on-demand resource allocation (PORA) algorithm, which strategically reserves computational resources to ensure availability for critical real-time tasks. We compare the proposed algorithms with several classical and state-of-the-art algorithms. Computational results demonstrate that our approach outperforms existing algorithms, processes more tasks, and effectively prioritizes urgent tasks, particularly in autonomous driving applications. Copyright © 2024, The Authors. All rights reserved.

关键词： Mobile edge computing  

摘要： Deep learning (DL) technologies can transform agriculture by improving crop health monitoring and management, thus improving food safety. In this paper, we explore the potential of edge computing for real-time classification of leaf diseases using thermal imaging. We present a thermal image dataset for plant disease classification and evaluate deep learning models, including InceptionV3, MobileNetV1, MobileNetV2, and VGG- 16, on resource-constrained devices like the Raspberry Pi 4B. Using pruning and quantization-aware training, these models achieve inference times up to 1.48x faster on Edge TPU Max for VGG16, and up to 2.13x faster with precision reduction on Intel NCS2 for MobileNetV1, compared to high-end GPUs like the RTX 3090, while maintaining state-of-the-art accuracy. Copyright © 2024, The Authors. All rights reserved.

关键词： Cloud computing   Mobile edge computing   Service placement   Service allocation   Optimization  

摘要： Mobile edge computing (MEC) is a new networking pattern that deploys resources layer by layer to yield high-quality services. The MEC reduces the response time, energy consumption, process time, and bandwidth usage while enhancing privacy, security, and communications in real-life applications. Determining the most optimal resources from the edge, fog, and cloud layers for the requested services by the users under different quality-of-service constraints is one of the most critical challenges in MEC networks, known as the service placement problem (SPP). An optimal service placement algorithm can increase the benefits of service providers and users' satisfaction. Consequently, a novel metaheuristic-based resource allocation algorithm named ERSGWO in presented in the current paper. In the ERSGWO, the reptile search algorithm (RSA) and gray wolf optimizer (GWO) are hybridized through an innovative complementary strategy. The RSA is enhanced in the ERSGWO by increasing the number of leaders. The outcome hybrid algorithm is then discretized using an intermediate phase. Likewise, a neighborhood search procedure with two new movement strategies is introduced in the ERSGWO to augment exploration and exploitation capabilities. Besides, the local departure ability is enriched through a mechanism that detects and reinitializes the trapped search agents in local optimums. Eventually, 12 MEC environments with different specifications are designed, and the ERSGWO is applied to them. The results are compared with the ARO, AOA, FOX, GWO, MRFO, RSA, SCA, STOA, TSA, APO, and EHO algorithms numerically and visually. The experimental results reveal that the ERSGWO outperformed its competitors in 97.73% of test cases.

关键词： Convolutional Neural Network   Deep Learning   Energy optimization   Internet of Things   Mobile Edge Computing  

摘要： The importance of the Internet of Things (IoT) devices is rising in this digital age. However, the high energy consumption of these devices limits theirlong-term functioning and efficiency. Task offloading technique in IoT is often used to address energy optimization. Conventional task offloading strategies lead to shorter lifespansand inefficient use of devices. Energy-aware task offloading is critical in IoT edge networks within the Mobile Edge Computing (MEC) environments to enhance resource utilization and reduce latency. This paper proposes an optimized Convolutional Neural Network (CNN) for efficient task offloading. The method efficiently predicts optimal offloading decisions using a comprehensive dataset that includes network characteristics, user behavior and resource utilization features. The optimized CNN architecture achieved notable performance with 91.075% accuracy, 91.82% precision, 91.07% recall, and 90.74% F1score. These results showed that the model ensures efficient resource allocation and extends the operational life of IoT devices. The key findings highlight the potential of Deep Learning (DL)models in contributing to the energy-aware task offloading fieldin real-time adaptive decision making within the MEC environments. © 2024 Seventh Sense Research Group.

关键词： Intelligent reconfigurable surface   Mobile edge computing   Unmanned aerial vehicle   Non-orthogonal multiple access  

摘要： Intelligent reconfigurable surface (IRS) is an emerging technology for the enhancement of spectrum and energy efficiency. We propose a novel IRS-and-unmanned aerial vehicle (UAV)-Assisted mobile edge computing (MEC) framework, where a MEC server installing on an UAV to facilitate task calculations by mobile users (MUs), and an IRS modulates channels between MUs and the UAV. Non-orthogonal multiple access (NOMA) is employed for further improving system-wide spectral efficiency. There are needs for joint optimization of multiple parameters, e.g., the task partition parameters and the transmit power of all MUs, the reflection coefficient matrix of the IRS and the movement trajectory of the UAV, and such needs raises the challenge of minimizing the long-term total energy consumption of all MUs while satisfying required transmission rate and task completion delay. We divide optimization tasks into two sub-problems and propose specific solutions respectively, i.e., relevant decisions about the UAV and MUs to be solved by deep reinforcement learning (DRL);and the reflection coefficient matrix of the IRS to be solved by block coordinate descent (BCD). A series of experiments have verified the effectiveness of the proposed communication techniques and optimization algorithms. Simulation results demonstrate that (1) NOMA-IRS technique achieves better energy efficacy compared to the cases where random IRS or no IRS is deployed and the conventional orthogonal multiple access (OMA) technique with IRS. (2) our proposed deep deterministic policy gradient (DDPG)-BCD algorithm outperforms other four benchmark algorithms in solving the complex and dynamic optimization problem.

关键词： eavesdropping   massive multiple input multiple output   mobile edge computing   partial offloading   secure computation efficiency  

摘要： In this article,the secure computation efficiency(SCE)problem is studied in a massive multipleinput multiple-output(mMIMO)-assisted mobile edge computing(MEC)*** first derive the secure transmission rate based on the mMIMO under imperfect channel state *** on this,the SCE maximization problem is formulated by jointly optimizing the local computation frequency,the offloading time,the downloading time,the users and the base station transmit *** to its difficulty to directly solve the formulated problem,we first transform the fractional objective function into the subtractive form one via the dinkelbach ***,the original problem is transformed into a convex one by applying the successive convex approximation technique,and an iteration algorithm is proposed to obtain the ***,the stimulations are conducted to show that the performance of the proposed schemes is superior to that of the other schemes.

关键词： Mobile edge computing   SAC   communication security   A-Star   UAV  

摘要： In mobile edge computing,unmanned aerial vehicles(UAVs)equipped with computing servers have emerged as a promising solution due to their exceptional attributes of high mobility,flexibility,rapid deployment,and terrain *** attributes enable UAVs to reach designated areas,thereby addressing temporary computing swiftly in scenarios where ground-based servers are overloaded or ***,the inherent broadcast nature of line-of-sight transmission methods employed by UAVs renders them vulnerable to eavesdropping ***,there are often obstacles that affect flight safety in real UAV operation areas,and collisions between UAVs may also *** solve these problems,we propose an innovative A*SAC deep reinforcement learning algorithm,which seamlessly integrates the benefits of Soft Actor-Critic(SAC)and A*(A-Star)*** algorithm jointly optimizes the hovering position and task offloading proportion of the UAV through a task offloading ***,our algorithm incorporates a path-planning function that identifies the most energy-efficient route for the UAV to reach its optimal hovering *** approach not only reduces the flight energy consumption of the UAV but also lowers overall energy consumption,thereby optimizing system-level energy *** simulation results demonstrate that,compared to other algorithms,our approach achieves superior system ***,it exhibits an average improvement of 13.18%in terms of different computing task sizes,25.61%higher on average in terms of the power of electromagnetic wave interference intrusion into UAVs emitted by different auxiliary UAVs,and 35.78%higher on average in terms of the maximum computing frequency of different auxiliary *** for path planning,the simulation results indicate that our algorithm is capable of determining the optimal collision-avoidance path for each auxiliary UAV,enabling them to safely reach their designated endpoints in div

关键词： Digital twin network slicing   mobile edge computing   the expected age of information (EAoI)   inference service models   cost modeling   DT and inference service instance placements   Digital twin network slicing   mobile edge computing   the expected age of information (EAoI)   inference service models   cost modeling   DT and inference service instance placements  

摘要： The advance of Digital Twin (DT) technology sheds light on seamless cyber-physical integration with the Industry 4.0 initiative. Through continuous synchronization with their physical objects, DTs can power inference service models for analysis, emulation, optimization, and prediction on physical objects. With the proliferation of DTs, Digital Twin Network (DTN) slicing is emerging as a new paradigm of service providers for differential quality of service provisioning, where each DTN is a virtual network that consists of a set of inference service models with source data from a group of DTs, and the inference service models provide users with differential quality of services. Mobile Edge Computing (MEC) as a new computing paradigm shifts the computing power towards the edge of core networks, which is appropriate for delay-sensitive inference services. In this paper we consider Age of Information (AoI)-aware inference service provisioning in an MEC network through DTN slicing requests, where the accuracy of inference services provided by each DTN slice is determined by the Expected Age of Information (EAoI) of its inference model. Specifically, we first introduce a novel AoI-aware inference service framework of DTN slicing requests. We then formulate the expected cost minimization problem by jointly placing DT and inference service model instances, and develop efficient algorithms for the problem, based on the proposed framework. We also consider dynamic DTN slicing request admissions where requests arrive one by one without the knowledge of future arrivals, for which we devise an online algorithm with a provable competitive ratio for dynamic request admissions, assuming that DTs of all objects have been placed already. Finally, we evaluate the performance of the proposed algorithms through simulations. Simulation results demonstrate that the proposed algorithms are promising, and the proposed online algorithm improves the number of admitted requests by more than 6%

关键词： large language models   retrieval augmented generation   microservice deployment   mobile edge computing  

摘要： Large Language Models (LLMs) have demonstrated impressive capabilities in autogenerating code based on natural language instructions provided by humans. We observed that in the microservice models of edge computing, the problem of deployment latency optimization can be transformed into an NP-hard mathematical optimization problem. However, in the real world, deployment strategies at the edge often require immediate updates, while human-engineered code tends to be lagging. To bridge this gap, we innovatively integrated LLMs into the decision-making process for microservice deployment. Initially, we constructed a private Retrieval Augmented Generation (RAG) database containing prior knowledge. Subsequently, we employed meticulously designed step-by-step inductive instructions and used the chain of thought (CoT) technique to enable the LLM to learn, reason, reflect, and regenerate. We decomposed the microservice deployment latency optimization problem into a collection of granular sub-problems (described in natural language), progressively providing instructions to the fine-tuned LLM to generate corresponding code blocks. The generated code blocks underwent integration and consistency assessment. Additionally, we prompted the LLM to generate code without the use of the RAG database for comparative analysis. We executed the aforementioned code and comparison algorithm under identical operational environments and simulation parameters, conducting rigorous result analysis. Our fine-tuned model significantly reduced latencies by 22.8% in handling surges in request flows, 37.8% in managing complex microservice types, and 39.5% in processing increased network nodes compared to traditional algorithms. Moreover, our approach demonstrated marked improvements in latency performance over LLMs not utilizing RAG technology and reinforcement learning algorithms reported in other literature. The use of LLMs also highlights the concept of symmetry, as the symmetrical structure of inpu

关键词： Mobile edge computing   Computation offloading   Load balancing   Cooperative topology   Multi-agent deep reinforcement learning  

摘要： Mobile Edge Computing (MEC) is a new computing paradigm that has shown great potential. How to extract the cooperative topological relationship between MEC servers to realize jointly computing is the key problem to solve the bottleneck of MEC computational capability. In previous studies, multi-MEC servers are regarded as unit computing nodes with the same cooperation relationship to jointly schedule offloading tasks, without considering the hierarchical and clustered topology of the server collaborative work. As a result, in the scenario of unbalanced distribution of computing resources, it is difficult to obtain the optimal joint scheduling strategy for offloading tasks according to the cooperation relationship and resource differences among MEC servers. Therefore, this paper considers introducing the topological relationship of group cooperation among multi-MEC servers to optimize the joint scheduling strategy, and proposes a Multi-Agent Hierarchical Graph Attention Soft Actor-Critic algorithm (MHSAC). Firstly, based on the differences in their own resources and the demands of the tasks they undertake, MEC servers are divided into series clusters. Then, a Hierarchical Graph Attention Network (HGAT) is used to model each agent to extract the physical communication topology information of the MEC server and the group topology information of multi-edge cooperation. The multi-agent soft Actor-Critic algorithm is used to obtain the offloading scheduling decision of multi-edge cooperation. Experiments show that the MHSAC algorithm that considering the topological relationship of multi-edge group cooperation can optimize load distribution under low latency and resource-limited requirements, achieving optimal load balancing values and task drop rates.