关键词： Autonomous aerial vehicles   Blockchains   Delays   Energy consumption   Optimization   Games   Security   Resource management   Privacy   Network systems   Multi-unmanned aerial vehicles   mobile edge computing   blockchain   Stackelberg game   computing offloading  

摘要： Unmanned Aerial Vehicle (UAV) is a promising technology that can serve as aerial base stations to assist the Internet of Things (IoT) network and solve various problems, such as expanding network coverage, improving network performance, transmitting energy to IoT devices, and performing IoT compute-intensive tasks. However, due to the communication between UAVs and the migration of computing tasks, privacy and security during the computing offloading process are challenging issues. To this end, we design an air-to-air multi-UAVs MEC network system based on multi-coalition game, and introduce blockchain technology to ensure privacy and security between UAVs, effectively ensuring the security and confidentiality of computing offloading between UAVs. In this paper, the joint optimization problem of UAV channel selection, UAV location deployment, block processor decision, block processor transmission power, and block processor generation frequency is studied. The goal is to minimize the weighted average sum of energy consumption and delay for MEC task computing and blockchain task processing. To handle this intractable issue, the original problem is decomposed into two subproblems and solved alternately with each other. In addition, the Joint Convex Optimization and Stackelberg Game Hierarchical (JCSH) algorithm is proposed, which solves the problem of blockchain-enabled computing offloading and resource allocation. The simulation results show that the JCSH algorithm has better performance and stronger robustness compared to other algorithms under different parameter settings.

关键词： deployment optimization   gradient-based optimizer   improved upper layer optimization   mobile edge computing   multi-UAV   task scheduling optimization  

摘要： Mobile edge computing (MEC) servers integrated with multi-unmanned aerial vehicles (multi-UAVs) present a new system the multi-UAV-assisted MEC system. This system relies on the mobility of the UAVs to reduce the transmission distance between the servers and mobile users, thereby enhancing service quality and minimizing the overall energy consumption. Achieving optimal UAV deployment and precise task scheduling is crucial for improved coverage and service quality in this system. This problem is framed as a nonconvex optimization problem known as joint task scheduling and deployment optimization. Recently, an optimization technique based on a dual-layer framework: Upper layer optimization and lower layer optimization have been proposed to tackle this problem and achieved superior performance compared to the alternative methods. In this framework, the lower layer was responsible for task scheduling optimization, while the upper layer was designed to assist in optimizing UAV deployment and thus achieving improved coverage and enhanced task scheduling for mobile users, thereby minimizing the total energy consumption. However, further refinement of upper layer optimization is needed to improve the deployment process. In this study, the upper layer undergoes enhancement through key modifications: First, random selection of the solutions is replaced with sequential selection to maintain the unique characteristics of each individual throughout the optimization process, fostering both exploration and exploitation. Second, a selection of recently reported metaheuristic algorithms, such as spider wasp optimizer (SWO), generalized normal distribution optimization (GNDO), and gradient-based optimizer (GBO), are adapted to optimize UAV deployments. Both improved upper layer and lower layer optimization led to the development of novel, more effective optimization approaches, including IToGBOTaS, IToGNDOTaS, and IToSWOTaS. These techniques are evaluated using nine instances with a

关键词： Interference   Signal to noise ratio   Optimization   Throughput   Energy efficiency   Resource management   Quality of service   Energy harvesting   Energy consumption   Multi-access edge computing   Computation offloading   fairness index   mobile edge computing   signal-to-interference-plus-noise ratio   throughput  

摘要： Mobile edge computing (MEC) improves network performance by minimizing latency and assigning computing tasks to edge servers. Nonetheless, delegating computations in environments with high device density poses considerable difficulties. Ensuring fairness in resource distribution among users is essential for preserving network stability and user satisfaction in these contexts. This research formulates the Fairness-aware Computation Offloading Optimization (FACOO) algorithm. The Lyapunov approach and sequential least squares quadratic programming (SLSQP) are used to ascertain the best offloading ratio, transmission power, and CPU frequency while complying with signal-to-interference-plus-noise ratio (SINR) limitations. Energy harvesting (EH) is built into FACOO to prolong device battery life and to ensure that MEC systems, which have limited resources, are more sustainable. The results show that FACOO greatly improves throughput and fairness while using significantly less energy, especially in settings with numerous nodes dispersed across large areas. Comprehensive simulations demonstrate that the method effectively balances fairness, throughput, and energy use, making it a workable way to improve resource allocation in MEC systems.

关键词： Mobile edge computing  

摘要： The combination of mobile edge computing (MEC) and sensing technologies, such as light detection and ranging (LiDAR), offers a viable path toward enhancing autonomous vehicle navigation and traffic monitoring in the context of intelligent transportation systems. In order to meet these needs, this article offers a methodology that investigates the use of elevated LiDAR (ELiD) and its integration with MEC. Our work focuses on two main challenges: optimizing the placement of ELiDs to ensure extensive urban coverage and minimizing network latency by efficiently routing data to MEC servers. By proposing a heuristic for real-time task allocation, we aim to enhance safety and operational efficiency in smart cities. Our findings show a modest optimality gap where the heuristic achieves a balance between computational efficiency and minimized cloud dependency, albeit at the cost of a marginally increased latency, highlighting the nuanced tradeoffs in edge-to-cloud task distribution for efficient LiDAR data processing in smart cities. © 2024 IEEE.

关键词： Mobile edge computing  

摘要： Mobile edge computing assisting vehicular networks to achieve collaborative computing is a significant research area, as it can effectively expand the on-board computational resources, thereby improving the responsiveness of vehicular applications and reducing energy consumption. However, the existing task offloading schemes still have various gaps and face challenges that should be addressed because task offloading problems are usually multi-objective optimization problems (MOOP) and NP-hard problems. In this respect, we formulate a new multi-objective model for vehicle-centered task offloading tacking into account the queuing and computing processes of tasks. The minimization of the average delay time, average energy consumption, and average payment cost for each vehicle is defined as a MOOP. We propose an efficient collaborative offloading approach based on task triage strategy and multi-objective optimization to address the model. Specifically, this approach can be decomposed into two stages. First, a task triage strategy is developed based on a comprehensive analysis of task urgency and task criticality to make offloading decisions for triaged tasks with extreme characteristics. Then, we propose a novel multi-objective optimization framework for the MOOP. Finally, we develop a multi-objective hybrid genetic algorithm that integrates the associative learning immediate memory strategy and genetic operations to solve the MOOP. Extensive experiments and performance evaluations based on real-world traces of taxis demonstrate the effectiveness of the proposed approach. The results indicate that the approach outperforms six other well-known multi-objective algorithms in solving the MOOP. © 1967-2012 IEEE.

关键词： Tuning   Adaptation models   Optimization   Scalability   Accuracy   Q-learning   Predictive models   Computational modeling   Long short term memory   Deep learning   Proactive edge caching   reinforcement learning   LSTM networks   hyperparameter tuning   network optimization   mobile edge computing   distributed networking  

摘要： The exponential growth of network traffic and data-intensive applications demands innovative solutions to manage data efficiently and ensure high-quality user experiences. Proactive edge caching has become a crucial technique for enhancing network performance by predicting and pre-storing content closer to users before access. Accurate prediction models, such as Long Short-Term Memory (LSTM) networks, are crucial for effective proactive caching. However, these models rely on carefully tuned hyperparameters to maintain predictive accuracy, and manual tuning is impractical in dynamic and diverse network environments, limiting scalability and adaptability. To overcome these challenges, we propose a novel framework, SPARCQ, that leverages Q-learning, a reinforcement learning algorithm, to automate hyperparameter tuning for LSTM-based prediction models. By dynamically adjusting hyperparameters, our approach ensures accurate predictions, improving caching efficiency and adaptability. Using the MovieLens dataset, we achieve an average improvement of 8% in cache hit ratios compared to baseline models, including popularity-based and untuned models. Additionally, our framework demonstrates scalability and robustness across geographically distributed regions, consistently adapting to diverse and evolving data patterns.

关键词： Mobile edge computing  

摘要： This paper exploits an Unmanned Aerial Vehicle assisted Mobile Edge Computing (UAV-MEC) system to meet the computation demand of User Equipments (UEs). However, in conventional UAV-MEC architecture, a UAV is dedicated only to one single service, which will inevitably limit overall system performance. To address the above issue of the conventional UAVMEC architecture, we propose a hybrid UAV-MEC model. In this model, a UAV is treated as an agent to provide either relay or computing services. Moreover, we propose an MEC Multiple Agent UAV Trajectory and User Association Policy Gradient (MEC-MAUTUA-PG) algorithm to optimize offloading strategies of the UEs as well as the trajectory design of UAV. We show the superiority of the proposed algorithm in terms of delay and the safety of trajectory design of UAV in the environment. © 1967-2012 IEEE.

关键词： Internet traffic prediction   generative adversarial network   5G   mobile edge computing   Internet traffic prediction   generative adversarial network   5G   mobile edge computing  

摘要： Recently, transformer-based learning models have shown promising results in network traffic prediction across various contexts. However, their potential in Mobile Edge Computing (MEC) environments remains largely unexplored, despite the unique requirements of MEC such as low latency and high-bandwidth communication between end-users and edge servers. This study evaluates the performance of four transformer-based learning models for Internet traffic prediction in MEC environments, comparing them against state-of-the-art models using a real-world dataset from publicly available mobile operator networks. Performance is assessed using mean squared error (MSE) and mean absolute error (MAE) as evaluation metrics. Our experiments demonstrate that the proposed GAN-based transformer model, referred to as 5GT-GAN-Trans, achieves an MSE of 0.428 and an MAE of 0.441, considerably outperforming baseline models such as FED-Former-F (MSE: 0.791, MAE: 0.606), FED-Former-W (MSE: 0.622, MAE: 0.508), Informer (MSE: 0.814, MAE: 0.649), and a standard Transformer model (MSE: 0.803, MAE: 0.640). These numerical improvements underscore the efficacy of transformer-based models for precise Internet traffic forecasting in MEC environments, offering valuable insights for improved network management and enhanced user experience.

关键词： Mobile edge computing   Federated learning   Client-edge-cloud   Personalized model   Non-independent and identically distributed  

摘要： Mobile edge computing aims to deploy mobile applications at the edge of wireless networks. Federated learning in mobile edge computing is a forward-looking distributed framework for deploying deep learning algorithms in many application scenarios. One challenge of federated learning in mobile edge computing is data heterogeneity since the unified model of federated learning performs poorly when client data are non-independent and identically distributed. Personalized federated learning can obtain amazing models in scenarios where client data are non-independent and identically distributed. This is because the personalized model captures the features of users' local data more accurately than the unified model. The personalized federated learning problem under two-tier (server-client) federated learning structures has been widely studied and applied. However, a lot of research results exhibit three distinct limitations: 1) suboptimal communication efficiency, 2) slow model convergence, and 3) underutilization of the relationships within user data, resulting in lower accuracy of personalized models. In this paper, we present the first personalized federated learning algorithm based on the client-edge-cloud structure. The edge server is responsible for model personalization and employs a learnable mixing parameter to mix the local model and the global model. We also utilize two learnable normalization parameters trained by clients to improve the performance of personalized models. Furthermore, in order to facilitate the collaboration among edge servers, we propose a similarity aggregation method to assign aggregation weights based on the Tanimoto coefficients between models. The experimental results show that the proposed algorithm not only increases the convergence speed of personalized models but also improves their testing accuracy.

关键词： Mobile edge computing  

摘要： As an emerging computing paradigm, mobile edge computing (MEC) provides processing capabilities at the network edge, aiming to reduce latency and improve user experience. Meanwhile, the advancement of containerization technology facilitates the deployment of microservice-based applications via edge node collaboration, ensuring highly efficient service delivery. However, existing research overlooks the resource contention among microservices in MEC. This neglect potentially results in inadequate resources for microservices constituting latency-sensitive applications, leading to increased response time and ultimately compromising quality of service (QoS). To solve this problem, we propose the Contention-Aware Multi-Application Microservice Deployment (CAMD) algorithm for collaborative MEC, balancing rapid response for applications with low-latency requirements and overall processing efficiency. The CAMD algorithm decomposes the overall deployment problem into manageable sub-problems, each focusing on a single microservice, then employs a heuristic approach to optimize these sub-problems, and ultimately arrives at an optimized deployment scheme through an iterative process. Finally, the superiority of the proposed algorithm is evidenced through intensive experiments and comparison with baseline algorithms. Copyright © 2024, The Authors. All rights reserved.