关键词： unmanned aerial vehicle   mobile edge computing   computation offloading   deep deterministic policy gradient   K-nearest neighbor  

摘要： Unmanned aerial vehicles (UAVs) are widely used to improve the coverage and communication quality of wireless networks and assist mobile edge computing (MEC) due to their flexible deployments. However, the UAV-assisted MEC systems also face challenges in terms of computation offloading and trajectory planning in the dynamic environment. This paper employs deep reinforcement learning to jointly optimize the computation offloading and trajectory planning for UAV-assisted MEC system. Specifically, this paper investigates a general scenario where multiple pieces of user equipment (UE) offload tasks to a UAV equipped with a MEC server to collaborate on a complex job. By fully considering UAV and UE movement, computation offloading ratio, and blocked relations, a joint computation offloading and trajectory optimization problem is formulated to minimize the maximum computational delay. Due to the non-convex nature of the problem, it is converted into a Markov decision process, and solved by the deep deterministic policy gradient (DDPG) algorithm. To enhance the exploration capability and stability of DDPG, the K-nearest neighbor (KNN) algorithm is employed, namely KNN-DDPG. Moreover, the prioritized experience replay algorithm, where the constant learning rate is replaced by the decaying learning rate, is utilized to enhance the converge. To validate the effectiveness and superiority of the proposed algorithm, KNN-DDPG is compared with the benchmark DDPG algorithm. Simulation results demonstrate that KNN-DDPG can converge and achieve 3.23% delay reduction compared to DDPG.

关键词： Servers   Task analysis   Resource management   Cost accounting   Approximation algorithms   Computational modeling   Multitasking   Mobile edge computing   truthfulness   resource allocation   multi-minded   algorithm design  

摘要： In the context of mobile edge computing (MEC), it can be quite challenging to provide and allocate multiple resources from heterogeneous MEC servers for remote execution of tasks of mobile devices (MDs). However, obtaining more resources for tasks can save time and effort, and MDs are willing to pay higher prices for more resources. Motivated by these challenges, this study addresses the problem of heterogeneous resource allocation with multi-minded (HRAM) in MEC. Unlike other studies that have focused on MDs with single-minded demands, this study considers the multi-attribute demands of MDs. It considers cases where an MD declares multiple demands, each with a different bid attached to it. This multi-minded approach gives MDs more flexibility and control over the resources they receive, leading to increased satisfaction and better outcomes. However, MDs are self-interested and can misreport their preferences, which results in a low utilization rate of heterogeneous resources. Therefore, we have formulated this problem in an auction-based setting, and our objective is to allocate heterogeneous resources of heterogeneous MEC servers to maximize social welfare, which is the sum of MDs' valuations. We demonstrate that the HRAM problem is NP-hard, proposing a randomized mechanism consisting of a second-price auction and a fixed-price auction. This study claims that the proposed randomized mechanism is universally truthful and that the MDs have no desire to misreport their demands. Additionally, we analyze the randomized mechanism's time complexity and approximation ratio and present the experimental results to support our claim. We demonstrate that the randomized mechanism performs excellently in different environments, benefiting MDs and edge cloud providers.

关键词： Mobile edge computing  

摘要： As mobile devices increasingly become focal points for advanced applications, edge computing presents a viable solution to their inherent computational limitations, particularly in deploying large language models (LLMs). However, despite the advancements in edge computing, significant challenges remain in efficient training and deploying LLMs due to the computational demands and data privacy concerns associated with these models. This paper explores a collaborative training framework that integrates mobile users with edge servers to optimize resource allocation, thereby enhancing both performance and efficiency. Our approach leverages parameter-efficient fine-tuning (PEFT) methods, allowing mobile users to adjust the initial layers of the LLM while edge servers handle the more demanding latter layers. Specifically, we formulate a multi-objective optimization problem to minimize the total energy consumption and delay during training. We also address the common issue of instability in model performance by incorporating stability enhancements into our objective function. Through novel fractional programming technique, we achieve a stationary point for the formulated problem. Simulations demonstrate that our method reduces the energy consumption as well as the latency, and increases the reliability of LLMs across various mobile settings. Copyright © 2024, The Authors. All rights reserved.

关键词： Mobile edge computing   PPO algorithm   Task offloading   GPS-free   Deep reinforcement learning  

摘要： Mobile Edge Computing (MEC) has gained significant attention in recent years due to its capability of providing computation and storage resources closer to end-users. However, existing research has focused on addressing MEC resource optimization problems with explicit location information and has not considered the challenges posed by users who refuse to grant system access to Global Positioning System (GPS) information. To address this issue, this paper models the location-protected MEC problem as an Integer Non-Linear Problem (INLP) and proposes a novel mechanism to cater to various types of users while optimizing both computation time and service latency. This problem is proposed to be solved by decoupling it into a path recommendation problem and a task offloading problem. For different user types, a Proximal Policy Optimization (PPO) algorithm is proposed to recommend user paths. By following these paths, multi-user interference is minimized. Then, the Deep Q-Network (DQN) algorithm is employed to determine MEC offloading strategies to solve the task offloading problem. Experimental results demonstrate that the algorithm outperforms related approaches across multiple dimensions, including computation time, service latency, and stability. Additionally, the proposed mechanism effectively addresses the offloading challenge for privacy-conscious users in edge computing scenarios. The proposed mechanism has several potential applications, such as resource optimization in mobile cloud computing, the Internet of Things (IoT), and smart cities. It also addresses optimization problems for various types of users in mobile edge computing technology.

关键词： game theory   mobile edge computing   Nash equilibrium   space-air-ground integrated network   task offloading  

摘要： Due to geographical factors, it is impossible to build large-scale communication network infrastructure in remote areas, which leads to poor network communication quality in these areas and a series of delay-sensitive tasks cannot be timely processed and responded. Aiming at the problem of limited coverage in remote areas, the space-air-ground integrated networks (SAGIN) combined with mobile edge computing (MEC) can provide low latency and high reliability transmission for offloading delay-sensitive tasks for users in remote areas. Considering the strong limitation of satellite resources in the space-ground integrated network and insufficient energy of local user equipment, firstly, a satellite-UAV cluster-ground three-layer edge computing network architecture is proposed in this paper. Under the condition that the delay requirements of various ground tasks are met, the task offloading problem is transformed into a Stackelberg game between ground user equipment and edge servers. In addition, it is proved that the existence of Nash equilibrium in non-cooperative game between ground user equipment by using potential game. Finally, a Nash equilibrium iterative offloading algorithm based on Stackelberg game (NEIO-SG) is proposed to find the optimal offloading strategy for tasks to minimize the system offloading cost and the optimal forwarding percentage strategy for offloading tasks to maximize the utility function of the edge server. Simulation results show that compared to other baseline algorithms, NEIO-SG reduces the total system latency during task offloading by about 13%$$ \% $$ and the energy consumption of the edge server by about 35%$$ \% $$.

关键词： Unmanned aerial vehicle relay   High-speed rail communication   Mobile edge computing   Dual-band communication   Resource allocation   Deep reinforce learning  

摘要： With the continuous development of communication and computation technologies, large bandwidth services place higher demands on computing capacity and throughput in High-Speed Rail (HSR) scenarios. On the one hand, Millimetre-Wave enables high data transmission rates, but leads to high Doppler frequency deviation as well as large path loss. On the other hand, the development of Mobile Edge Computing greatly alleviates user computing congestion. In this paper, we propose the Adaptive Joint Communication and Computation Resource Allocation scheme to solve the energy optimization problem of a dual-band UAV and Mobile Relay relay-assisted HSR offloading system. This scheme works well to optimize the performance of the system through resource allocation. In addition, since the optimization problem is modeled as a Mixed-Integer Nonlinear Program, we propose the Pre- and Post-state connected Parameterized Deep Q-Network algorithm, which is based on Deep Reinforcement Learning approach, for offloading decision and bandwidth resource allocation. Simulation results show that the proposed algorithms result in lower system energy consumption, while ensuring a high task completion rate as well.

关键词： Mobile edge computing  

摘要： In future 6G networks, anti-jamming will become a critical challenge, particularly with the development of intelligent jammers that can initiate malicious interference, posing a significant security threat to communication transmission. Additionally, 6G networks have introduced mobile edge computing (MEC) technology to reduce system delay for edge user equipment (UEs). Thus, one of the key challenges in wireless communications is minimizing the system delay while mitigating interference and improving the communication rate. However, the current fixed-position antenna (FPA) techniques have limited degrees of freedom (DoF) and high power consumption, making them inadequate for communication in highly interfering environments. To address these challenges, this paper proposes a novel MEC anti-jamming communication architecture supported by mobile antenna (MA) technology. The core of the MA technique lies in optimizing the position of the antennas to increase DoF. The increase in DoF enhances the system's anti-jamming capabilities and reduces system delay. In this study, our goal is to reduce system delay while ensuring communication security and computational requirements. We design the position of MAs for UEs and the base station (BS), optimize the transmit beamforming at the UEs and the receive beamforming at the BS, and adjust the offloading rates and resource allocation for computation tasks at the MEC server. Since the optimization problem is a non-convex multi-variable coupled problem, we propose an algorithm based on penalty dual decomposition (PDD) combined with successive convex approximation (SCA). The simulation results demonstrate that the proposed MA architecture and the corresponding schemes offer superior anti-jamming capabilities and reduce the system delay compared to FPA. Copyright © 2024, The Authors. All rights reserved.

关键词： Mobile edge computing  

摘要： Edge computing is deemed a promising technique to execute latency-sensitive applications by offloading computation-intensive tasks to edge servers. Extensive research has been conducted in the field of end-device to edge server task offloading for several goals, including latency minimization, energy optimization, and resource optimization. However, few of them consider our mobile computing devices (smartphones, tablets, and laptops) to be edge devices. In this paper, we propose a novel multi-tier edge computing framework, which we refer to as M-TEC, that aims to optimize latency, reduce the probability of failure, and optimize cost while accounting for the sporadic failure of personally owned devices and the changing network conditions. We conduct experiments with a real testbed and a real commercial CBRS 4G network, and the results indicate that M-TEC is capable of reducing the end-to-end latency of applications by at least 8% compared to the best baseline under a variety of network conditions, while providing reliable performance at an affordable cost. © 2024, CC BY.

关键词： Internet of Things   Collaboration   Protocols   Pricing   Resource management   Wireless communication   Network architecture   Age of information (AoI)   cross-technology offloading   federated learning   heterogeneous IoT   multi-agent deep reinforcement learning   mobile edge computing  

摘要： Heterogeneous Internet of Things (IoT) networks, which operate using various protocols and spectrum bands like WiFi, Bluetooth, Zigbee, and LoRa, bring many opportunities to collaborate and achieve timely data collection. However, several challenges must be addressed due to heterogeneous data patterns, coverage, spectrum bands, and mobility. This paper introduces a cross-technology IoT network architecture design that facilitates collaboration between service providers (SPs) to share their spectrum bands and offload computing tasks from heterogeneous IoT devices using multi-protocol mobile gateways (M-MGs). The objective is to minimize the age of information (AoI) and energy consumption by jointly optimizing collaboration between M-MGs and SPs for bandwidth allocation, relaying, and cross-technology data scheduling. A pricing mechanism is presented to incentivize different levels of collaboration and matching between M-MGs and SPs. Given the uncertainty due to mobility and task requests, we design a cross-technology federated matching algorithm (CT-Fed-Match) based on a multi-agent actor-critic approach in which M-MGs and SPs learn their strategies in a distributed manner. Furthermore, we incorporate federated learning to enhance the convergence of the learning process. The numerical results demonstrate that our CT-Fed-Match-RC algorithm with cross-technology and relaying collaboration reduces the AoI by 30 times and collects 8 times more packets than existing approaches.

关键词： Mobile edge computing   Cloud   Task scheduling   Microservices   Optimization   Container  

摘要： In recent years, mobile applications have proliferated across domains such as E-banking, Augmented Reality, E-Transportation, and E-Healthcare. These applications are often built using microservices, an architectural style where the application is composed of independently deployable services focusing on specific functionalities. Mobile devices cannot process these microservices locally, so traditionally, cloud-based frameworks using cost-efficient Virtual Machines (VMs) and edge servers have been used to offload these tasks. However, cloud frameworks suffer from extended boot times and high transmission overhead, while edge servers have limited computational resources. To overcome these challenges, this study introduces a Microservices Container-Based Mobile Edge Cloud Computing (MCBMEC) environment and proposes an innovative framework, Optimization Task Scheduling and Computational Offloading with Cost Awareness (OTSCOCA). This framework addresses Resource Matching, Task Sequencing, and Task Scheduling to enhance server utilization, reduce service latency, and improve service bootup times. Empirical results validate the efficacy of MCBMEC and OTSCOCA, demonstrating significant improvements in server efficiency, reduced service latency, faster service bootup times, and notable cost savings. These outcomes underscore the pivotal role of these methodologies in advancing mobile edge computing applications amidst the challenges of edge server limitations and traditional cloud-based approaches.