关键词： Mobile edge computing   Knowledge sharing algorithm   Harmony search algorithm   Task scheduling   Multi-UAV deployment  

摘要： In the realm of multi-UAV-assisted mobile edge computing (MEC), the joint optimization of UAV deployment and task scheduling has emerged as a potent approach for mitigating energy consumption while ensuring highquality services for mobile users. Nevertheless, the intricate interdependence between UAV deployment and task scheduling presents formidable challenges when implementing this scheme. In response, we introduce a novel methodology called the Dual-Stage Hybrid Strategy with Genetic-Simulated Annealing and Knowledge Sharing (DSHSGSK). DSHSGSK employs an alternating optimization strategy known as HS-GSK, supplemented by an elimination operator, to simultaneously optimize UAV deployment and task scheduling. In pursuit of heightened efficiency in UAV deployment and a more effective task scheduling strategy, we propose an innovative location encoding scheme for UAVs. To harness the local search capabilities of the Harmony search (HS) algorithm and the global search capabilities of the Gaining Sharing Knowledge based Algorithm (GSK), we introduce a coordination parameter (CP) to modulate the frequency of alternation between these two optimization techniques. Experimental results validate the superiority of our proposed method over other state-of-theart algorithms for jointly optimizing multi-UAV deployment and task scheduling. Notably, our approach excels in terms of energy consumption, underscoring its exceptional performance.

关键词： Intelligent reflecting surface   Mobile edge computing   Power transfer   Information security  

摘要： In this paper,we study an Intelligent Reflecting Surface(IRS)assisted Mobile Edge Computing(MEC)system under eavesdropping threats,where the IRS is used to enhance the energy signal transmission and the offloading performance between Wireless Devices(WDs)and the Access Point(AP).Specifically,in the proposed scheme,the AP first powers all WDs with the wireless power transfer through both direct and IRS-assisted ***,powered by the harvested energy,all WDs securely offload their computation tasks through the two links in the time division multiple access *** determine the local and offloading computational bits,we formulate an optimization problem to jointly design the IRS's phase shift and allocate the time slots constrained by the security and energy *** cope with this non-convex optimization problem,we adopt semidefinite relaxations,singular value decomposition techniques,and Lagrange dual ***,we propose a dichotomy particle swarm algorithm based on the bisection method to process the overall optimization problem and improve the convergence *** numerical results illustrate that the proposed scheme can boost the performance of MEC and secure computation rates compared with other IRS-assisted MEC benchmark schemes.

关键词： Mobile edge computing   Task offloading   Long short-term memory   Deep reinforcement learning   Recurrent neural networks   Energy efficiency   Latency  

摘要： Edge computing is a novel and potential computing model which moves storage and computing capabilities to the network edge, substantially decreasing service latency and network traffic. The existing Internet of Things (IoT) network offloading algorithm faces limitations such as a fixed number of applications, high edge-to-edge delay, and reliance on a single Mobile Edge Computing (MEC) server, posing security and privacy concerns. Moreover, resource-constrained mobile devices need more effective data integration and compression strategies. Addressing these challenges, this study suggests an approach based on deep reinforcement learning (DRL), specifically the SAV (State Action Value) Oriented QL (Q-Learning) based Task Offloading method, to optimise task offloading and resource allocation in edge-cloud computing. The model aims to empower Mobile Devices (MDs) to develop optimal offloading decisions for long-term Quality Perception, utilising a neural network to establish the relationship between MD state and action value. The paper introduces a Recurrent Extended Memory Network (REMN) to capture dynamic workload behaviour at Edge nodes (ENs). It incorporates Quality Mapping, Quality Estimation, and a Quality-Aware DRL Task Offloading Algorithm to improve the accuracy and efficiency of the offloading procedure in MEC systems. This systematic approach improves overall system performance and enables MDs to leverage ENs for neural network training, reducing computational burdens. As a result, it can accomplish a more significant number of tasks, reducing latency from 0.74 ms to 7.168 ms and decreasing energy consumption from 270 J to 1820.39 J for tasks ranging from 10 to 50, respectively.

关键词： Digital twin   intelligence system   machine learn- ing   mobile edge computing   unmanned aerial vehicle  

摘要： Nowadays, the functional integration of digital twin (DT) technology and artificial intelligence (AI) methodologies has enabled reliable predictions of many random processes, supporting efficient control and optimization procedures. In line with this trend, this paper explores the joint use of these technologies in an AI-empowered DT framework for an unmanned aerial vehicle-aided multi-access edge computing (UAV-MEC) system. Specifically, this approach defines an intelligent UAVMEC system capable of significantly improving service quality and deployment flexibility. The focus is on a UAV-MEC network consisting of multiple elementary service areas, where DTs efficiently orchestrate and reduce congestion levels by utilizing UAVs with onboard processing capabilities. A potential architecture for the DTs is outlined, conceptualizing each DT as a collection of basic cyber entities. Additionally, a suitable framework utilizing a matching game approach is proposed to effectively manage task offloading, channel allocation, and the dynamic assignment of UAV support to congested service zones within the same area. Finally, comprehensive simulation results validate the efficacy of the proposed intelligent UAV-MEC system, as indicated by metrics such as task completion delay and accuracy in congestion prediction.

关键词： Task analysis   Resource management   Optimization   Uplink   Delays   Fading channels   Computer architecture   Mobile edge computing   user-centric network   task offloading   resource allocation  

摘要： In the traditional cellular-based mobile edge computing (MEC), users at the edge of the cell are prone to suffer severe inter-cell interference and signal attenuation, leading to low throughput even transmission interruptions. Such edge effect severely obstructs offloading of tasks to MEC servers. To address this issue, we propose user-centric mobile edge computing (UCMEC), a novel MEC architecture integrating user-centric transmission, which can ensure high throughput and reliable communication for task offloading. Then, we formulate an long-term delay minimization problem by jointly optimizing task offloading, power allocation, and computing resource allocation in UCMEC. To solve the intractable problem, we propose two decentralized joint optimization schemes based on multi-agent deep reinforcement learning (MADRL) and convex optimization, which consider both cooperation and non-cooperation among network nodes. Simulation results demonstrate that the proposed schemes in UCMEC can significantly improve the uplink transmission rate by at least 176.99% and reduce the long-term average total delay by at least 16.36% compared to traditional cellular-based MEC.

关键词： Mobile edge computing   Multimedia   Multirate   Resource allocation   Schedule   Throughput  

摘要： Next-generation multimedia and vehicular applications aim to support streaming services while in transit. Roadside units such as cloudlets and remote radio heads (RRHs) of fifth/sixth generation cellular networks are scheduled to provide edge computing and caching functions for high quality experience. Multimedia streaming technology supports users by transmitting video data for viewing. If the buffered data is short, a playing lag occurs. However, if a larger amount of data is buffered, there is a possibility that users might not even watch, causing the network resource waste. Therefore, this research determines how to balance the trade-off between transmitting and playing data. Furthermore, the diverse quality of signals causes various throughput such that how to assign a high throughput position to transmit data is also considered for vehicular streaming transmission. The study also addresses the caching and scheduling problem to maximize the minimal delay difference among users in highway transmission. The main contribution of this work includes: (1) improve the efficiency of cache resource allocation, (2) adopt on moving speed and traffic conditions for high bitrate schedule, and (3) outperform the existing methods according to the delay differential and number of lag times.

关键词： 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： Privacy   Task analysis   Entropy   Protection   Wireless communication   Delays   Cloud computing   Mobile edge computing   computation offloading   privacy protection   deep reinforcement learning  

摘要： The emergence of mobile edge computing (MEC) imposes an unprecedented pressure on privacy protection, although it helps the improvement of computation performance including energy consumption and computation delay by computation offloading. To this end, we concern about the privacy protection in the MEC system with a curious edge server. We present a deep reinforcement learning (DRL)-driven computation offloading strategy designed to concurrently optimize privacy protection and computation cost. We investigate the potential privacy breaches resulting from offloading patterns, propose an attack model of privacy theft, and correspondingly define an analytical measure to assess privacy protection levels. In pursuit of an ideal computation offloading approach, we propose an algorithm, SAC-PP, which integrates actor-critic, off-policy, and maximum entropy to improve the efficiency of learning processes. We explore the sensitivity of SAC-PP to hyperparameters and the results demonstrate its stability, which facilitates application and deployment in real environments. The relationship between privacy protection and computation cost is analyzed with different reward factors. Compared with benchmarks, the empirical results from simulations illustrate that the proposed computation offloading approach exhibits enhanced learning speed and overall performance.

关键词： Servers   Resource management   Task analysis   Optimization   Global Positioning System   Measurement   Computational modeling   Age of Information   mobile edge computing   generalized processor sharing  

摘要： Mobile Edge Computing (MEC) is expected to play a significant role in the development of 6G networks, as new applications such as cooperative driving and eXtended Reality (XR) require both communication and computational resources from the network edge. However, the limited capabilities of edge servers may be strained to perform complex computational tasks within strict latency bounds for multiple clients. In these contexts, both maintaining a low expected Age of Information (AoI) and guaranteeing a low Peak AoI (PAoI) even in the worst case may have significant user experience and safety implications. In this work, we investigate a theoretical model of a MEC server, deriving the expected AoI and the PAoI and latency distributions under the First In First Out (FIFO) and Generalized Processor Sharing (GPS) resource allocation policies. We consider both synchronized and unsynchronized systems, and draw insights on the robust design of resource allocation policies from the analytical results, as well as considering the dimensioning of MEC capabilities in two realistic 6G use cases.

关键词： LEO satellite communication   Lyapunov optimization   Mobile edge computing   Satellite edge computing  

摘要： Despite the rapid growth of the Internet industry, the provision of full Internet service to remote regions is still challenging. As a solution, the combination of Low Earth Orbit (LEO) satellite communication and Mobile Edge Computing (MEC) is gaining attention. However, considering the high speed of LEO satellites in network environments remains a significant challenge. To this end, this paper introduces a dynamic computation offloading and resource allocation framework in the LEO satellite MEC architecture. Using Lyapunov optimization, we propose an efficient DCOOL algorithm to minimize average power consumption and propagation delay constrained by queue stability. Finally, comparative analysis and simulations demonstrate the superior performance of DCOOL while achieving lower power consumption and stable workload processing. (c) 2024 The Authors. 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-NC-ND license (http://***/licenses/by-nc-nd/4.0/).