关键词： Mobile edge computing   STAR-RISs   Multi-cell communication systems   Secure communications   MADRL   DDPG  

摘要： The poor propagation environment between user equipments (UEs) and mobile edge computing (MEC) servers may lead to UEs' high energy consumptions and task delay. Reconfigurable intelligent surface (RIS) can significantly improve the quality of wireless propagation environment by intelligently adjusting the phase-shifts and amplitudes of its reflecting elements. However, the passive or active RISs can only reflect signals, i.e., the transmitters and receivers must be located on the same side of RISs. Simultaneous transmitting and reflecting (STAR) RIS, which can transmit and reflect incident signals simultaneously, has been recognized a promising solution to solve the above problem. For STAR-RIS-aided multi-cell secure communications MEC systems, we first formulate an optimization problem to minimize the sum of weighted delay and energy consumptions of all UEs, by jointly optimizing the phase-shifts and amplitudes of STAR-RISs and system spectrum and computation resource allocations. Second, under the practical coupled phase-shift model of STAR-RIS, we develop a multi-agent deep reinforcement learning (MADRL) based scheme, where the access point (AP) in each cell acts an agent to decide phase-shifts, amplitudes, computation task offloading proportions, etc, for the STAR-RIS and UEs within its coverage based on deep deterministic policy gradient (DDPG), by taking into account interference among multiple cells and possible information leakage of UEs. Finally, we verify and evaluate the performance of the proposed scheme through extensive simulations. The simulation results show that our proposed MADRL-based scheme outperforms the existing benchmarks by employing multiple agents and considering co-channel interference among cells. Moreover, simulation results also show that the performance of our developed scheme can indeed be improved by using STAR-RISs.

关键词： Mobile edge computing   Computation offloading   System-centric energy efficiency   Latency   Optimization  

摘要： Mobile edge computing (MEC) enables computation tasks of end users to be offloaded and processed at nearby edge servers, which is a promising technology that can effectively alleviate end users' computing pressure. However, how to balance two important metrics for MEC computation offloading, i.e., latency and energy efficiency, is still a key challenge. In this paper, we investigate joint computation offloading and resource allocation for latency-sensitive applications in order to maximize system-centric computation energy efficiency (SCEE) in MEC systems. An optimization problem is formulated to maximize the sum computation energy efficiency of all users which contains the energy consumption at both user and edge server sides. Meanwhile, task completion latency constraints for all users are considered. We decompose the original optimization problem into two tractable subproblems. The optimal computation offloading as well as communication and computing resource allocation solutions are obtained by solving these subproblems. Furthermore, a hierarchical system-centric energy efficient computation offloading and resource allocation (SEECR) algorithm is developed. Numerical results show that compared with the benchmark schemes, the proposed algorithm can effectively improve the computation energy efficiency performance while guaranteeing the latency requirements of all users.

关键词： Mobile edge computing   Unmanned aerial vehicle   Multi-agent deep reinforcement learning   Trajectory optimization   Offloading strategy  

摘要： As a new network architecture, the air-ground cooperative network is a support for future 6G network to achieve ubiquitous connectivities. To effectively relieve the computational pressure of massive data in 6G wireless networks, Unmanned Aerial Vehicles (UAVs) equipped with Mobile Edge Computing (MEC) servers have become an emerging technology that provides computing resources for Mobile Devices (MDs). Due to limited on-board energy and computational capabilities, this paper investigates a multi-UAV collaborative assisted MEC architecture. The optimization problem of minimizing the total computational cost is constructed by jointly optimizing the UAVs trajectories and MDs offloading strategies scheduling. The coupling between the optimization variables and the non-convexity of the problem can make it difficult to solve directly. To address the above concerns, the non-convex optimization problem is converted into a Markov decision process. The UAVs-assisted Offloading Strategy based on Reinforcement Learning (UOS-RL) algorithm is proposed to address the convergence difficulties caused by the high-dimensional continuous action space. Furthermore, the experience data generated by agents interacting with the environment is highly differentiated due to the highly dynamic variation of the environment. Hence, a Priority Experience Replay (PER) mechanism is proposed to improve the training efficiency of the UOS-RL algorithm based on the priority of the experience data. Simulation results show that the proposed PER-UOS-RL algorithm outperforms the existing works in terms of the computational cost.

关键词： Mobile edge computing  

摘要： With the development of communication and computation technologies, mobile edge computing (MEC) has become an emerging technology being deployed in 5G/B5G mobile networks to improve the mobile user experience. In this paper, we study the asymptotic performance of parallel computing in MEC networks with massive multi-input and multi-output (MIMO) in millimeter wave band. First, a massive MIMO enabled MEC network with a parallel-limited computing model is constructed, and the task completed probability is defined as a new network performance metric. In the proposed model, we derive the task offloading and computing probabilities via the probability generation functional of the Poisson point process, from which, the task completed probability is obtained. Then we propose a computing resource allocation optimization algorithm to maximize the task completed probability within a given delay constraint. The numerical simulation results prove the accuracy of the obtained theoretical results and verify the outperformance of the proposed algorithm. © 2024, The Authors. All rights reserved.

关键词： Internet of Things   mobile edge computing   unmanned aerial vehicle   age of information   clustering   trajectory  

摘要： With the development of the Internet of Things (IoT) technology, massive amounts of sensor data in applications such as fire monitoring need to be transmitted to edge servers for timely processing. However, there is an energy-hole phenomenon in transmitting data only through terrestrial multi-hop networks. In this study, we focus on the data collection task in an unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) network, where a UAV is deployed as the mobile data collector for the ground sensor nodes (SNs) to ensure high information freshness. Meanwhile, the UAV is equipped with an edge server for data caching. We first establish a rigorous mathematical model in which the age of information (AoI) is used as a measure of information freshness, related to both the data collection time and the UAV's flight time. Then a mixed-integer non-convex optimization problem is formulated to minimize the peak AoI of the collected data. To solve the problem efficiently, we propose an iterative two-step algorithm named the AoI-minimized association and trajectory planning (AoI-MATP) algorithm. In each iteration, the optimal SN-collection point (CP) associations and CP locations for the parameter epsilon are first obtained by the affinity propagation clustering algorithm. The optimal UAV trajectory is found using an improved elite genetic algorithm. Simulation results show that based on the optimized epsilon, the AoI-MATP algorithm can achieve a balance between data collection time and flight time, reducing the peak AoI of the collected data.

关键词： federated learning   internet public opinion   mobile edge computing   semantic communication  

摘要： Building a public opinion monitoring system can detect public opinion crises in advance and deal with crisis public relations in a timely manner. Semantic communication is a new type of communication technology with development potential, which reduces the amount of data required for transmission by mining semantic information in information sources. In this paper, a multi-user semantic communication system based on federated learning deployment is used to realize data transmission in edge computing scenarios. Use the data from the client side to train the deep learning model more effectively. This paper also evaluates the performance of the proposed semantic communication system. The model trained by federated learning can achieve an effect close to centralized training and protect user privacy.

关键词： Data integrity   Data privacy   Servers   Multi-access edge computing   Image edge detection   Electronic mail   Costs   Edge data integrity (EDI)   mobile edge computing   per-edge one-round   minimal verification tree  

摘要： Ensuring edge data integrity is crucial for reliable and secure low-latency services in mobile edge computing. However, existing edge data integrity (EDI) schemes suffer from certain limitations. These include the generation of excessive communication overhead and significant time consumption due to multi-round communications between application vendors and individual edge servers during the integrity verification process. Additionally, there is a notable straggler effect when employing batch verification techniques. To overcome these challenges, this paper presents a pioneering solution scheme for per-edge one-round EDI, referred to as OR-EDI. The proposed scheme effectively mitigates communication overhead, reduces time consumption, and eliminates the straggler effect, while simultaneously maintaining a high level of corruption detection accuracy. To implement OR-EDI, we introduce a novel verification structure known as the Minimal Verification Tree (MVT) and devise algorithms for acquiring Auxiliary Verification Information (AVI) and constructing the MVT. To comprehensively evaluate the performance of OR-EDI, we conduct a rigorous analysis that encompasses its correctness, efficiency, and security aspects. Furthermore, we implement the proposed scheme and perform a comparative assessment against existing EDI schemes, presenting extensive experimental results. These results affirm the effectiveness and efficiency of OR-EDI in effectively addressing the challenges associated with EDI.

关键词： IoV   Task offloading   Mobile edge computing   Fog computing   Fuzzy neural network  

摘要： The Internet of Vehicles (IoV) technology is progressively maturing because of the growth of private cars and the establishment of intelligent transportation systems. The development of smart cars has, therefore, been followed by a parallel rise in the volume of media and video games in the automobile and a massive increase in the need for processing resources. Smart cars cannot process the enormous quantity of requests created by vehicles because they have limited computing power and must maintain many outstanding jobs in their queues. The distribution of edge servers near the customer side of the highway may also accomplish real-time resource requests, and edge servers can assist with the shortage of computational power. Nevertheless, the substantial amount of energy created while processing is also an issue we must address. A joint task offloading strategy based on mobile edge computing and fog computing (EFTO) was presented in this paper to address this problem. Practically, the position of the processing activity is first discovered by obtaining the computing task's route, which reveals all the task's routing details from the starting point to the desired place. Next, to minimize the time and time expended during offloading and processing, a multi-objective optimization problem is implemented using the task offloading technique F-TORA based on the Takagi-Sugeno fuzzy neural network (T-S FNN). Finally, comparative trials showing a decrease in time consumed and an optimization of energy use compared to alternative offloading techniques prove the effectiveness of EFTO.

关键词： Intelligent reflecting surface (IRS)   mobile edge computing   quality of service   unmanned aerial vehicles (UAV)   wireless inland waterway communications  

摘要： Due to its exceptional ability to create favorable line-of-sight (LoS) propagation environments, the intelligent reflecting surface (IRS) is widely recognized as a technological enabler for wireless inland waterway communications. In this article, an IRS-assisted hybrid unmanned aerial vehicles (UAV)-terrestrial network architecture with unmanned surface vehicles (USVs) is proposed and a USVs energy minimization problem is formulated by jointly considering offloading decisions, computation capability, beamforming vector design and IRS phase shift-vector. To address the formulated problem, we decouple the original problem into two subproblems, in which the first subproblem focuses on joint offloading decisions and computation capability and the second subproblem concerns joint IRS phase shift-vector and the beamforming vector design. The enhanced differential evolution algorithm (EDE) is proposed to solve the former subproblem, and the minimum-variance-distortionless-response (MVDR) and enhanced min-maximization (EMM) algorithms are proposed to obtain optimized beamforming vector and IRS phase shift-vector in the second subproblem, respectively. Simulation results show how the proposed solution realizes a good tradeoff between network energy consumption and capacity in comparison to three alternative algorithms. The results also show that the proposed algorithm can improve the network performance in terms of the number of successfully offloaded tasks.

关键词： Autonomous aerial vehicles   Task analysis   NOMA   Relays   Trajectory   Computational modeling   Signal to noise ratio   Unmanned aerial vehicle   mobile edge computing   trajectory design   reconfigurable intelligence surface  

摘要： In this letter, we investigate the mobile edge computing framework with unmanned aerial vehicle (UAV) and reconfigurable intelligent surface (RIS) collaboration, in which a dedicated non-orthogonal multiple access (NOMA) based protocol is introduced for task offloading. In the model, the UAV acts as a relay node and computation server to support multiple ground users for offloading computation tasks to remote access point with the assistance of the RIS. To explore the impact of the joint UAV and RIS design with NOMA on computation performance, the sum computation bits maximization problem is formulated by optimizing computation and offloading bits, RIS phase shift design, UAV trajectory and bandwidth allocation. In order to obtain the solution, the original problem is decomposed into three more tractable subproblems, which can be solved by applying semidefinite relaxation method and successive convex approximation technique. Finally, the numerical solution demonstrates that our proposed algorithm achieves improvements on computation capacity compared with the benchmark and outperforms considerably the scheme without collaboration of the UAV and RIS.