关键词： Intelligent reflecting surface   Unmanned aerial vehicle   Mobile edge computing   UAV-mounted IRS   Wireless energy transfer  

摘要： Intelligent reflecting surface (IRS) and unmanned aerial vehicle (UAV) have been recently used in wireless- powered mobile edge computing (MEC) systems to enhance the computation bits and energy harvesting performance. However, in the conventional IRS- and UAV-aided MEC systems, the IRS is installed at fixed locations on a building, which restricts the computation performance. UAV-mounted IRS (UAV-IRS), as a promising technology, combines the advantages of UAV and IRS. Hence, in this work, we study a UAVIRS wireless-powered MEC system, where multiple UAV-IRSs are considered between Internet of Things (IoT) devices and the base station to improve the computation bits and energy harvesting. The multi-antenna base station first charges the IoT devices via radio frequency signals, and then IoT devices offload their computation tasks to the base station via UAV-IRSs. We formulate a computation bits maximization problem for all IoT devices by jointly determining detection beamforming at IoT devices, active energy beamforming at the base station, power allocation, time slot assignment, CPU frequency, the phase shifts design in the wireless energy transfer (WET) and task offloading, and UAV-IRSs positions. A block coordinate descent (BCD) algorithm by decomposing the introduced problem into four blocks is proposed, while the detection beamforming, active energy beamforming, transmit power, time slot assignment, CPU frequency, and the phase shifts design in the task offloading are derived in closed-form results. Also, the successive convex approximation and semidefinite relaxation (SDR) are adopted to obtain the UAV-IRS positions and the phase shifts in the WET, respectively. The simulation results verify the effectiveness of the presented BCD method compared with the different benchmark schemes.

关键词： Servers   Resource management   Uplink   Performance evaluation   NOMA   Interference cancellation   Internet of Things   Decoding   Channel allocation   Minimax techniques   Mobile edge computing   rate splitting multiple access   max-min fairness   matching theory  

摘要： Rate splitting multiple access (RSMA) provides a flexible transmission framework that can be applied in mobile edge computing (MEC) systems. However, the research work on RSMA-assisted MEC systems is still at the infancy and many design issues remain unsolved, such as the MEC server and channel allocation problem in general multi-server and multi-channel scenarios as well as the user fairness issues. In this regard, we study an RSMA-assisted MEC system with multiple MEC servers, channels and devices, and consider the fairness among devices. A max-min fairness computation offloading problem to maximize the minimum computation offloading rate is investigated. Since the problem is difficult to solve optimally, we develop an efficient algorithm to obtain a suboptimal solution. Particularly, the time allocation and the computing frequency allocation are derived as closed-form functions of the transmit power allocation and the successive interference cancellation (SIC) decoding order, while the transmit power allocation and the SIC decoding order are jointly optimized via the alternating optimization method, the bisection search method and the successive convex approximation method. For the channel and MEC server allocation problem, we transform it into a hypergraph matching problem and solve it by matching theory. Simulation results demonstrate that the proposed RSMA-assisted MEC system outperforms current MEC systems under various system setups.

关键词： Mobile edge computing  

摘要： To improve the performance and reliability of task vehicle collaborative unloading, the study adopted Monte Carlo tree search and deep neural networks to optimize resource allocation of task vehicles in collaborative unloading. Secondly, through multi-mode collaboration, the relay unloading task of roadside units was carried out. Meanwhile, the service range of vehicle collaborative unloading was expanded based on the calculation results, achieving the full utilization of idle computing resources. These experiments confirmed that compared to random search and greedy search, the proposed network model scheme improved service latency performance by 58.3% and 47.1%, respectively. The proposed multi-mode joint unloading mechanism had significant performance improvement under the collaborative unloading mechanism from adjacent vehicles to vehicles. It offloaded tasks to service vehicles outside the communication range, reducing completion latency by approximately 33.6%. Therefore, this task vehicle collaboration unloading method improved the performance of mobile edge computing systems, reduced computing and storage costs, and lowered the energy consumption and maintenance costs of task vehicles. This research method can improve the efficiency and safety of task vehicle collaboration unloading, providing technical support for the optimization of intelligent transportation systems. © 2024 Slovene Society Informatika. All rights reserved.

关键词： Delays   Subspace constraints   Bandwidth   Servers   Resource management   Costs   Computational modeling   Mobile edge computing   relaying   bandwidth allocation   random offloading   multi-convex optimization  

摘要： This letter studies stochastic Multi-hop Mobile Edge Computing (MMEC), where a Relay Node (RN) receives tasks from the users and randomly decides to assign them to its own computing server or to offload them to one of the multiple Higher-level computing Nodes (HNs). We take into account the waiting times in the computation and transmission queues and present the system Average Response Time (ART) in terms of offloading probabilities and links' bandwidths. Based on that, we aim at minimizing the system ART by joint optimization of the task flow distribution among the RN/HNs and the bandwidth allocation among the links. We analyze the formulated problem and prove it is multi-convex. Moreover, we derive the closed-form expressions for the links' bandwidths when the offloading probabilities are fixed, and based on the presented analysis and insights, we propose effective solution methods. Numerical results reveal the promising performance of the proposed methods in efficient use of the resources and reduction of the ART.

关键词： Unmanned aerial vehicle (UAV)   reconfigurable intelligent surface (RIS)   mobile edge computing   energy consumption   Stackelberg game  

摘要： Unmanned Aerial Vehicles (UAVs) can be employed as short-term aerial base stations or as access points for User Equipments (UEs) to communicate with other UEs effectively. However, communication links may be obstructed by buildings, leading to poor data transfer performance and significant energy consumption. Deploying Reconfigurable Intelligent Surfaces (RIS) as part of the UAV-assisted communication system proves to be an effective means to avoid building obstructions and enhance wireless information quality. However, the complexity of communication relationships in multi-UAV systems with RIS-aided communication poses a significant challenge in energy reduction. Therefore, this study investigates a new RIS-aided multi-UAV communication framework for edge computing systems. The system aims to meet the quality-of-service (QoS) for UEs while minimizing the total energy consumption. To optimize the total energy consumption of RIS-aided multi-UAV communication, the impact of communication between multiple UAVs and differences between UE clusters on that system's performance is also considered. We introduce a Stackelberg game to deal with the communication relationship between multiple UAVs and design a K-means-based clustering algorithm to segment UEs periodically. A model-free deep reinforcement learning algorithm grounded in maximum entropy is proposed to jointly optimize UAV trajectory design, phase shift control, and power allocation to reduce energy consumption further. Experimental results indicate that the system proposed performs favorably concerning both energy consumption and throughput.

关键词： Task analysis   Autonomous aerial vehicles   Optimization   Trajectory   Resource management   Servers   Delays   Unmanned aerial vehicles (UAV)   mobile edge computing   task offloading   computation resource allocation   UAV trajectory control  

摘要： Recent developments in unmanned aerial vehicles (UAVs) and mobile edge computing (MEC) have provided users with flexible and resilient computing services. However, meeting the computation-intensive and delay-sensitive demands of users poses a significant challenge due to the limited resources of UAVs. To address this challenge, we consider a multi-UAV-assisted MEC system. Based on this system, we formulate a multi-objective optimization problem aiming at minimizing the total task completion delay, reducing the total UAV energy consumption, and maximizing the total number of offloaded tasks. Since the problem is a mixed-integer non-linear programming (MINLP) and NP-hard problem, we propose a joint task offloading, computation resource allocation, and UAV trajectory control (JTORATC) approach. The problem is split into three components to cope with the coupling of these decision variables, and then solved individually to obtain the corresponding decisions. Specifically, the sub-problem of task offloading is solved by using distributed splitting and threshold rounding methods, the sub-problem of computation resource allocation is solved by adopting the Karush-Kuhn-Tucker (KKT) method, and the sub-problem of UAV trajectory control is solved by employing the successive convex approximation (SCA) method. Simulation results show that the proposed JTORATC has superior performance compared with the other benchmark methods.

关键词： NOMA   high altitude platform station   resource allocation   Mobile edge computing   energy consumption   energy consumption   position optimization   position optimization   resource allocation   energy consumption   position optimization  

摘要： In this paper, the energy consumption (EC) optimization of an aerial high altitude platform station (HAPS) aided mobile edge computing (MEC) network with non-orthogonal multiple access (NOMA) in the presence of imperfect successive interference cancellation is studied. Specifically, joint design schemes of the resource allocation (RA) and the two-dimensional (2D) horizontal position are proposed to minimize the sum EC subject to the different constraint conditions. In particular, we jointly optimize the receive beamforming (BF), the power allocation (PA), HAPS position, the local computation resource, the computation task offload coefficient, and the computation resource allocated for each user via the block coordinate descent method. Namely, given the other optimization parameters, we first optimize a 2D position of HAPS. Then, given the 2D position, by introducing the auxiliary variables, a joint design of BF, PA, offload coefficient and computation resource is solved by an efficient iteration algorithm based on the successive convex approximation method. Additionally, a suboptimal joint design scheme is also developed to lower the complexity. Simulation results show that the proposed two design schemes of the joint RA and position are effective in reducing the EC, and they have a lower EC when compared to benchmark schemes.

关键词： Mobile edge computing   Task caching   Convolutional neural networks   Multi-criteria task ranking  

摘要： Given the problems with a centralized cloud and the emergence of ultra-low latency applications, and the needs of the Internet of Things (IoT), it has been found that novel methods are needed to support centralized cloud technology. Mobile edge computing is one of the solutions to mitigate these challenges. In this paper, we study task caching at Device to Device (D2D)-assisted network edge. In the proposed scheme, we predict the possibility of re-requesting tasks in the future using convolutional neural networks (CNN). Based on this predicted possibility, the number of last requests, and the number of cached versions of this task type in the neighbors, in addition to the characteristics of a task itself, including the required cache volume and processing resources, we rank the tasks using the proposed Multi-Criteria Task Ranking using Predicted requests (MCTRP) scheme and select the best replacement option in the cache of each Mobile User Equipment (MUE). The proposed scheme has proved to be of considerable benefit in terms of reducing delay and energy consumption and improving the utility of MUEs.

关键词： mobile edge computing   Index Terms -ACO   task offloading   trajectory planning   trajectory planning   UAV   UAV   trajectory planning   UAV  

摘要： In the paper, the Unmanned Aerial Vehicle (UAV) path planning and task offloading problem in UAV-assisted mobile edge computing (MEC) systems is investigated. A bi-criterion ant colony optimization (bi-ACO) framework is proposed for the considered problem with the objectives of minimizing the total cost and the completion time, meanwhile satisfying the energy, deadline, location, and priority constraints. In the bi-ACO framework, multiple heterogeneous colonies are introduced with different preferences of objectives. Each colony maintains five pairs of pheromone matrices for constructing feasible solutions. Besides the colony settings, three key components of bi-ACO are delicately designed: feasible solution generation method (FSGM) to construct a feasible solution, solution division method (SDM) to improve obtained solutions of good quality, and pheromone update method (PUM) to updates pheromone matrices by pheromone evaporation operation and pheromone enhancement operation based on the preferences of colonies. Four Pareto-based metrics are introduced to evaluate the performance of the compared algorithms. Experimental results show that the proposal outperforms the compared baseline algorithms in effectiveness and robustness.

关键词： forest monitoring networks   mobile edge computing   computation offloading   offloading cost optimization   dung beetle optimization algorithm   symmetrical features  

摘要： In forest monitoring networks, the computational capabilities of sensors cannot meet the latency requirements for complex tasks, and the limited battery capacity of these sensors hinders the long-term execution of monitoring tasks. Mobile edge computing (MEC) acts as an effective solution for this issue by offloading tasks to edge servers, significantly reducing both task latency and energy consumption. However, the computational capacity of MEC servers and the bandwidth in the system are limited, and the communication environment in forested areas is complex. To simulate the complexity of the forest communication environment, we incorporate empirical path loss and multipath fading into the calculation of signal transmission rates. The computational offloading problem is then converted into a minimum-cost optimization problem with multiple constraints related to energy consumption and latency, which we formulate as an NP-hard problem. We propose a dung beetle optimization (DBO) strategy for computational offloading, enhancing it with an improved circle chaotic mapping, a dimension decomposition strategy, and Cauchy disturbance. This algorithm has the beauty of symmetry in the search range, and the symmetrical features can comprehensively search for existing solutions. Experimental results demonstrate that the improved dung beetle optimization algorithm (IDBO) achieves better convergence, lower complexity, and superior optimization outcomes compared to local offloading strategies and other metaheuristic algorithms, confirming the effectiveness of the proposed algorithm and ensuring the service quality of the forest monitoring network.