关键词： Servers   Resource management   Costs   Energy consumption   Optimization   Mobile handsets   Delays   Computational modeling   Scheduling algorithms   Wireless communication   Mobile edge computing   energy consumption   computing cost   dependent task offloading   resource allocation  

摘要： Mobile edge computing (MEC) is a promising computing paradigm and can effectively reduce the energy consumption and computing costs at mobile devices by offloading computation-intensive and latency-sensitive applications/tasks to edge servers. However, how to achieve cost-effective dependent task offloading and resource allocation subject to application completion time constraint and service configuration constraint at edge side in heterogeneous MEC environments remains a challenge. To address this challenge, in this paper, we study the multi-application dependent task offloading and resource allocation problem in heterogeneous MEC environments for jointly minimizing the energy consumption and computing cost. We first formulate this problem as a mixed integer nonlinear programming (MINLP) problem. We propose a two-stage alternating optimization algorithm. In the first stage, a genetic-based algorithm is proposed to determine an optimized task offloading profile for given transmit power matrix, a look ahead based task scheduling algorithm is designed to obtain an optimized task schedule for the profile. In the second stage, the transmit power allocation problem for a given offloading profile is solved using convex optimization techniques. Extensive simulation results show that the proposed algorithm can effectively reduce the total cost of task executions as compared with baseline algorithms.

关键词： Internet of things   Vehicular networks   Energy-efficient communications   Ultra-reliable low-latency communications   Mobile edge computing  

摘要： The integration of Internet of Things (IoT) technologies into the vehicular industry has initiated a new era of connected and autonomous vehicles, revolutionizing transportation systems. However, this transformation introduces significant challenges, especially in 5 G networks, such as achieving Ultra-Reliable Low-Latency Communications (URLLC) and maintaining energy efficiency within the high mobility of vehicular environments. These are essential for supporting sustainable and environmentally friendly computing practices. To address these challenges, this paper introduces a URLLC-aware and energy-efficient data offloading strategy, utilizing the Asynchronous Advantage Actor-Critic (A3C) algorithm to navigate the complex dynamics of vehicular Mobile Edge Computing (MEC) environments. Our proposed method balances latency and energy consumption tradeoffs while ensuring robust communication reliability. Technical evaluations reveal that our approach significantly outperforms other algorithms, achieving up to 8.2 % energy savings and a reduction of over 29 % in latency.

关键词： Internet of Remote Things   Task offloading   Energy-efficient communication   Mobile Edge Computing  

摘要： The Internet of Remote Things (IoRT) involves networks of devices deployed in extensive and often remote areas, collecting data for transmission and processing. In such networks, Unmanned Aerial Vehicles (UAVs) gather data, which is then sent to Low Earth Orbit (LEO) satellites for processing. These systems often face significant challenges, particularly in task offloading. Conventional methods typically rely on static routing and scheduling algorithms that do not adapt to changing conditions and usually overlook the complexity of dynamic decision-making in harsh or isolated environments, thus failing to address the critical challenges of energy efficiency and latency. In this paper, we introduce a method comprised of a three-layer architecture. The first layer, the IoRT computing layer, uses Deep Q-Network (DQN) to optimize local decisions based on device constraints and task urgency. The second layer features UAVs serve as Mobile Edge Computing (MEC), which not only processes data but also decides whether to process tasks locally or offload them to LEO satellites, utilizing the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) for this decision-making process. The third LEO Satellite Layer has a high computational capacity to handle offloaded tasks. Simulation results demonstrate notable improvements: compared to another method, the proposed model shows a 14.73 % reduction in energy consumption and a 23.13 % decrease in latency while reducing execution costs by an average of 28.7 %.

关键词： Mobile edge computing   active/sleep control   Lyapunov optimization   deep reinforcement learning   Mobile edge computing   active/sleep control   Lyapunov optimization   deep reinforcement learning  

摘要： In this paper, we investigate the energy minimization problem in cell-free mobile edge computing (MEC) networks under dynamic channel conditions and random arrival tasks. We aim to minimize the total energy consumption of user equipment (UEs) and MEC servers (MECSs) by jointly optimizing MECS active/sleep mode selection, UE-MECS association decision (MSAD), and task offloading, communication, and computation resource allocation (TORA) across two different timescales. Considering that the involved optimization variables affect the system performance in different timescales, we decouple the formulated stochastic optimization problem into two subproblems: MSAD operating in the large timescale and TORA in the small timescale. Leveraging the Lyapunov method, the stochastic TORA problem is decoupled into a series of deterministic problems, of which the closed-form solutions are presented. Furthermore, the MSAD problem is reformulated as a constrained Markov decision process (MDP). Then, we propose a double dueling deep Q-network (D3QN) to learn the optimal MSAD based on the TORA results. Numerical results demonstrate that the proposed online TORA-assisted MSAD learning algorithm has effective convergence and achieves substantial energy reductions for the MEC networks compared with the benchmark schemes.

关键词： Task analysis   Servers   Resource management   Mobile applications   Mobile handsets   Multi-access edge computing   Costs   Mobile edge computing   auction theory   incentive mechanism   computation offloading   dependent application  

摘要： As mobile applications grow increasingly computation-intensive, the challenges arising from the limitations of mobile devices in terms of computing resources and battery life become more pronounced. Mobile Edge Computing (MEC) provides a promising avenue to address these challenges and enhance user experience. While existing studies have extensively explored resource allocation and task scheduling in MEC, most treat tasks as monolithic entities, overlooking the nuanced subtasks/components that often make up mobile applications. This paper endeavors to bridge the gap between the need for incentive mechanisms and the offloading of dependent computation tasks in MEC. Drawing inspiration from auction theory, we introduce a novel Multi-stage Iterative Combinatorial Double Auction (MICDA) mechanism, specifically tailored for dependent tasks in a cloud-edge-end cooperative computing scenario. Through theoretical analysis, the MICDA mechanisms demonstrate truthfulness, individual rationality, budget balance, and computational efficiency. Comprehensive experiment results further confirm its superior performance in improving application makespan and social welfare compared to other existing offloading strategies. This work validates the effective integration of dependency-aware computation offloading and auction mechanisms in overcoming economic and computational challenges in MEC systems, thereby paving the way for their potential application in broader real-world scenarios.

关键词： mobile edge computing   Cognitive radio   matching theory   matching theory   evolu- tionary game   matching theory  

摘要： In this paper, we study a novel integration of the Mobile edge computing (MEC) technology with cognitive radio (CR) into a holistic network, namely CR-MEC, aim to create a paradigm for communication and computing fusion in 6G networks. In this contribution, we study the comprehensive resource allocation mechanism of user grouping, power and spectrum. More specifically, multiple cognitive users (CUs) of CR-MEC networks are classified into different user groups by employing a proposed user replicator dynamical evolution (URDE) algorithm. After that, the optimal power allocation is characterized in each group for balancing the user fairness. In particular, according to the users' preference lists, multiple CUs select their suitable primary spectrum by using the two-side matching theory. Additionally, these CUs would offload their tasks to the same MEC server, by implementing either non-orthogonal multiple access (NOMA) or OMA schemes. Furthermore, we carry out the relative performance evaluations in terms of energy efficiency for the CR-MEC networks and conclude that the obtained performances based on the proposed algorithms would approach to the centralized solutions.

关键词： Unmanned aerial vehicles   Mobile edge computing   Trajectory planning   V-shaped and S-shaped transfer functions   K -medoids clustering algorithm  

摘要： The mobile edge computing system supported by multiple unmanned aerial vehicles (UAVs) has gained significant interest over the last few decades due to its flexibility and ability to enhance communication coverage. In this system, the UAVs function as edge servers to offer computing services to Internet of Things devices (IoTDs), and if they are located distant from those devices, a significant amount of energy is consumed while data is transmitted. Therefore, optimizing UAVs' trajectories is an indispensable process to minimize overall energy consumption in this system. This problem is difficult to solve because it requires multiple considerations, including the number and placement of stop points (SPs), their order, and the association between SPs and UAVs. A few studies in the literature have been presented to address all of these aspects;nevertheless, the majority of them suffer from slow convergence speed, stagnation in local optima, and expensive computational costs. Therefore, this study presents a new trajectory optimization algorithm, namely ITPA-GBOKM, based on a newly proposed transfer-based encoding mechanism, gradient-based optimizer, and K-Medoids Clustering algorithm to tackle this problem more accurately. The K-medoid clustering algorithm is used to achieve better association between UAVs and SPs since it is less sensitive to outliers than the K-means clustering algorithm;the transfer function-based encoding mechanism is used to efficiently define this problem's solutions and manage the number of SPs;and GBO is utilized to search for the best SPs that could minimize overall energy consumption, including that consumed by UAVs and IoTDs. The proposed ITPA-GBOKM is evaluated using 13 instances with several IoTDs ranging from 60 to 700 to show its effectiveness in dealing with the trajectory optimization problem at small, medium, and large scales. Furthermore, it is compared to several rival optimizers using a variety of performance metrics, including av

关键词： Computation task offloading   MIMO-NOMA   Mobile edge computing   Hybrid PufferFish Osprey Optimization   Throughput  

摘要： One of the promising technologies to minimize computing delay and task offloading is Mobile Edge Computing (MEC), which enables mobile devices to offload high computational works. Non-Orthogonal Multiple Access (NOMA) is also regarded as one of the significant approaches for improving the spectrum efficacy, whereas the Massive Multiple-Input Multiple-Output (MIMO) helps to assist the enormous amount of candidates for continuous offloading. These two mechanisms can support the offloading and then enhance the MEC framework's performance. Nevertheless, the incorporation of portable cloud computing might result in substantial delays in offloading or congestion in network traffic due to the restricted capacity of the main network infrastructure, particularly when a considerable number of users simultaneously seek computational offloading. This emphasizes the necessity for sophisticated network management and improvements to infrastructure in order to satisfy the increasing demands of mobile edge computing. This work presents a highly effective MIMO-NOMA technique in MEC to increase the process of task offloading. The objective of this work is to decrease the overall transmission and computing delay under the MEC computing capability and the transmit power of the user. With the support of this pairing mechanism for Massive MIMO-NOMA, the lower channel gain candidates and the greater channel gain users can offload their information to the MEC. The Hybrid PufferFish Osprey Optimization (HPFOO) algorithm is utilized to optimize the parameters in the model. The HPFOO is a combination of the Pufferfish Optimization Algorithm (POA) and Osprey Optimization Algorithm (OOA) with the goal of reducing both transmission and computing delay. The performance investigation of the NOMA-MIMO-based MEC device is carried out by comparing its overall latency and total data throughput with those of the current system configurations.

关键词： Mobile edge computing   Power management   Levy walk   Reliability   Task scheduling  

摘要： Mobile edge computing (MEC) enables computationally intensive tasks to be processed at the network edge to provide low-latency services. However, inefficient task scheduling can negatively impact performance metrics like completion time and energy consumption. This paper proposes CAPL-MEC, an adaptive task scheduling framework that utilizes Levy walk modeling to address mobility patterns in MEC. The system model generates random edge nodes within defined bounds to simulate heterogeneous environments. A power consumption model is also presented to optimize dynamic and static power. Device mobility follows an adaptive Levy walk distribution where the power law exponent is time-varying. Latency and reliability (task replication) models are also defined. The CAPL-MEC algorithm utilizes an adaptive Levy walk approach to predict device locations and schedule tasks accordingly. A hybrid task allocation strategy combines proximity awareness, mobile-centric execution, and handovers between mobile and edge devices. Simulations evaluate CAPL-MEC across metrics like completion time, energy consumption, CPU and memory utilization, and wait times under various configurations. Results demonstrate that CAPL-MEC outperforms other algorithms by minimizing completion time through efficient resource allocation based on predicted mobility patterns. Energy consumption is also reduced through power-conscious scheduling. The framework presents a practical and adaptable solution for task scheduling in dynamic MEC environments.

关键词： Resource management   Jamming   Autonomous aerial vehicles   Heuristic algorithms   Energy consumption   Optimization   Task analysis   Anti-jamming   deep reinforcement learning   energy and latency minimization   mobile edge computing   resource management   unmanned aerial vehicle  

摘要： In mobile edge computing (MEC) systems, multiple unmanned aerial vehicles (UAVs) can be utilized as aerial servers to provide computing, communication, and storage services for edge users, called UAV-assisted MEC, which has emerged as a promising technology to improve both the computing and communication performances. Unlike existing works without considering jamming attacks, we investigate a multi-UAV-assisted-MEC scenario under multiple malicious jammers and then propose a resource management approach with the objective of minimizing both the system energy consumption and latency. Due to the time-varying nature of communication environments, we design a multi-agent deep reinforcement learning (MADRL)-based resource management approach to dynamically adjust the CPU frequency, communication bandwidth, and channel access selection of UAVs to enhance the system performance against jamming attacks. On this basis, in order to enhance the algorithm learning efficiency, we propose a multi-agent twin-delayed deep deterministic policy algorithm in combination with the prioritized experience replay mechanism (PER-MATD3) to effectively search for the joint resource management strategy under high-dimensional state and action spaces, where the time-varying channel state information and imperfect attack behavior information are also effectively trained to improve the learning capacity and convergence speed. Simulation and experimental results verify that the proposed approach can significantly decrease the overall system latency (i.e., computing and communication latency) and energy consumption compared to other benchmark algorithms under different real-world settings.