关键词： Autonomous aerial vehicles   Jamming   Trajectory   Resource management   Energy consumption   Optimization   Wireless networks   Channel allocation   Delays   Servers   UAV trajectory   mobile edge computing   jamming   non-convex mixed-integer nonlinear optimization  

摘要： In this paper, we address optimum design of uncrewed aerial vehicle (UAV)-based wireless networks with a focus on computation offloading in the presence of an active aerial attacker. Our design aims to minimize the maximum computation time among the tasks of ground users while satisfying the energy consumption requirements. To this end, we propose a joint optimization problem of partial computation offloading, ground user association, multiple UAVs trajectory control, computation resource, and sub-channel assignment. To tackle the underlying non-convex mixed-integer nonlinear optimization problem, we use the alternating optimization approach to iteratively solve the five sub-problems, namely, user-UAV association, user scheduling, partial offloading control and bit allocation over time slots, computation resource and sub-channel assignment, and UAV trajectory control until convergence. Moreover, the successive convex approximation method is employed to solve the non-convex sub-problems and improve the resilience of the system against jammer attacks. Additionally, we propose low-complexity algorithms to solve the involved sub-problems. Via extensive numerical studies, we illustrate the effectiveness of our proposed design compared to baselines under the impact of an aerial attacker.

关键词： Autonomous aerial vehicles   Jamming   Semantic communication   Reliability   Delays   Energy consumption   Servers   Resource management   Quality of experience   Data mining   mobile edge computing   unpiloted aerial vehicle   resource scheduling   anti-jamming   reinforcement learning  

摘要： Semantic communication is a great potential technology to improve the reliability and effectiveness of communication systems, but the limited computing capacity of users, the potential jamming attacks, and the inflexibility of fixed base stations pose significant challenges to communication efficiency. To overcome those challenges, this paper proposes a joint resource scheduling approach for unpiloted aerial vehicle (UAV)-assisted mobile edge computing (MEC) system based on reliable semantic communications against malicious jamming attacks. Specifically, we maximize the system's quality-of-experience (QoE) by jointly optimizing UAV trajectory, task offloading decision, and user association. Since the problem is non-convex and the jamming behavior is dynamic, an advanced softmax deep double deterministic policy gradients (SD3) based on the prioritized experience replay (PER) algorithm (called PER-SD3) is designed to realize an efficient communication strategy against jamming. Simulation results demonstrate that the proposed PER-SD3 approach can significantly improve the system delay and semantic communication performances compared with baseline approaches in the literature.

关键词： Mobile edge computing   Location prediction   5G   Computation offloading   Transformer   Dueling DQN   Double DQN   TOPSIS  

摘要： Mobile edge computing (MEC) brings the power of cloud computing closer to where users are, enhancing network performance and improving user experience. However, as users move around and individual edge servers cover only limited areas, there is a need for intelligent service assignment to keep up with user demands and low turn around time (TAT) requirements. This paper introduces TMLpSA, a synergistic framework that combines advanced user mobility prediction and decision-making techniques to optimize service assignments in a periodic fashion. By leveraging a Transformer model to anticipate where users will go next, and integrating a DRL-based TOPSIS technique, TMLpSA predicts user trajectories and proactively identifies the most suitable edge servers to assign services along their anticipated paths. Simulation results highlight how TMLpSA minimizes average application TAT significantly by 23.32%, while not only reducing offload energy consumption but also improving task completion rate and resource utilization with reasonable service migration frequency relative to the second best benchmark approach.

关键词： Servers   Array signal processing   Integrated sensing and communication   Optimization   Energy consumption   Complexity theory   Computational modeling   Wireless networks   Resource management   Autonomous aerial vehicles   Integrated sensing and communication   mobile edge computing   alternating direction method of multipliers   computation offloading   beamforming design  

摘要： When applying integrated sensing and communications (ISAC) in future mobile networks, many sensing tasks have low latency requirements, preferably being implemented at terminals. However, terminals often have limited computing capabilities and energy supply. In this paper, we investigate the effectiveness of leveraging the advanced computing capabilities of mobile edge computing (MEC) servers and the cloud server to address the sensing tasks of ISAC terminals. Specifically, we propose a novel three-tier integrated sensing, communication, and computing (ISCC) framework composed of one cloud server, multiple MEC servers, and multiple terminals, where the terminals can optionally offload sensing data to the MEC server or the cloud server. The offload message is sent via the ISAC waveform, whose echo is used for sensing. We jointly optimize the computation offloading and beamforming strategies to minimize the average execution latency while satisfying sensing requirements. In particular, we propose a low-complexity distributed algorithm to solve the problem. Firstly, we use the alternating direction method of multipliers (ADMM) and derive the closed-form solution for offloading decision variables. Subsequently, we convert the beamforming optimization sub-problem into a weighted minimum mean-square error (WMMSE) problem and propose a fractional programming based algorithm. Numerical results demonstrate that the proposed ISCC framework and distributed algorithm significantly reduce the execution latency and the energy consumption of sensing tasks at a lower computational complexity compared to existing schemes.

关键词： Mobile edge computing   Task offloading   Improved Particle swarm algorithm   Chemical reaction optimization algorithm  

摘要： The advent of mobile edge computing has given rise to a pressing concern: the optimal methodology for offloading edge computing tasks. Edge computing provides computing and storage capabilities at the edge of the network, in close proximity to the user. Through process of computation offloading, tasks that would otherwise be performed by the terminal are migrated to edge servers for execution, this has potential to significantly reduce application latency and conserve terminal energy consumption. Nevertheless, the majority of current significant research on task offloading for mobile edge environments focuses on the latency or energy consumption of a single mobile terminal. Accordingly, a hybrid heuristic task offloading algorithm based on integer coding is put forth for edge computing environments comprising multiple edge servers and multiple mobile terminals. The completion time of the task and the weighted calculation of energy consumption of the terminal were used as evaluation indexes. Based on the concepts of individual historical optimization and global optimization of the Improved Particle Swarm Optimization(IPSO) algorithm, four chemical molecular operations simulated by chemical reaction optimization (CRO) algorithm were introduced, so that molecules could be continuously optimized in the process of chemical reaction. The results of comparative simulation experiments demonstrate that compared with classical algorithms such as the IPSO algorithm, CRO algorithm and newer algorithms, IPSO_CRO algorithm can obtain better unloading decisions, and the optimization of objective function values, namely energy consumption and delay, is significantly better than other algorithms.

关键词： Heuristic algorithms   Optimization   Autonomous aerial vehicles   Internet of Things   Energy consumption   Approximation algorithms   Vehicle dynamics   Particle swarm optimization   Monitoring   Computational modeling   Mobile edge computing   unmanned aerial vehicle   swarm intelligence   task offloading   UAV-assisted wireless-powered MEC   particle swarm optimization  

摘要： Mobile edge computing (MEC) significantly boosts the computing power and reduces the energy consumption of Internet of Things (IoT) devices, serving as a valuable complement to cloud computing. The application of unmanned aerial vehicle (UAV) for MEC systems can effectively alleviate the issue of insufficient or damaged communication facilities in remote areas, further expanding the scope of MEC applications. In this article, we present a system model for UAV-assisted wireless-powered MEC systems in a dynamic environment with the objective of maximizing the computation rate of user devices. Due to the complexity of the optimization objective in dynamic environments, we propose a swarm intelligence-based optimization framework with a mechanism for responding to environmental changes, which is intended to enhance population diversity in both static and dynamic environments with the aim of overcoming premature convergence. We integrate particle swarm optimization and harmony search into the proposed framework, naming them DOPSO and DOHS, respectively. Simulation results for two offloading modes in UAV-assisted MEC systems indicate that the proposed framework significantly outperforms other dynamic optimization algorithms.

关键词： Servers   Collaboration   Vehicle dynamics   Dynamic scheduling   Optimization   Edge computing   Mobile computing   Resource management   Computational modeling   Real-time systems   Mobile edge computing   vehicle assisted   service caching   task offloading   multi-objective optimization  

摘要： The development of artificial intelligence (AI) enables vehicular edge computing (VEC) servers to be able to provide more intelligent services. However, the limited storage resources of VEC servers constrain the deployment of intelligent service contents, which greatly restricts the intelligence level of the VEC network. To resolve this problem, we first design a novel vehicle-assisted VEC network architecture and further propose VaCo, a Vehicle-assisted Collaborative caching system. VaCo allows VEC servers to download the cached service content from any vehicle in the VEC network to support task offloading. VaCo mainly considers the real-time scheduling problem of vehicle storage resources under the dynamic VEC network and the benefit problem caused by invoking vehicle resources under the highly dynamic load environment. VaCo models the vehicle storage resources as an independent resource pool and deploys a cross-VEC server content retrieval mechanism to achieve unified and efficient management of the storage resources of the vehicle cluster and the VEC server cluster. Then, we propose a multi-swarm collaborative optimization scheme to jointly optimize the service failure rate and cost, and further propose a Pareto-based optimization scheme to ensuring that VaCo can correctly evaluate the benefits of invoking vehicle resources in a dynamic VEC network. Finally, we implement VaCo and conduct extensive evaluations on real-world dataset. The experimental results on the real trajectory dataset show that VaCo can effectively utilize vehicle resources and ensure the benefits of both vehicles and VEC servers simultaneously.

关键词： Delays   Quality of experience   Heuristic algorithms   Energy consumption   Resource management   Optimization   Training   Long short term memory   Industrial Internet of Things   Deep reinforcement learning   Mobile edge computing   computation task offloading   quality of experience   deep reinforcement learning  

摘要： In the realm of mobile edge computing (MEC), efficient computation task offloading plays a pivotal role in ensuring a seamless quality of experience (QoE) for users. Maintaining a high QoE is paramount in today's interconnected world, where users demand reliable services. This challenge stands as one of the most primary key factors contributing to handling dynamic and uncertain mobile environments. In this study, we delve into computation offloading in MEC systems, where strict task processing deadlines and energy constraints can adversely affect the system performance. We formulate the computation task offloading problem as a Markov decision process (MDP) to maximize the long-term QoE of each user individually. We propose a distributed QoE-oriented computation offloading (QECO) algorithm based on deep reinforcement learning (DRL) that empowers mobile devices to make their offloading decisions without requiring knowledge of decisions made by other devices. Through numerical studies, we evaluate the performance of QECO. Simulation results reveal that compared to the state-of-the-art existing works, QECO increases the number of completed tasks by up to 14.4%, while simultaneously reducing task delay and energy consumption by 9.2% and 6.3%, respectively. Together, these improvements result in a significant average QoE enhancement of 37.1%. This substantial improvement is achieved by accurately accounting for user dynamics and edge server workloads when making intelligent offloading decisions. This highlights QECO's effectiveness in enhancing users' experience in MEC systems.

关键词： Servers   Resource management   Delays   Reliability engineering   Error probability   Optimization   Temperature sensors   Sensor phenomena and characterization   Reliability theory   Energy consumption   Low-latency   mobile edge computing   end-to-end reliability   fixed frames structure   dynamic frames structure  

摘要： In this paper, we consider a multi-access mobile edge computing (MEC) network with multiple sensors and one MEC server in industrial Internet of Things networks, where the MEC server provides a joint computation service (in the computation phase) for a set of sub-tasks offloaded by different sensors (in the communication phase). Due to the requirements of low latency and ultra reliability, we utilize finite blocklength information theory to characterize the reliability of the communication phase and exploit extreme value theory to investigate the delay violation probability in the computation phase. Following these characterizations, we derive the average end-to-end error probability of the entire service and provide two average end-to-end reliability-optimal design frameworks considering fixed frames structure and dynamic frames structure, in both of which the goal is to minimize the average end-to-end error probability by optimally allocating the total time length to each frame, as well as allocating each frame length to the communication phase and the computation phase. For the fixed frames structure, the original problem is decomposed, and the joint convexity of the decomposed sub-problems is rigorously proved, and the optimal solutions are obtained by the proposed optimal time allocation algorithm. Moreover, for the dynamic frames structure, we reformulate the optimization problem by introducing an average time constraint. By exploiting Lagrange multipliers, we transform the reformulated optimization problem into a dual problem with strong duality, the solutions of which can be obtained by the proposed time allocation algorithm. Via simulations, we validate the proven convexity and the approximation in our analytical model and evaluate the performance for both fixed frames length structure and dynamic frames length structure.

关键词： Pricing   Servers   Resource management   Internet of Things   Games   Computational modeling   Energy harvesting   Heuristic algorithms   Costs   Batteries   Mobile edge computing   pricing strategy   resource allocation   stackelberg game  

摘要： Mobile edge computing (MEC) mitigates the energy and computation burdens on mobile users (MUs) by offloading tasks to the network edge. To optimize MEC server utilization through effective resource allocation, a well-designed pricing strategy is indispensable. In this paper, we propose a dynamic differential pricing scheme (DDPS) for an edge offloading scenario with energy harvesting devices, which determines prices based on computing resource usage to enhance edge server (ES) utilization. First, an offloading decision algorithm is proposed to balance harvested and consumed energy, determining whether and how much data to offload. Second, a Stackelberg game-based differential pricing algorithm is proposed to optimize computing resource allocation for MUs and reallocate surplus resources to delay-sensitive devices. Extensive simulations are conducted to demonstrate the effectiveness of the proposed DDPS scheme. Specifically, in comparison to the existing best-performing pricing scheme, for different task arrival rates, DDPS can achieve a 5.3% decrease in average execution delay, a 1.7% increase in ES utility (U-server, which represents the payment from MUs minus penalties for discarded tasks), and a 2.1% increase in the average ratio of service for MUs. In addition, DDPS also improves 2.8% U-server on average with different ES computation capacities.