关键词： Task analysis   Internet of Things   Autonomous aerial vehicles   Delays   Costs   Resource management   Disasters   Aerial computing   mobile edge computing   deep reinforcement learning   computation offloading  

摘要： The exponential growth of Internet of Things (IoT) devices and emerging applications have significantly increased the requirements for ubiquitous connectivity and efficient computing paradigms. Traditional terrestrial edge computing architectures cannot provide massive IoT connectivity worldwide. In this article, we propose an aerial hierarchical mobile edge computing system composed of high-altitude platforms (HAPs) and unmanned aerial vehicles (UAVs). In particular, we consider non-divisible tasks and formulate a task offloading problem to minimize the long-term processing cost of tasks while satisfying the queueing mechanism in the offloading procedure and processing procedure of tasks. We propose a multi-agent deep reinforcement learning (DRL) based computation offloading algorithm in which each device can make its offloading decision according to local observations. Due to the limited computing resources of UAVs, high task loads of UAVs will increase the ratio of abandoning offloaded tasks. To increase the success ratio of completing tasks, the convolutional LSTM (ConvLSTM) network is utilized to estimate the future task loads of UAVs. In addition, a prioritized experience replay (PER) method is proposed to increase the convergence speed and improve the training stability. The experimental results demonstrate that the proposed computation offloading algorithm outperforms other benchmark methods.

关键词： Unmanned aerial vehicle   Mobile edge computing   Evolutionary algorithm   Autonomous system  

摘要： In the domain of 5G/6G wireless communications, mobile edge computing (MEC) technology is widely utilized for efficient data transmission. Unmanned aerial vehicles (UAVs) have emerged as the most recent transmission carriers in this landscape. However, the challenges associated with UAV deployment and path planning are often regarded as NP-hard, nonconvex, and nonlinear problems. Traditional optimization techniques struggle to address these complexities effectively. To address this issue, this study proposes "Edge-UAV", a novel mobile edge computing system specifically designed for UAVs. The primary objective of Edge-UAV is to minimize energy consumption and optimize the operational efficiency of UAVs. To achieve this goal, this study introduces a two-stage optimization strategy and a nonhovering transmission strategy. First, the coordinate updating operation of the UAVs' hovering points is decoupled from the path planning task. Additionally, the perturbation-inheritance algorithm is employed to enhance the coordinate updating process. In the second stage, the nonhovering transmission strategy enables UAVs to perform data transmission tasks while in flight, effectively optimizing their working time. This paper provides a detailed elucidation of the roles played by these innovative strategies within the Edge-UAV system. To assess the superiority of the proposed strategies, eight sets of comparative experiments involving 60-200 individual devices awaiting data transmission are conducted. The experimental results demonstrate the significant advantages of the Edge-UAV system in terms of reducing total energy consumption and optimizing the operational hours of UAVs. Through comprehensive experimentation and analysis, this study contributes to the advancement of UAV-assisted mobile edge computing in 5G/6G wireless communication networks. The proposed strategies exhibit promising potential for enhancing the efficiency and performance of UAVs.

关键词： UAV-enabled wireless communications   A2A   RSSI   path loss   ground reflection   mobile edge computing  

摘要： Unmanned aerial vehicle (UAV)-enabled wireless communications are becoming increasingly important in applications such as maritime and forest rescue operations. UAV systems often depend on wireless networking and mobile edge computing (MEC) devices for effective deployment, particularly in swarm UAV-enabled MEC configurations focusing on channel modeling and path loss characteristics for air-to-air (A2A) communications. This paper examines path loss characteristics in far-field (FF) ground reflection scenarios, specifically comparing two environments: FF1 (forest floor) and FF2 (seawater floor). LoRa modules operating at 868 MHz were deployed for communication between a transmitting UAV (Tx-UAV) and a receiving UAV (Rx-UAV) to conduct this study. We investigated the received signal strength indicator (RSSI) and path loss characteristics across channel bandwidths of 125 kHz and 250 kHz and spread factors (SF) of 7, 9, and 12. Experimental results show that ground reflection has minimal impact in the FF1 scenario, whereas, in the FF2 scenario, ground reflection significantly influences communication. Therefore, in the seawater environment, a UAV-enabled LoRa MEC configuration using a 250 kHz bandwidth and an SF of 7 is recommended to minimize the effects of ground reflection.

关键词： Internet of vehicle   Mobile edge computing   Cloud computing   Resource allocation   Offloading strategy  

摘要： Internet of Vehicles (IoV) relies heavily on its computing capability to facilitate various vehicular applications. Since the cloud computing or mobile edge computing (MEC) only cannot satisfy the latency requirement due to the limitation of the resource coverage, the cloud- edge-end cooperative computing has become an emerging paradigm. A comprehensive IoV architecture is considered and a joint optimization problem is formulated to minimize the system function value. To optimize the resource allocation and the task offloading strategy, the simulated spring system algorithm (SSSA) is designed where the initial problem is decoupled into two sub-problems with priority. The first one is to allocate computing resources based on KKT conditions, thus the individual optimal solution is achieved. The second one is solved based on the idea of simulated spring system such that the task offloading strategy is obtained. Two sub-problems iterate mutually to update each other until finishing the binary tree traversal. Thus, the proposed solution adapts to various conditions and the computational complexity is also reduced compared with traditional methods. Simulation verifies that the proposed algorithm reduces the maximum system function value by about 31% compared with the benchmark methods and performs efficiently in various road conditions.

关键词： Semantics   Jamming   Wireless communication   Heuristic algorithms   Resource management   Computational modeling   Autonomous aerial vehicles   Semantic communication   unmanned aerial vehicle   mobile edge computing   deep reinforcement learning   resource management   anti-jamming  

摘要： The integration of semantic communication with mobile edge computing (MEC) has emerged as a prominent research area. In this paper, we explore a novel scenario where semantic communication is integrated with unmanned aerial vehicles (UAVs) to enhance MEC, particularly in the face of jamming attacks. Our research focuses on addressing the resource management challenge to minimize task completion time and maximize semantic spectral efficiency (SSE) while adhering to quality of service requirements and resource constraints. Given the non-convexity of this problem and the dynamic behavior of jamming attacks, this paper proposes a deep reinforcement learning (DRL) algorithm by jointly optimizing UAV trajectories, user associations, and channel selections against jamming. In detail, the proposed anti-jamming DRL-based resource management approach can effectively capture the jammer's behavior, and learn to adjust semantic task and resource scheduling strategies with the objective to minimize the negative effect of jamming attacks on task offloading and semantic communication. Simulation results demonstrate that the proposed approach outperforms baseline algorithms in terms of task completion time and total SSE under different real-world settings.

关键词： Streaming media   Stochastic processes   Video recording   Quality assessment   Performance evaluation   Delays   Visual analytics   Mobile edge computing   video offloading   mobility   video quality   stochastic program   sample average approximation   clustering   multi-armed bandit   online learning  

摘要： Videos are projected to account for roughly 80% of global mobile data traffic by 2028. Many camera-equipped mobile devices, such as surveillance drones, require realtime video analytics, encompassing tasks like object detection and action recognition. These devices, restricted by limited resource constraints, need to offload videos to Mobile Edge Computing (MEC) to simultaneously optimize video analytics performance and minimize delay. However, MEC is facing significant challenges in providing efficient video offloading solutions, especially due to uncertainties caused by dynamic device mobility and the associated trade offs in selecting video quality for offloading. Offloading high-quality videos enhances video analytics performance, such as object detection accuracy. Yet, as a mobile device relocates, lower video quality or serving by a different MEC cloudlet may be required (triggering a service migration) to maintain a satisfactory service performance. In this paper, we study the Video Offloading Problem (VOP) in MEC to address these challenges. We propose two uncertainty-aware approaches that model the uncertainties in the environment to solve VOP. Our first approach, focusing on system side, is based on Two-stage Stochastic Program and proposing a unique clustering-based Sample Average Approximation to effectively solve TSP-VOP. The second approach, focusing on device side, employs an online learning algorithm based on a multi-armed bandit to learn and select the optimal offloading solution online. Through extensive experiments, we show that our proposed approaches significantly enhance video offloading decisions, with high video quality and reduced service migration costs under uncertain device mobility, compared to other benchmarks.

关键词： Protocols   Autonomous aerial vehicles   Security   Authentication   Task analysis   Servers   Real-time systems   Mobile edge computing   mutual authentication   self-certified public key cryptosystem   security analysis   UAV  

摘要： Unmanned Aerial Vehicle(UAV)-assisted mobile edge computing(MEC) ensures continuous MEC services by promptly restoring overloaded or disabled MEC infrastructure. Equipped with sufficient computing resources, UAVs deploy to areas needing MEC, such as task offloading and entertainment services. However, in areas with paralyzed edge nodes, vehicle users are unable to verify the legitimacy of UAVs as they cannot access to the trusted authority(TA). Furthermore, the integrity of UAV-assisted MEC environments can be compromised by malicious attackers, as all network participants rely on wireless communication for their interactions. Many authentication schemes have been proposed for UAV environments. However, these schemes encounter a problem of having to communicate through TA for authentication with vehicle users, which makes them difficult to apply to UAV-assisted MEC environments. Therefore, we propose a new broadcast authentication protocol, which can recover MEC services using MEC-equipped UAVs. The proposed protocol can provide UAVs and vehicle users with high reliability via a self-certified public-key cryptosystem, which can verify the legitimacy of the communication partner without the TA. Moreover, we guarantee the user privacy and preserve sensitive information using biohash technology. We verify the security robustness of the proposed protocol using various simulation tool, informal, and formal analyses. We also estimate the practical deployment of the proposed protocol using "Network Simulator-3". Moreover, we estimate the computation and communication overheads and compare with other existing protocols. The results show that the proposed protocol is feasible and provides users with convenient and seamless intelligent transportation services in UAV-assisted MEC environments.

关键词： Deep reinforcement learning   LEO satellites   mobile edge computing   mobile edge computing   proximal policy optimization   proximal policy optimization   mobile edge computing   proximal policy optimization   spatiot- emporal load  

摘要： Satellite edge computing, as an extension of ground edge computing, is a key technology for providing computing services by deploying resources on low earth orbit (LEO) satellites. However, the temporal and spatial differences in population density and economic levels may lead to unbalanced computation workloads on LEO satellites. Considering the mobility and limited resources inherent in LEO satellites, effectively utilizing the LEO satellite network to meet global competitive demands for task offloading becomes challenging. Therefore, in this paper, we propose an adaptive task offloading approach with spatiotemporal load awareness, named ATO-SLA, in satellite edge computing, aiming to optimize users' perceived-delay and energy consumption. Specifically, to avoid LEO satellite overload, we first introduce the spatiotemporal load factor, formally modelling the spatiotemporal load-aware task offloading problem. Then, the Markov decision process is employed to structure the task offloading decision process. Afterwards, we propose a task offloading algorithm based on proximal policy optimization strategy to adaptively solve the problem. Finally, experimental results demonstrate that ATO-SLA achieves a lower average delay and average energy consumption compared with other approaches.

关键词： Servers   Costs   Heuristic algorithms   Base stations   Optimization   Clustering algorithms   Mobile handsets   Telecommunications   Symbols   Programming   Mobile edge computing   profit-driven optimization   reinforcement learning   integer nonlinear programming  

摘要： Edge computing has emerged as a promising paradigm to meet the increasing demands of latency-sensitive and computationally intensive applications. In this context, efficient server deployment and service placement are crucial for optimizing performance and increasing platform profit. This paper investigates the problem of server deployment and service placement in a multi-user scenario, aiming to enhance the profit of Mobile Network Operators while considering constraints related to distance thresholds, resource limitations, and connectivity requirements. We demonstrate that this problem is NP-hard. To address it, we propose a two-stage method to decouple the problem. In stage I, server deployment is formulated as a combinatorial optimization problem within the framework of a Markov Decision Process (MDP). We introduce the Server Deployment with Q-learning (SDQ) algorithm to establish a relatively stable server deployment strategy. In stage II, service placement is formulated as a constrained Integer Nonlinear Programming (INLP) problem. We present the Service Placement with Interior Barrier Method (SPIB) and Tree-based Branch-and-Bound (TDB) algorithms and theoretically prove their feasibility. For scenarios where the number of users changes dynamically, we propose the Distance-and-Utilization Balance Algorithm (DUBA). Extensive experiments validate the exceptional performance of our proposed algorithms in enhancing the profit.

关键词： Mobile edge computing   Truthfulness   Resource allocation   Energy consumption   Mechanism design  

摘要： Mobile Edge Computing (MEC) aims at decreasing the response time and energy consumption of running mobile applications by offloading the tasks of mobile devices (MDs) to the MEC servers located at the edge of the network. The demands are multi-attribute, where the distances between MDs and access points lead to differences in required resources and transmission energy consumption. Unfortunately, the existing works have not considered both task allocation and energy consumption problems. Motivated by this, this paper considers the problem of task allocation with multi-attributes, where the problem consists of the winner determination and offloading decision problems. First, the problem is formulated as the auction-based model to provide flexible service. Then, a randomized mechanism is designed and is truthful in expectation. This drives the system into an equilibrium where no MD has incentives to increase the utility by declaring an untrue value. In addition, an approximation algorithm is proposed to minimize remote energy consumption and is a polynomial-time approximation scheme. Therefore, it achieves a tradeoff between optimality loss and time complexity. Simulation results reveal that the proposed mechanism gets the near-optimal allocation. Furthermore, compared with the baseline methods, the proposed mechanism can effectively increase social welfare and bring higher revenue to edge server providers.