关键词： Autonomous aerial vehicles   Trajectory   Task analysis   Resource management   Jamming   Security   Internet of Things   UAV   trajectory design   power control   security   mobile edge computing   federated learning   fairness  

摘要： Unmanned aerial vehicles (UAVs)-aided mobile-edge computing (MEC) systems face several challenges that hinder their practical implementation. First, the broadcast nature of wireless communications can cause security issues. Second, UAVs have constrained onboard power. Finally, the UAV should be able to serve a maximum number of ground users (GUs). It is also crucial to maintain fairness such that all GUs get equal opportunities to securely offload tasks to UAVs. We seek to address the aforementioned challenges by designing an intelligent mechanism, FairLearn, which maximizes the fairness in secure MEC services by controlling the UAV 3D trajectory, transmission power, and scheduling time for task offloading by mobile GUs. To this end, we formulate a maximization problem and solve it using a deep neural network (DNN)-based model, where the UAVs collaboratively learn the model by utilizing a federated learning (FL) approach. Each UAV uses a reinforcement learning (RL)-based approach to individually generate the training dataset, making the training data span different network scenarios. Our model is based on UAV pairs, where one UAV executes the GUs' offloaded tasks, while the other is a jammer that suppresses eavesdroppers. The simulation evaluation of FairLearn shows that it significantly improves the performance of UAV-enabled MEC systems.

关键词： Servers   Interference   Partitioning algorithms   Delays   Quality of service   Throughput   Simulation   Mobile edge computing   multiple choice-upper confidence bound   graph partition   edge server placement   interference management  

摘要： The deployment of edge servers make a significant impact on the service quality of a Mobile Edge Computing (MEC) system. This service quality relies on solving two key sub-problems: 1) interference management between servers 2) the placement of MEC servers. To improve the Quality of Service (QoS), we propose a method based on Graph Partition (GP) and Upper Confidence Bound (UCB) for solving these two sub-problems. Regarding interference management, we use an undirected graph to represent the interference between MEC servers so that the overall graph can be divided into multiple subsets of non-interfering MEC servers. Regarding server placement, we propose a Multiple Choice-Upper Confidence Bound (MC-UCB) algorithm that place an collection of interference aware edge servers in each selection. To evaluate the performance, we define a user's QoS function based on transmission delay, throughput, and user density comprehensively and compared with Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) from previous work. The simulation results show that the performance of the proposed algorithms is improved by more than 4% compared with the GA algorithm and 6% compared with the PSO algorithm.

关键词： NOMA   Transmitters   Receivers   Channel estimation   Jamming   Task analysis   Resource management   Covert communications   mobile edge computing   non-orthogonal multiple access   reconfigurable intelligent surface   reinforcement learning  

摘要： Mobile edge computing (MEC) is a key enabling technology for the sixth-generation (6G) wireless networks. In this paper, we apply covert communications to MEC to prevent information leakage, where two candidate technologies of 6G, reconfigurable intelligent surface (RIS) and non-orthogonal multiple access (NOMA), are adopted. Specifically, a legitimate transmitter sends messages to a pair of legitimate receivers, while a warden aims to detect whether the legitimate transmission exists. We can hide the existence of the stronger-signal receiver's transmission from the warden by exploiting the nature of NOMA, and we use a jammer to further hide this existence. We first analyze the performance for the case of fixed power allocation between the legitimate transmitters and the jammer. The closed-form expressions for the minimum detection error probability and ergodic public/covert rates are derived. Then, we design a reinforcement learning (RL)-based power-allocation optimization algorithm that maximizes the sum rate while ensuring covertness, by optimizing the power allocation between the transmitters and the jammer. Simulation results validate the correctness of our analysis and demonstrate the covertness of the proposed scheme. Furthermore, the performance of the RL-based algorithm is significantly better than that of the baseline scheme, which reflects the effectiveness of our proposed algorithm.

关键词： Base stations   Task analysis   Resource management   Servers   Wireless communication   Multi-access edge computing   Deep learning   High volatility   mobile edge computing   online task offloading  

摘要： With the proliferation of smart devices in recent years, many applications requiring high computing capability and low latency have emerged. Edge computing is one of the promising paradigms to support such applications. Due to the high volatility of edge environments, e.g., frequent movements of mobile devices, varying task sizes, and time-variant channel conditions, we have to make the offloading and resource management decisions on the fly. This paper formulates and studies the problem of online task offloading and resource management in heterogeneous mobile edge environments. The goal of the problem is to minimize the overall system latency. We prove that the problem is NP-hard. Moreover, traditional algorithms needing long decision-making times are insufficient to support applications with high volatility. This paper proposes a deep learning-assisted online algorithm that can make fast decisions. In particular, we design an offline solver for the proposed problem and use a deep neural network to emulate the solver. We conduct extensive simulations to evaluate the proposed approach. Results show that the proposed approach is around $50,000\times$50,000x and $500\times$500x faster than the commercial Gurobi solver for the optimal solution and the proposed offline approximation solver, respectively. Moreover, the overall latency under the proposed approach is near-optimal.

关键词： Resource management   Task analysis   Satellite broadcasting   Servers   Satellites   Optimization   Low earth orbit satellites   Satellite access networks   intelligent transportation systems   mobile edge computing   cooperative multi-agent proximal policy optimization   attention mechanism   deep reinforcement learning  

摘要： The proliferation of intelligent transportation systems (ITS) has led to increasing demand for diverse network applications. However, conventional terrestrial access networks (TANs) are inadequate in accommodating various applications for remote ITS nodes, i.e., airplanes and ships. In contrast, satellite access networks (SANs) offer supplementary support for TANs, in terms of coverage flexibility and availability. In this study, we propose a novel approach to ITS data offloading and computation services based on SANs. We use low-Earth orbit (LEO) and cube satellites (CubeSats) as independent mobile edge computing (MEC) servers that schedule the processing of data generated by ITS nodes. To optimize offloading task selection, computing, and bandwidth resource allocation for different satellite servers, we formulate a joint delay and rental price minimization problem that is mixed-integer non-linear programming (MINLP) and NP-hard. We propose a cooperative multi-agent proximal policy optimization (Co-MAPPO) deep reinforcement learning (DRL) approach with an attention mechanism to deal with intelligent offloading decisions. We also decompose the remaining subproblem into three independent subproblems for resource allocation and use convex optimization techniques to obtain their optimal closed-form analytical solutions. We conduct extensive simulations and compare our proposed approach to baselines, resulting in performance improvements of 9.9%, 5.2%, and 4.2%, respectively.

关键词： Optimization   Delays   Wireless communication   Task analysis   Costs   Servers   Clustering algorithms   Generalized benders decomposition   Lyapunov optimization   mobile edge computing   service caching   user-centric network  

摘要： Edge service caching can effectively reduce the delay or bandwidth overhead for acquiring and initializing applications. To address single-base station (BS) transmission limitation and serious edge effect in traditional cellular-based edge service caching networks, in this paper, we proposed a novel user-centric edge service caching framework where each user is jointly provided with edge caching and wireless transmission services by a specific BS cluster instead of a single BS. To minimize the long-term average delay under the constraint of the caching cost, a mixed integer non-linear programming (MINLP) problem is formulated by jointly optimizing the BS clustering and service caching decisions. To tackle the problem, we propose JO-CDSD, an efficiently joint optimization algorithm based on Lyapunov optimization and generalized benders decomposition (GBD). In particular, the long-term optimization problem can be transformed into a primal problem and a master problem in each time slot that is much simpler to solve. The near-optimal clustering and caching strategy can be obtained through solving the primal and master problem alternately. Extensive simulations show that the proposed joint optimization algorithm outperforms other algorithms and can effectively reduce the long-term delay and caching cost.

关键词： Task analysis   Resource management   Delays   Approximation algorithms   Cost accounting   Computational modeling   Pricing   Mobile edge computing   resource allocation   task offloading   auction   truthfulness  

摘要： Mobile edge computation (MEC) has recently emerged as a promising computing paradigm for supporting latency-sensitive mobile applications. Due to the limited resources of the edge servers (ESs), efficient resource allocation mechanisms are key to realize the MEC paradigm. In such a resource allocation process, it is a significant challenge to guarantee truthfulness while enabling flexible task offloading and satisfying the locality constraint. To address such a challenge, we propose a truthful auction-based resource allocation mechanism with flexible task offloading (TARFO) in an MEC system. Specifically, we first design the minimum delay task graph partitioning algorithm, aiming at calculating the minimum completion time and the task offloading solutions under different resource profiles. Based on this algorithm, for each smart mobile device (SMD), we further determine the set of feasible non-dominated resource profiles and the corresponding task offloading solutions. We next propose an efficient primal-dual approximation winning bid selection algorithm to determine the set of the winning bids and a critical value based pricing algorithm to calculate the payments of the winning bids. Strict theoretical analysis demonstrates TARFO can ensure truthfulness, individual rationality, computational efficiency and a smaller approximation ratio. Simulation results verify the effectiveness and efficiency of TARFO.

关键词： Servers   Inspection   Data integrity   Quality of service   Distributed databases   Costs   Time complexity   Mobile edge computing   edge data integrity   smart contract   corruption localization   inspection algorithm  

摘要： The emerging Mobile Edge Computing (MEC) paradigm reforms the way of data caching by motivating App vendors to store latency-sensitive data on distributed edge servers. In volatile MEC environments, ensuring Edge Data Integrity (EDI) is a major concern for App vendors. Existing EDI solutions only consider the scenario with a single App vendor and multiple edge servers, neglecting more complex multi-vendor and multi-server cases. If multiple App vendors check their data replicas cached on the same edge server simultaneously, integrity verification efficiency will drop exponentially. To mitigate this challenge, we make the first attempt to develop a Smart Inspection Algorithm (SIA) to pre-select unreliable data replicas for different App vendors in each verification round by jointly considering cache services' QoS (Quality-of-Service) and data replicas' unverified time. By implementing this approach, edge servers can merely verify the selected data replicas, greatly reducing computation and communication overheads in EDI verification. Theoretically, SIA can achieve O(n) expected time complexity. Supported by SIA, we expand the EDI problem in multi-vendor and multi-server MEC environments (referred to as the MVMS-EDI problem) and propose a smart contract-based approach entitled MVMS-SC to tackle the problem efficiently and impartially. We provide a rigorous theoretical analysis of the correctness, security, and efficiency of MVMS-SC. Both large-scale and small-scale experiments with real-world datasets are correspondingly performed on a single machine and a real platform to validate the superiority of MVMS-SC in terms of computation and communication efficiencies.

关键词： Mobile edge computing  

摘要： Unmanned aerial vehicles (UAVs) are widely applied in multiple fields, which emphasizes the challenge of obtaining UAV flight information to ensure the airspace safety. UAVs equipped with automatic dependent surveillance-broadcast (ADS-B) devices are capable of sending flight information to nearby aircrafts and ground stations (GSs). However, the saturation of limited frequency bands of ADS-B leads to interferences among UAVs and impairs the monitoring performance of GS to civil planes. To address this issue, the integration of the 5th generation mobile communication technology (5G) with ADS-B is proposed for UAV operations in this paper. Specifically, a hierarchical structure is proposed, in which the high-altitude central UAV is equipped with ADS-B and the low-altitude central UAV utilizes 5G modules to transmit flight information. Meanwhile, based on the mobile edge computing technique, the flight information of sub-UAVs is offloaded to the central UAV for further processing, and then transmitted to GS. We present the deterministic model and stochastic geometry based model to build the air-to-ground channel and air-to-air channel, respectively. The effectiveness of the proposed monitoring system is verified via simulations and experiments. This research contributes to improving the airspace safety and advancing the air traffic flow management. Copyright © 2024, The Authors. All rights reserved.

关键词： Mobile edge computing  

摘要： Fifth generation (5G) advancements rely heavily on integrating mobile edge computing (MEC) with ultra-dense networks (UDN). This not only boosts network capacity but also offers on-site processing power for future mobile devices. However, efficiently utilizing these resources requires well-designed scheduling policies within the MEC-UDN setup. This paper addresses this by proposing a resource scheduling scheme that considers both macro and small cells, minimizing task delay and device energy consumption. It employs an adaptive group learning whale optimization algorithm (AGLWOA)" to select the best offloading mode, target base station, and channel for each task. Simulation results show this approach outperforms previous methods in optimizing system performance and user experience. © 2024, The Authors. All rights reserved.