关键词： Autonomous aerial vehicles   Real-time systems   Sensors   Data acquisition   Servers   Task analysis   Trajectory optimization   data processing   mobile edge computing   trajectory optimization   unmanned aerial vehicles  

摘要： As a promising technology, unmanned aerial vehicles (UAVs)-enabled information gathering has received intensive interest in both academia and industry in recent years. However, the familiar paradigm, namely, data collection can hardly fulfill the active and real-time information gathering demands of many thriving smart applications, such as smart grid, where the UAV needs to serve as an intelligent aerial entity, gathering and analysing the information of surroundings actively and timely. To this aim, this article studies the paradigm data acquisition, where the UAV dynamically gathers information by on-board sensors. We consider a general mission scenario where a UAV acquires data in real time, which needs to be timely processed with the assistance of a mobile edge computing server. To address the real-time characteristics, we construct a novel theoretical data acquisition rate model, based on which the real-time processing quality of requirement (QoR) is defined. We respectively minimize the UAV energy consumption and mission completion time, under the constraints including real-time processing QoR and UAV mobility, by jointly optimizing the UAV trajectory, resources allocation, and time duration. To tackle these highly complex problems, we propose efficient algorithms by means of successive convex approximation and block coordinates descent techniques, where a closed-form solution for the UAV transmission and computation resources allocation is rigorously derived. Moreover, we consider a practical multi-location trajectory optimization where the UAV needs to traverse multiple target locations or areas in addition to the initial and final locations. The simulation results demonstrate the significant system performance gains brought by the proposed design and the effectiveness of the algorithms.

关键词： Mobile edge computing   Power control   Computation offloading   Deep deterministic policy gradient   Double Deep Q -Networks  

摘要： Smart mobile devices have recently emerged as a promising computing platform for computation tasks. However, the task performance is restricted by the computing power and battery capacity of mobile devices. Mobile edge computing, an extension of cloud computing, solves this problem well by providing computational support to mobile devices. In this paper, we discuss a mobile edge computing system with a server and multiple mobile devices that need to perform computation tasks with priorities. The limited resources of the mobile edge computing server and mobile device make it challenging to develop an offloading strategy to minimize both delay and energy consumption in the long term. To this end, an online algorithm is proposed, namely, the double reinforcement learning computation offloading (DRLCO) algorithm, which jointly decides the offloading decision, the CPU frequency, and transmit power for computation offloading. Concretely, we first formulate the power scheduling problem for mobile users to minimize energy consumption. Inspired by reinforcement learning, we solve the problem by presenting a power scheduling algorithm based on the deep deterministic policy gradient (DDPG). Then, we model the task offloading problem to minimize the delay of tasks and propose a double Deep Q -networks (DQN) based algorithm. In the decision-making process, we fully consider the influence of task queue information, channel state information, and task information. Moreover, we propose an adaptive prioritized experience replay algorithm to improve the model training efficiency. We conduct extensive simulations to verify the effectiveness of the scheme, and the simulation results show that compared with the conventional schemes, our method reduces the delay by 48% and the energy consumption by 53%.& COPY;2022 Elsevier Inc. All rights reserved.

关键词： association privacy   location privacy   mobile edge computing   privacy protection   task offloading  

摘要： In mobile edge computing (MEC), mobile devices can choose to offload their tasks to edge servers for execution, thereby effectively reducing the completion time of tasks and energy consumption of mobile devices. However, most of the data transfer brought by offloading relies on wireless communication technology, making the private information of mobile devices vulnerable to eavesdropping and monitoring. Privacy leakage, especially the location and association privacies, can pose a significant risk to users of mobile devices. Therefore, protecting the privacy of mobile devices during task offloading is important and cannot be ignored. This paper considers both location privacy and association privacy of mobile devices during task offloading in MEC and targets to reduce the leakage of location and association privacy while minimizing the average completion time of tasks. To achieve these goals, we design a privacy-preserving task offloading scheme to protect location privacy and association privacy. The scheme is mainly divided into two parts. First, we adopt a proxy forwarding mechanism to protect the location privacy of mobile devices from being leaked. Second, we select the proxy server and edge server for each task that needs to be offloaded. In the proxy server selection policy, we make a choice based on the location information of proxy servers, to reduce the leakage risk of location privacy. In the edge server selection strategy, we consider the privacy conflict between tasks, the computing ability, and location of edge servers, to reduce the leakage risk of association privacy plus the average completion time of tasks as much as possible. Simulated experimental results demonstrate that our scheme is effective in protecting the location privacy and association privacy of mobile devices and reducing the average completion time of tasks compared with the-state-of-art techniques.

关键词： Mobile edge computing  

摘要： In the current internet of things era, various devices can provide more services by connecting to the Internet. However, the explosive growth of connected devices will cause cloud core overload and significant network delays. To overcome these overload and delay problems, the mobile edge computing (MEC) network is proposed to provide most of the computing and storage near the radio access network to reduce the traffic of the core cloud network and provide lower latency for the terminal. Mobile edge computing can work with third parties to develop multiple services, such as mobile big data analysis and context-aware services. However, while using the service, it may also encounter a large amount of popular data being accessed in a short period of time. Without proper handling of replica generation and deployment, even in low-latency environments, it can still kill the benefits of MEC due to increased access time. Although many scholars have proposed related issues for copy replication, there are *** that can be improved. To avoid the situation of insufficient availability of replicas, replica replication is performed, but infinite replicas may lead to a significant increase in traffic and waste of resources. And when deploying replicas, it is necessary to avoid placing them on congested nodes and consider how to achieve better load balancing. To improve the above problems, we synthesize the advantages of previous algorithms, make up for the shortcomings, and propose an adaptive replica configuration mechanism to predict the popularity of files and replicate replicas to low-blocking nodes. This method spreads the subsequent access workload by copying the popular file in advance to improve the system's overall performance. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Unmanned air vehicle   mobile edge computing   task loss rate   multi-agent reinforcement learning  

摘要： In practical systems, a computing task generated by an Internet of Things device (IoTD) is usually given a valid period (vap). The tasks that cannot be executed within the vap will be dropped. The main goal of this letter is to minimize the task loss rate (TLR) in unmanned-air-vehicle (UAV) assisted mobile edge computing (MEC) due to the timeout. Furthermore, to ensure an equal service opportunity for the IoTDs and an equal energy consumption (EC) level for the UAVs, the issues of the TLR-fairness between IoTDs and the EC-fairness between UAVs are also considered. This is formulated as the mixed integer nonlinear programming (MINLP), which is difficult to be addressed by traditional methods, especially when the fast decision-making process is required. To address this problem, we present a new solution based on the multi-agent deep deterministic policy gradient (MA-DDPG) to optimize the flight trajectory, the association between the UAVs and IoTDs and the task scheduling of the IoTDs. Simulation results verify the effectiveness of the proposed MA-DDPG based algorithm.

关键词： UAV   Mobile edge computing   Resource allocation  

摘要： The proliferation of Internet-of-Things (IoTs) devices provides a promising platform for various intelligent applications such as virtual reality. However, because of the limited onboard computation resource of IoT devices, the computation task suffers from long latency. Mobile edge computing (MEC), which is a critical technology, allows offloading the computation tasks to an edge server to alleviate the shortage of computation resources and accelerate the computation tasks of IoT devices. Owing to the flexibility and mobility advantages of unmanned aerial vehicles (UAVs), UAV-assisted MEC has been widely researched. However, existing studies mostly explore a centralized offloading strategy. Therefore, when the number of IoT devices increases, the performance of the centralized strategy is reduced. The present study explores an intelligent strategy to minimize computation latency using a distributed algorithm. We develop a distributed algorithm named DeepMECagent based on deep reinforcement learning to optimize the computation offloading with minimum computation latency. In the considered scenario, a UAV functions as an aerial edge server to collect and process the computation tasks offloaded by ground IoT *** simulation results demonstrate the effective-ness of the proposed approach for minimizing the computation latency, where the computation latency of the proposed algorithm improves 39.71%,87.42%, and 88.55%, respectively, while compared with the centralized-DQN, Q-table, and the random algorithm. Given the expiration time as 1 second, the number of completed tasks within the expiration time of the proposed DeepMECagent is around 2 x and 1.25 x compared with the random algorithm and the Q-table algorithm, respectively.

关键词： Quality of service   Privacy   Load management   Computational efficiency   Resource management   Task analysis   Data aggregation   Decentralization   load balancing   mobile edge computing   peer-offloading algorithm   small cells  

摘要： In order to fit the requirements of future enterprise management networks with multiple service providers, conventional mobile edge computing enabled small cells (MEC-SCs) peer-offloading requires research efforts towards fully-decentralized computation-efficient global-optimal quality of service (QoS) aware load balancing, while ensuring service providers' privacy protection. In this article, we propose a new fully-decentralized on-demand MEC-SC peer-offloading NETwork (named DEEP-NET), targeting QoS-aware load balancing with enhanced latency and service providers' privacy protection. Newly developed federated gradient descent based algorithm is fully decentralized to MEC-SCs, which only requires local data and privacy-free inter-MEC-SC data sharing to achieve global optimal QoS-/latency-aware fairness. Result analysis for convergence of the proposed DEEP-NET provides guidance to the future topology optimization of fully-decentralized on-demand MEC-SC deployment. Besides, DEEP-NET outperforms the benchmarks with dynamic user demand to achieve optimal load balancing, with enhanced QoS, latency, and service providers' privacy.

关键词： Unmanned aerial vehicle   Mobile edge computing   Multi -chrome genetic algorithm   Evolutionary algorithm   Autonomous system  

摘要： This paper proposes a multi-unmanned aerial vehicle (UAV)-enabled autonomous mobile edge comput-ing (MEC) system, in which several UAVs are deployed to provide services to user devices (UDs). The aim is to reduce/minimize the overall energy consumption of the autonomous system via designing the optimal trajectories of multiple UAVs. The problem is very complicated to be solved by traditional methods, as one has to take into account the deployment updation of stop points (SPs), the association of SPs with UDs and UAVs, and the optimal trajectories designing of UAVs. To tackle this problem, we propose a variable-length trajectory planning algorithm (VLTPA) consisting of three phases. In the first phase, the deployment of SPs is updated via presenting a genetic algorithm (GA) having variable -length individuals. Accordingly, the association between UDs and SPs is addressed by using a close rule. Finally, a multi-chrome GA is proposed to jointly handle the association of SPs with UAVs and their order for UAVs. The proposed VLTPA is tested via performing extensive experiments on eight instances ranging from 60 to 200 UDs, which reveal that the proposed VLTPA outperforms other compared state-of-the-art algorithms.(c) 2021 ISA. Published by Elsevier Ltd. All rights reserved.

关键词： vehicle-to-infrastructure   internet of vehicles   mobile edge computing   channel occlusion   task scheduling   non-dominated sorting genetic algorithm-III  

摘要： In recent years, as more and more vehicles request service from roadside units (RSU), the vehicle-to-infrastructure (V2I) communication links are under tremendous pressure. This paper first proposes a dynamic dense traffic flow model under the condition of fading channel. Based on this, the reliability is redefined according to the real-time location information of vehicles. The on-board units (OBU) migrate intensive computing tasks to the appropriate RSU to optimize the execution time and calculating cost at the same time. In addition, competitive delay is introduced into the model of execution time, which can describe the channel resource contention and data conflict in dynamic scenes of the internet of vehicles (IoV). Next, the task scheduling for RSU is formulated as a multi-objective optimization problem. In order to solve the problem, a task scheduling algorithm based on a reliability constraint (TSARC) is proposed to select the optimal RSU for task transmission. When compared with the genetic algorithm (GA), there are some improvements of TSARC: first, the quick non-dominated sorting is applied to layer the population and reduce the complexity. Second, the elite strategy is introduced with an excellent nonlinear optimization ability, which ensures the diversity of optimal individuals and provides different preference choices for passengers. Third, the reference point mechanism is introduced to reserve the individuals that are non-dominated and close to reference points. TSARC's Pareto based multi-objective optimization can comprehensively measure the overall state of the system and flexibly schedule system resources. Furthermore, it overcomes the defects of the GA method, such as the determination of the linear weight value, the non-uniformity of dimensions among objectives, and poor robustness. Finally, numerical simulation results based on the British Highway Traffic Flow Data Set show that the TSARC performs better scalability and efficiency than other met

关键词： Mobile edge computing   Computation power maximization   Restricted multiple knapsack problem   Differential evolution  

摘要： High-density connection is among the natures of next-generation wireless communication systems. Meanwhile, various computation-intensive smart applications are becoming growing popularity with the technology boom. Thus, strong computation power will become a crucial requirement for wireless communication systems. Mobile edge computing provides a promising solution to this requirement by pushing a cloud-like computation capacity to the network edge. This study aims to maximize the computation power of a mobile edge computing enabled dense network. To this end, a computation bits maximization problem is formulated by jointly optimizing offloading decision and resource allocation. The problem is a mixed-variable nonlinear programming problem. First, by analyzing the feasibility of computation modes and constructing a user distribution strategy, the original problem is decoupled into two sub-problems, i.e., the assignment of the offloading users and resource allocation. The offloading users' assignment is modeled as a restricted multiple knapsack problem, and a profit density is defined to maximize the overall profits of multiple knapsacks. Then, an improved differential evolution algorithm is developed to address the knapsack problem, in which mutation and repair operators are designed according to the profit density. Based on the optimal solution to the knapsack problem, the resource allocation sub-problem is solved by the corresponding calculation. Finally, extensive experiments are conducted to evaluate the performance of our scheme. Results show that: (1) our scheme provides superior computation power compared to that of benchmark schemes;(2) the performance gain of our scheme over benchmark schemes expands with growing connection density. Therefore, our scheme is an effective computation power optimization scheme.