关键词： internet of vehicles   mobile edge computing   edge-cloud collaboration   deep deterministic policy gradient networks   computational offloading  

摘要： With the continuous development of intelligent transportation system technology, vehicle users have higher and higher requirements for low latency and high service quality of task computing. The computing offloading technology of mobile edge computing (MEC) has received extensive attention in the Internet of Vehicles (IoV) architecture. However, due to the limited resources of the MEC server, it cannot meet the task requests from multiple vehicle users simultaneously. For this reason, making correct and fast offloading decisions to provide users with a service with low latency, low energy consumption, and low cost is still a considerable challenge. Regarding the issue above, in the IoV environment where vehicle users race, this paper designs a three-layer system task offloading overhead model based on the Edge-Cloud collaboration of multiple vehicle users and multiple MEC servers. To solve the problem of minimizing the total cost of the system performing tasks, an Edge-Cloud collaborative, dynamic computation offloading method (ECDDPG) based on a deep deterministic policy gradient is designed. This method is deployed at the edge service layer to make fast offloading decisions for tasks generated by vehicle users. The simulation results show that the performance is better than the Deep Q-network (DQN) method and the Actor-Critic method regarding reward value and convergence. In the face of the change in wireless channel bandwidth and the number of vehicle users, compared with the basic method strategy, the proposed method has better performance in reducing the total computational cost, computing delay, and energy consumption. At the same time, the computational complexity of the system execution tasks is significantly reduced.

关键词： Mobile edge computing   Unmanned aerial vehicle   Evolutionary algorithm   Genetic algorithm  

摘要： This paper presents a multi-unmanned aerial vehicle (UAV)-assisted mobile edge computing system, where multiple UAVs are used to serve mobile users. We aim to minimize the overall energy consumption of the system by planning the trajectories of UAVs. To plan the trajectories of UAVs, we need to consider the deployment of hovering points (HPs) of UAVs, their association with UAVs, and their order for each UAV. Therefore, the problem is very complicated, as it is non-convex, nonlinear, NP-hard, and mixed-integer. To solve the problem, this paper proposed an evolutionary trajectory planning algorithm (ETPA), which comprises four phases. In the first phase, a variable-length GA is adopted to update the deployments of HPs for UAVs. Accordingly, redundant HPs are removed by the remove operator. Subsequently, a differential evolution clustering algorithm is adopted to cluster HPs into different clusters without knowing the number of HPs in advance. Finally, a GA is proposed to construct the order of HPs for UAVs. The experimental results on a set of eight instances show that the proposed ETPA outperforms other compared algorithms in terms of the energy consumption of the system.

关键词： unmanned aerial vehicle   mobile edge computing   path planning   deep reinforcement learning   age of information  

摘要： As unmanned aerial vehicles (UAVs) can provide flexible and efficient services concerning the sparse network distribution, we study a UAV-assisted mobile edge computing (MEC) network. To satisfy the freshness requirement of IoT applications, the age of information (AoI) is incorporated as an important performance metric. Then, the path planning problem is formulated to simultaneously minimize the AoIs of mobile devices and the energy consumption of the UAV, where the movement randomness of IoT devices are taken into account. Concerning the dimension explosion, the deep reinforcement learning (DRL) framework is exploited, and a double deep Q-learning network (DDQN) algorithm is proposed to realize the intelligent and freshness-aware path planning of the UAV. Extensive simulation results validate the effectiveness of the proposed freshness-aware path planning scheme and unveil the effects of the moving speed of devices and the UAV on the achieved AoI.

关键词： Task analysis   Sensors   Internet of Things   Optimization   Batteries   Energy harvesting   Quality of service   Mobile edge computing   energy harvesting   cognitive radio system   online optimization algorithm  

摘要： Mobile edge computing (MEC) has recently become a prevailing technique to alleviate the intensive computation burden in Internet of Things (IoT) networks. However, the limited device battery capacity and stringent spectrum resource significantly restrict the data processing performance of MEC-enabled IoT networks. To address the two performance limitations, we consider in this paper an MEC-enabled IoT system with a wireless device (WD) replenishing its battery by means of energy harvesting (EH) and opportunistically accessing the licensed spectrum of an overlaid primary communication link to offload its sensing data to an MEC server (MS) for edge processing. Under time-varying fading channel, random energy arrivals, and stochastic ON-OFF state of the primary link, we aim to design an online algorithm to jointly control the cognitive data sensing rate and processing method (i.e., local and edge processing) without knowing future system information. In particular, we aim to maximize the long-term average sensing rate of the WD subject to quality of service (QoS) requirement of primary link, average power constraint of MS and data queue stability of both MS and WD. We formulate the problem as a multi-stage stochastic optimization and propose an online algorithm named PLySE that applies the perturbed Lyapunov optimization technique to decompose the original problem into per-slot deterministic optimization problems. For each per-slot problem, we derive the closed-form optimal solution of data sensing and processing control to facilitate low-complexity real-time implementation. Interestingly, our analysis finds that the optimal solution exhibits an threshold-based structure related to the current energy state, secondary queueing backlogs and primary link activity. Simulation results collaborate with our analysis and demonstrate more than 46.7% data sensing rate improvement of the proposed PLySE over representative benchmark methods.

关键词： Mobile edge computing   Computation offloading   CMOP   Fine-grained task offloading scheduling   NSGA-II   Parallel computing  

摘要： In mobile edge computing (MEC), task offloading can solve the problem of resource constraints on mobile devices effectively, but it is not always optimal to offload all the computation subtasks of an application. In order to solve the energy and delay of fine-grained offloading in MEC, this paper proposes a novel offloading scheduling based on multi-user fine-grained for IoT. In order to optimize the execution location and scheduling order of subtasks, we regard the computation task as a directed acyclic graph (DAG). Considering the energy consump -tion and delay, the computation offloading is considered as a constrained multi-objective optimization problem (CMOP), and then an improved NSGA-II algorithm is proposed to solve the CMOP. The proposed algorithm can realize local and edge parallel processing, which can effectively reduce the delay and energy consumption. Finally, extensive experiments show that the proposed algorithm is significantly better than the related algorithms in terms of energy and delay. Compared with no-segmentation and related segmentation algorithms, the proposed algorith -m can achieve up 10-50% reduction about energy saving. And the proposed algorithm can make the optimal decision in practical applications.

关键词： Task analysis   Energy consumption   Optimization   Schedules   Wireless communication   Time-frequency analysis   Throughput   Intelligent reflecting surface   phase shifts   mobile edge computing   computation offloading   flat-fading channel   frequency-selective channel  

摘要： Intelligent reflecting surface (IRS) has been increasingly considered in mobile edge computing (MEC), assisting smart terminals (STs) in offloading computationally-intense tasks to base stations (BSs). This paper presents a new IRS-assisted MEC framework, which jointly optimizes the local CPU frequencies of the STs, the receive beamformers of the BS, the ST offloading schedules, and the IRS phase configuration, to minimize the energy consumption of the STs. To this end, we reveal that the optimal CPU frequency is time-invariant for each ST. Under flat-fading channels, the IRS phases and the receive beamformers of the BS can be then decoupled from the offloading schedules. Based on this structure, we develop an alternating optimization to solve the IRS phase configuration and the receive beamformers, and then exploit the Lagrange duality method to solve the offloading schedules. We prove that the overall algorithm is guaranteed to compute a stationary point solution for the problem of interest with a low complexity. Under frequency-selective channels, we also develop a new alternating optimization algorithm to minimize the energy consumption, where manifold optimization is leveraged to effectively solve the IRS phase shifts. Numerical results show that the proposed algorithms are superior to existing techniques in terms of energy efficiency under both flat-fading and frequency-selective channels.

关键词： Mobile edge computing   MES deployment   K-Means   Task offloading  

摘要： Recent years have witnessed the effect of Mobile Edge Computing (MEC) during resource-intensive and time-critical applications toward various mobile devices. Therefore, Mobile Edge Severs (MES) are widely deployed adjacent to the 5G Base Station (BS) to upgrade the performance of the specific application system. Unfortunately, there have rare researches for the location planning of edge servers in the MEC scenario. The deployment of MES may cover a wide range of theoretical concerns, such as computation offloading cost, system performance. In this paper, we consider the problem of optimization of MES deployment in multiple BSs scenarios. To achieve this, we proposed an approach based on the improved K-Means clustering to determine the theoretical location and amount of edge servers. Besides, mobile computation tasks are strategically assigned to the distance-first edge server. To this end, we then develop a reasonable deployment scheme based on K-means for edge servers, which can effectively reduce the network delay, energy consumption, and cost of edge servers. We have compared the density-based clustering algorithm proposed in the recent research. Extensive simulation results indicate that our strategy reduces average completion time by 15.7%, power consumption by 22%, and overhead by 19% in edge server deployment issues.

关键词： Mobile edge computing  

摘要： With the help of 5G and mobile edge computing, business owners, manufacturing facility managers and control engineers are experiencing the benefits of transforming traditional, manual processes into automated, efficient technological solutions. Massive transformation is expected to change the way manufacturing facilities operate with the help of 5G in the coming years as manufacturers consider how leveraging technology.

关键词： autonomous vehicles   dictionary learning   internet of vehicles   mobile edge computing   multitarget tracking  

摘要： With the advancements in 5G network and mobile edge computing technology, autonomous vehicle technology has gained new development opportunities. Multitarget tracking becomes the research hotspots in autonomous vehicles. Since many factors such as motion blur, partial occlusion, and illumination changes affect the performance of target tracking, the problem of target tracking is still an open topic. In this article, inspired by the strong discriminative ability of dictionary learning, the discriminative dictionary learning with embedded auto-encoder (DDLEA) algorithm is developed for the multitarget tracking system. The DDLEA algorithm integrates the auto-encoder into the dictionary learning framework and learns sparse representations while preserving the local structure and discriminative information of data. The learned dictionary model has the strong recognition ability. Further, a multitarget tracking system is developed based on the proposed DDLEA algorithm and the hierarchical data association scheme. Based on the target confidence in the STKSVD model, the hierarchical data association method first uses the Hungarian algorithm to complete the preliminary matching of high confidence targets, and then further tracks the low confidence targets to improve the tracking ability. Experiments are carried out the public MOT 2015 dataset. Compared with several popular multitarget tracking algorithms, the tracking performance of our system is satisfactory.

关键词： Servers   Wireless communication   Task analysis   Image edge detection   Computational modeling   Energy consumption   Resource management   Mobile edge computing   computation offloading   service caching   personalized service preference   computation latency   sum-utility maximization  

摘要： Mobile edge computing (MEC) has emerged as an attractive solution by executing computation-intensive services at a powerful edge server instead of mobiles. Two types of data are necessary to this end. One is user-specific data acquired from mobiles, called computation offloading (CO). The other is service-specific data downloaded from a central cloud, called service caching (SC). It is noteworthy that CO and SC decisions are coupled when each user's service preference (SP) is personalized. Specifically, noting that the optimal SC is to cache services likely to be requested more frequently, the resultant SC tends to be biased to the SP of the user whose offloading rate is high. On the other hand, such an SC decision causes longer computing latency of users with a relatively low offloading rate, which ultimately limits a CO decision for agile MEC services. This work tackles this issue from a sum-utility maximization perspective under radio-resource and computation-latency constraints. The average computation latency is first derived in closed-form by modeling a computation as a stochastic process following a hyper-exponential distribution. Based on it, we first consider the case for homogeneous SP where CO and SC decisions are decoupled. Thus, SC can be deterministically controlled using the homogeneous SP, while CO decision is independently determined, lying between water-filling and channel-inversion allocations. Next, we design a joint CO-and-SC policy for heterogeneous SP. CO and SC decisions are iteratively optimized with the other fixed by leveraging the homogeneous SP's result. The optimal stopping rules are derived, guaranteeing the sum-utility enhancement. The proposed algorithm's effectiveness is verified by simulations that the proposed CO-and-SC design for heterogenous SP always outperforms that for homogeneous SP.