关键词： Mobile edge computing   unmanned aerial vehicle ( UAV)   unloading decision   joint optimization problem   USS algorithm  

摘要： Due to the birth of various new Internet of Things devices, the rapid increase of users, and the limited coverage of infrastructure, computing resources will inevitably become insufficient. Therefore, we consider an unmanned aerial vehicle (UAV)-assisted mobile edge computing system with multiple users, an edge server, a remote cloud server, and an UAV. A UAV, as a relay node, can provide users with extensive communications and certain computing capabilities. Our proposed scheme aims to optimize the unloading decision of the tasks among all users and the allocation of computing and communication resources to minimize overall energy consumption and costs of computing andmaximum delay. To solve the joint optimization problem, we propose an efficient USS algorithm, which includes a UAV position optimization algorithm, semi-qualitative relaxation method, and self-adaptive adjustment method. Our numerical results show that the proposed algorithm can significantly reduce the unloading cost of multi-user tasks compared with four other unloading decisions, such as traditional cloud computing, which uses only the edge server.

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： Lightweight AI model   Nested neural network   Motion deblurring   Mobile edge computing  

摘要： Motion deblurring methods based on convolutional neural networks (CNN) have recently demonstrated their advantages over conventional methods. However, repetitions of scaling or slicing operations of these methods on the input images inevitably lead to spatial information loss. Meanwhile, some recent methods based on complex models inevitably bring a large model size and huge computing cost. It is still challenging to balance the deblurring performance and the cost. To this end, we propose a lightweight nested network (LNNet) for the motion-deblurring task. Our LNNet leverages several simple yet efficient sub-networks to process motion deblurring features at each stage. We design a nested connection, which is conducive to the model size reduction when connecting sub-networks so as to reuse deblurring information and facilitate deblurring information diversity. Meanwhile, we introduce the feature-fusion module to improve deblurring performance further. We perform extensive experiments on a workstation platform and an embedded mobile edge computing (MEC) platform to evaluate our LNNet as well as other existing methods. Extensive experimental results demonstrate that our LNNet achieves superior deblurring performance than state-of-the-art methods with a small model size within a short running time. Moreover, experimental results also show that our model is quite suitable for other embedded devices.

关键词： Mobile edge computing   Authentication   Security   User anonymity  

摘要： Mobile edge computing can meet the needs of users in real time by pushing cloud resources, such as computing, network, and storage, to the edge of a mobile network. Although mobile edge technology has brought many conveniences to mobile services, many security problems, such as mutual authentication between mobile users and servers, and the impersonation of mobile users or servers, have not been properly solved. In this paper, we propose a lightweight and secure authentication protocol to solve such security problems. We conducted a formal security analysis of our protocol using ProVerif and the Real-or-Random model to prove its correctness and security, and even if the registration center is attacked, our protocol is still secure. At the end of this paper, by comparing the communication and computing costs with other existing protocols, we prove the security and efficiency of our protocol.

关键词： Mobile edge computing  

摘要： With the rapid development of Internet of Things technology and interactive applications, the number of terminal devices in the network is increasing, and the development of interactive applications is hindered by network delay. To solve the network delay, bandwidth, and workload requirements in the new era, edge computing came into being. Edge computing aims to implement computing, storage, communication, and other services at the edge of the network by sinking cloud services from the core network to the edge of the network. Current research studies pay less attention to the impact of edge server location on the system performance, and edge server deployment is one of the key technologies for mobile edge computing. Therefore, we take 5G macrocellular/microcellular cluster as the edge server deployment scenario, propose an equivalent bandwidth-based deployment strategy, establish a mathematical model for edge server deployment, and contract a task experience function as an evaluation index from two aspects: task time and energy overhead. Based on the analysis of the experimental results, it is verified that the deployment strategy based on equivalent bandwidth is superior to other deployment strategies in terms of terminal device task overhead.

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： 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   computation offloading   network uncertainties   energy efficiency  

摘要： Computation offloading is indispensable for mobile edge computing (MEC). It uses edge resources to enable intensive computations and save energy for resource-constrained devices. Existing works generally impose strong assumptions on radio channels and network queue sizes. However, practical MEC systems are subject to various uncertainties rendering these assumptions impractical. In this paper, we investigate the energy-efficient computation offloading problem by relaxing those common assumptions and considering intrinsic uncertainties in the network. Specifically, we minimize the worst-case expected energy consumption of a local device when executing a time-critical application modeled as a directed acyclic graph. We employ the extreme value theory to bound the occurrence probability of uncertain events. To solve the formulated problem, we develop an c-bounded approximation algorithm based on column generation. The proposed algorithm can efficiently identify a feasible solution that is less than (1 + epsilon) of the optimal one. We implement the proposed scheme on an Android smartphone and conduct extensive experiments using a real-world application. Experiment results corroborate that it will lead to lower energy consumption for the client device by considering the intrinsic uncertainties during computation offloading. The proposed computation offloading scheme also significantly outperforms other schemes in terms of energy saving.

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： Channel estimation   Signal processing algorithms   Convolutional neural networks   Encoding   Deep learning   Training   Frequency-domain analysis   V2X   mobile edge computing   channel estimation   deep learning   edge intelligence  

摘要： The fifth generation New Radio vehicle-to-everything (5G NR-V2X) has higher requirements for delay and reliability, which brings challenges to the physical layer signal processing. Mobile edge computing (MEC) can provide larger capacity data storage and more efficient computation through localization of on-board unit (OBU) and next generation Node B (gNB) services. This paper combines the channel estimation of New Radio vehicle-to-infrastructure (NR-V2I) communication baseband signal processing with MEC and designs an intelligent channel estimation framework. In the MEC server, this paper proposes a channel estimation algorithm based on deep learning. This algorithm uses a one-dimensional convolutional neural network (1D CNN) to complete frequency-domain interpolation and conditional recurrent unit (CRU) for time-domain state prediction. Additional velocity coding vector and multipath coding vector track changes in the environment, and accurately train channel data in different mobile environments. System simulation and analysis show that the proposed algorithm improves the channel estimation accuracy, reduces the bit error rate, and enhances the robustness compared with the representative channel estimation algorithms for NR-V2I.