关键词： Reconfigurable intelligent surface   mobile edge computing   edge learning   ADMM  

摘要： The ever-growing popularity and rapid development of artificial intelligence (AI) have raised rethinking on the evolution of wireless networks. Mobile edge computing (MEC) provides a natural platform for AI applications since has rich computation resources to train machine learning (ML) models, as well as low-latency access to the data generated by mobile and Internet of things (IoT) devices. In this article, we present an infrastructure to perform ML tasks at an MEC server with the assistance of a reconfigurable intelligent surface (RIS). In contrast to conventional communication systems where the principal criterions are to maximize the throughput, we aim at maximizing the learning performance. Specifically, we minimize the maximum learning error of all participating users by jointly optimizing transmit power of mobile users, beamforming vectors of the base station (BS), and the phase-shift matrix of the RIS. An alternating optimization (AO)-based framework is proposed to optimize the three terms iteratively, where a successive convex approximation (SCA)-based algorithm is developed to solve the power allocation problem, closed-form expressions are derived to solve the beamforming design problem, and an alternating direction method of multipliers (ADMM)-based algorithm is designed to efficiently solve the phase-shift matrix design problem. Simulation results demonstrate significant gains of deploying an RIS and validate the advantages of our proposed algorithms over various benchmarks. Lastly, a unified sensing-communication-learning platform is developed based on the CARLA simulator and the SECOND network, and a use case (3D object detection in autonomous driving) for the proposed scheme is demonstrated on the developed platform.

关键词： Mobile edge computing   Latency   Throughput   Traffic decentralization   Mininet  

摘要： In the centralized architecture of traditional mobile cellular network, every traffic of mobile terminals has to traverse the centered network core and then deliver to the corresponding service mobile terminals. Since the traffic load is concentrated on the central network core, it is difficult to deliver the explosive amount of network traffic and guarantee latency requirements of diversifying applications. In order to solve this problem, the mobile edge computing (MEC) architecture that distributes the traffic load by locating the computing and caching server closer to the terminal is currently being developed. In this paper, the effect of distribution of mobile traffic with MEC is evaluated in terms of throughput enhancements and latency reduction. The simulations were conducted using Mininet to determine what performance gains can be obtained when applying the MEC architecture to the mobile cellular network. As a result, under the same link bandwidth condition, MEC architecture provides an increase in data rates of approximately 265% for high-quality video transmission and about 162% for low-quality video transmission, and shows the reduced delay of packet arrival times. (C) 2021 The Korean Institute of Communications and Information Sciences (KICS). Publishing services by Elsevier B.V.

关键词： Wireless communication   Servers   Optimization   Interference   Convergence   Resource management   Computational modeling   Artificial neural networks   caching   combinatorial optimization   computation offloading   heterogeneous networks   long term evolution   mobile edge computing   resource allocation  

摘要： This paper explores the problem of joint computational offloading and content caching (OCP) in the wireless heterogeneous mobile edge computing (MEC) system, where each small-cell base station (BS) is equipped with the MEC server having the content caching/processing capabilities. The communication and computing resources of the system are allocated to users requesting or offloading their contents via the BSs to minimize the system-wide computational overhead. Due to the non-deterministic polynomial time hardness of the OCP, it is difficult to solve it with an exact integer-programming (IP) method. Instead, the problem is solved by adopting the Hopfield neural network (HNN) based approach. In particular, the HNN model representing the OCP is constructed. The global energy minimum of the model corresponds to a solution of the OCP. However, because of the negativity of diagonal weights, the convergence of this model to a stable state cannot be guaranteed. Subsequently, a range of "synthetic" HNN models with the global convergence property is developed to replace the original HNN. Based on these models, three different search algorithms are formulated and implemented in a long-term evolution advanced network. The algorithms demonstrate an improved performance when compared to other relevant IP methods in simulations.

关键词： Mobile edge computing   wireless powered   user cooperation   non-orthogonal multiple access   computation efficiency  

摘要： In this article, we investigate the application of user cooperation (UC) and non-orthogonal multiple access (NOMA) schemes for a wireless powered mobile edge computing (MEC) system under the non-linear energy harvesting model, in which two single-antenna mobile users first harvest energy from a multi-antenna access point (AP) integrated with an MEC server. Then, during the computation offloading phase, both mobile users simultaneously offload tasks to the MEC server with the harvested energy, by performing NOMA protocol. To better enhance the system performance, UC scheme is carried out, where the near user acts as a relay to help the far user offload computation tasks to the AP. To obtain energy efficient MEC design, our objective is to maximize the computation efficiency (i.e., the total computation bits divided by the consumed energy) by jointly designing the energy beamforming, time and power allocations, which yields a challenging nonconvex optimization problem. To deal with it, the original problem is first transformed into a more tractable formulation by applying the semidefinite relaxation (SDR) technique and then solved by utilizing the sequential convex approximation (SCA) method. Numerical results demonstrate that UC has a great impact when two users are close, while NOMA makes effect when two users are relatively far. Combining both NOMA and UC, the proposed scheme, named NOMA-UC MEC, yields better system performance than the benchmark schemes.

关键词： UAV trajectory   Mobile edge computing   Energy minimization   Full offloading   Secure communications  

摘要： This paper designs an unmanned aerial vehicle (UAV)-enabled mobile edge computing (MEC) system. The main work of the UAV is to communicate with multiple ground users, meanwhile it should satisfy the quality and security via sending jamming signal to against the potential eavesdropper. We aim to maximize the secure computation efficiency, subject to the trajectory of the UAV, the minimum secret rate and the expected bits of tasks. Due to the non-convex of the original problem, we exploit the block coordinate and transform the original problem into a tractable problem. We then propose a two-stage alternative optimization algorithm for secure computation efficiency maximization by jointly optimizing the transmission power and UAV's trajectory. The simulation results show that our proposed algorithms consume much less energy compared to other benchmark schemes. (C) 2021 Elsevier B.V. All rights reserved.

关键词： Task analysis   Servers   Buildings   Unmanned aerial vehicles   Vehicle dynamics   Wireless communication   Relays   Mobile edge computing   computation offloading   vehicular networks   unmanned aerial vehicles   game theory  

摘要： Vehicular computation offloading is a well-received strategy to execute delay-sensitive and/or compute-intensive tasks of legacy vehicles. The response time of vehicular computation offloading can be shortened by using mobile edge computing that offers strong computing power, driving these computation tasks closer to end users. However, the quality of communication is hard to guarantee due to the obstruction of dense buildings or lack of infrastructure in some zones. Unmanned Aerial Vehicles (UAVs), therefore, have become one of the means to establish communication links for the two ends owing to its characteristics of ignoring terrain and flexible deployment. To make a sensible decision of computation offloading, nevertheless vehicles need to gather offloading-related global information, in which Software-Defined Networking (SDN) has shown its advances in data collection and centralized management. In this paper, thus, we propose an SDN-enabled UAV-assisted vehicular computation offloading optimization framework to minimize the system cost of vehicle computing tasks. In our framework, the UAV and the Mobile Edge Computing (MEC) server can work on behalf of the vehicle users to execute the delay-sensitive and compute-intensive tasks. The UAV, in a meanwhile, can also be deployed as a relay node to assist in forwarding computation tasks to the MEC server. We formulate the offloading decision-making problem as a multi-players computation offloading sequential game, and design the UAV-assisted Vehicular computation Cost Optimization (UVCO) algorithm to solve this problem. Simulation results demonstrate that our proposed algorithm can make the offloading decision to minimize the Average System Cost (ASC).

关键词： Servers   Distributed databases   Edge computing   Cloud computing   Indexes   Routing   Protocols   Data location service   Cuckoo Summary   greedy routing   mobile edge computing   SDN  

摘要： The hierarchical mobile edge computing satisfies the stringent latency requirements of data access and processing for emerging edge applications. The data location service is a basic function to provide data storage and retrieval to enable these applications. However, it still lacks research of a scalable and low-latency data location service in the environment. The existing solutions, such as DNS and DHT, fail to meet the requirement of those latency-sensitive applications. Therefore, in this article, we present a low-latency hybrid data-sharing framework, HDS. The HDS divides the data location service into two parts: intra-region and inter-region. More precisely, we design a data sharing protocol called Cuckoo Summary to achieve fast data localization in intra-region. Furthermore, for the inter-region data sharing, we develop a geographic routing based scheme to achieve efficient data localization with only one overlay hop. The advantages of HDS include short response latency, low implementation overhead, and few false positives. We implement the HDS framework based on a P4 prototype. The experimental results show that, compared to the state-of-the-art solutions, our design achieves 50.21% shorter lookup paths and 92.75% fewer false positives.

关键词： Mobile edge computing   collaborative offloading   delivery rate  

摘要： Mobile edge computing (MEC) is an emerging computing paradigm for enabling low-latency, high-bandwidth and agile mobile services by deploying computing platform at the edge of network. In order to improve the cloud-edge-end processing efficiency of the tasks within the limited computation and communication capabilities, in this article, we investigate the collaborative computation offloading, computation and communication resource allocation scheme, and develop a collaborative computing framework that the tasks of mobile devices (MDs) can be partially processed at the terminals, edge nodes (EN) and cloud center (CC). Then, we propose the pipeline-based offloading scheme, where both MDs and ENs can offload computation-intensive tasks to a particular EN and CC, according to their computation and communication capacities, respectively. Based on the proposed pipeline offloading strategy, a sum latency of all MDs minimization problem is formulated with the consideration of the offloading strategy, computation resource, delivery rate and power allocation, which is a non-convex problem and difficult to deal with. To solve the optimization problem, by using the classic successive convex approximation (SCA) approach, we transform the non-convex optimization problem into the convex one. Finally, simulation results indicate that the proposed collaboration offloading scheme with the pipeline strategy is efficient and outperforms other offloading schemes.

关键词： Cost Optimization   Distributed Computing   Game Theory   Mobile Edge Computing   Multi MEC Servers   Nash Equilibrium   Task Offloading  

摘要： With the development of mobile edge computing, how to utilize the computing power of edge computing to effectively and efficiently offload data and to compute offloading is of great research value. This paper studies the computation offloading problem of multi-user and multi-server in mobile edge computing. Firstly, in order to minimize system energy consumption, the problem is modeled by considering the joint optimization of the offloading strategy and the wireless and computing resource allocation in a multi-user and multi-server scenario. Additionally, this paper explores the computation offloading scheme to optimize the overall cost. As the centralized optimization method is an NP problem, the game method is used to achieve effective computation offloading in a distributed manner. The decision problem of distributed computation offloading between the mobile equipment is modeled as a multi-user computation offloading game. There is a Nash equilibrium in this game, and it can be achieved by a limited number of iterations. Then, we propose a distributed computation offloading algorithm, which first calculates offloading weights, and then distributedly iterates by the time slot to update the computation offloading decision. Finally, the algorithm is verified by simulation experiments. Simulation results show that our proposed algorithm can achieve the balance by a limited number of iterations. At the same time, the algorithm outperforms several other advanced computation offloading algorithms in terms of the number of users and overall overheads for beneficial decision-making.

关键词： Mobile edge computing   Computation offloading   Context-aware   Federated learning  

摘要： Mobile edge computing (MEC) provides an effective solution to help the Internet of Things (IoT) devices with delay-sensitive and computation-intensive tasks by offering computing capabilities in the proximity of mobile device users. Most of the existing studies ignore context information of the application, requests, sensors, resources, and network. However, in practice, context information has a significant impact on offloading decisions. In this paper, we consider context-aware offloading in MEC with multi-user. The contexts are collected using autonomous management as the MAPE loop in all offloading processes. Also, federated learning (FL)-based offloading is presented. Our learning method in mobile devices (MDs) is deep reinforcement learning (DRL). FL helps us to use distributed capabilities of MEC with updated weights between MDs and edge devices (Eds). The simulation results indicate our method is superior to local computing, offload, and FL without considering context-aware algorithms in terms of energy consumption, execution cost, network usage, delay, and fairness.