关键词： Wireless communication   Body area networks   Servers   Task analysis   Medical services   Energy consumption   Games   Mobile edge computing   WBAN   green MEC   task offloading   incentive   5G healthcare  

摘要： With the unprecedented growth of wireless body area network (WBAN) users and computation-intensive 5G-based healthcare applications, mobile edge computing (MEC)-enabled healthcare systems that enable computation offloading to edge servers in proximity, are gaining much interest. However, due to the ever-increasing requirement of WBAN users' quality of experience (QoE), the computational load on the MEC server increases, resulting in high energy costs and heavy carbon emissions. Therefore, in this paper, we focus on joint cost and energy-efficient task offloading in the MEC-enabled healthcare system by designing incentives for WBAN users to curtail their amount of task offloading. In particular, we model the interaction among the MEC server and WBAN users using the Stackelberg game and derive the optimal task offloading decision for WBAN users and corresponding reimbursement amount. As the number of WBAN users is large, we propose an alternating direction method of multipliers (ADMM)-based algorithm to achieve the optimal solution in a distributed manner. Further, simulation results show that the proposed algorithm maximizes the payoffs of both the MEC server and the WBAN users, while also reducing the MEC server energy cost by 52.38% compared to benchmark schemes.

关键词： Mobile edge computing  

摘要： In recent years, deep learning models have become ubiquitous in industry and academia alike. Modern deep neural networks can solve one of the most complex problems today, but coming with the price of massive compute and storage requirements. This makes deploying such massive neural networks challenging in the mobile edge computing paradigm, where edge nodes are resource-constrained, hence limiting the input analysis power of such frameworks. Semantic and layer-wise splitting of neural networks for distributed processing show some hope in this direction. However, there are no intelligent algorithms that place such modular splits to edge nodes for optimal performance. This work proposes a novel placement policy, SplitPlace, for the placement of such neural network split fragments on mobile edge hosts for efficient and scalable computing. © 2022 is held by the owner/author(s).

关键词： Mobile edge computing   Cooperative computation offloading   Delay minimization   Resource allocation  

摘要： Mobile edge computing (MEC) is a promising paradigm, which brings computation resources in proximity to mobile devices and allows the tasks of mobile devices to be offloaded to MEC servers with low latency. The joint problem of cooperative computation task offloading and resource allocation is a challenging issue. The joint problem of cooperative computation task offloading scheme and resource assignment in MEC is investigated in this paper, where the vertical cooperation among mobile devices, mobile edge server nodes and mobile cloud server nodes is considered, and the horizontal computation cooperation between edge nodes is considered as well. A computation offloading decision, cooperative selection, power allocation and CPU cycle frequency assignment problem is formulated. The objective is to minimize the latency while guaranteeing the constraint of transmission power, energy consumption and CPU cycle frequency. The formulated latency optimization problem is a nonconvex mixed-integer problem in general, which has binary variables and continuous variables. In order to solve the formulated problem. A joint iterative algorithm based on the Lagrangian dual decomposition, ShengJin Formula method, and monotonic optimization method is proposed. The CPU cycle frequence allocation is handled by the ShengJin Formula method due to the cubic equation of one variable about the CPU frequence allocation. The transmission power assignment is handled by the monotonic optimization method. In the algorithm convergence with different number of tasks, the proposed algorithm can quickly and effectively reach the convergence state and getting the minimum task execution delay. Numerical results demonstrate that the proposed algorithm outperforms the Full MEC, Full Local and Full Cloud three schemes in terms of execution latency.

关键词： Mobile edge computing  

摘要： It has been tough for the sports industry and the athletes during the COVID-19 pandemic, but in Taiwan, we have been lucky to contain the pandemic to a level that we could hold the 2021 National Intercollegiate Athletic Games (NIAG) in early May. We formed a Sport Technology Team of more than 30 scholars, students, and engineers to provide novel systems and solutions that make the athletic games rich of sport technologies. Some of the features could be the first time shown to the internet audience for large-scale athletic games. The technologies involved include table tennis ball trajectory and bounce distribution, badminton shuttlecock tracking and trajectory, augmented-reality enriched content for real-time video streaming on social networks, real-time 3D broadcasting with wide-field free view-angle, in-stadium video stream pushing by private 5G network with Mobile Edge Computing (MEC), AI-based sport data analytics during live streaming, a Technology-Enhanced Broadcasting System for sport events, etc. This paper introduces the respective technologies that we have developed, deployed, and demonstrated in the 2021 NIAG, Taiwan. We stress the architecture design and integration of TBS, which is a System of Systems, as well as experimental results on real athletic games in the smart stadiums that we have established. © 2021, CC BY-NC-SA.

关键词： Mobile edge computing  

摘要： Mobile edge computing (MEC) emerges as a promising solution for servicing delay-sensitive tasks at the edge network. A body of recent literature started to focus on cost-efficient service placement and request scheduling. This work investigates the joint optimization of service placement and request scheduling in a dense MEC network, and develops an efficient online algorithm that achieves close-to-optimal performance. Our online algorithm consists of two basic modules: (1) a regularization with look-ahead approach from competitive online convex optimization, for decomposing the offline relaxed minimization problem into multiple sub-problems, each of which can be efficiently solved in each time slot;(2) a randomized rounding method to transform the fractional solution of offline relaxed problem into integer solution of the original minimization problem, guaranteeing a low competitive ratio. Both theoretical analysis and simulation studies corroborate the efficacy of our proposed online MEC optimization algorithm. Copyright © 2021, The Authors. All rights reserved.

关键词： Delays   Unmanned aerial vehicles   Processor scheduling   Optimization   Games   Computational modeling   Minimization   UAV swarms   mobile edge computing   delay   scheduling   potential game  

摘要： Coalition-based unmanned aerial vehicle (UAV) swarms havebeen widelyused in urgent missions. To fasten the completion, mobile edge computing (MEC) has been introduced into UAV networks where coalition leaders act as servers to help members with data computing. This paper investigates a relative delay optimization in MEC-assisted UAV swarms. Considering that the scheduling methods have great impact on the delay, some theoretical analyses are made and a scheduling method based on the shortest effective job first (SEJF) is proposed. Based on the coupled relationship between scheduling and resource allocation, the computation offloading and channel access problems are then jointly optimized. To solve the problem in distributed UAV networks, the optimization problem is formulated as an offloading game. It is proved that the game is an exact potential game (EPG) and it has at least one pure strategy Nash Equilibrium (PNE). To reach the PNE, a distributed offloading algorithm based on concurrent best-better response (CBBR) is designed. Finally, the simulations show that the performance of the proposed CBBR algorithm is better than traditional algorithms. Compared with other scheduling methods, the proposed scheduling method based on SEJF reduces the delay by up to 30%.

关键词： Internet of vehicles (IoV)   Road side unit   Mobile edge computing   Reinforcement learning   (Analytic Hierarchy Process-Deep Q Network) AHP-DQN   Task allocation   Offloading strategy  

摘要： Mobile edge computing has been deeply integrated with internet of vehicles (IoV) due to its efficient computing capabilities close to devices. However, the inefficiency of storage and computing capabilities for vehicle terminals is in conflict with the diversification of network application services, which poses a huge challenge to the high-performance computing in IoV. In response to the high-performance computing requirements, a mobile edge computing task distribution and offloading algorithm based on deep reinforcement learning is proposed in order to solve low terminal storage capacity and diversified network service problems. Firstly, taking the energy consumption and transmission bandwidth of vehicle terminals as constraints, this paper establishes a task offloading and resource allocation model based on mathematical model using in-vehicle communication network. Besides, the model takes the maximum task processing rate as the objective function. Secondly, the AHP-DQN framework is used to solve the model, and the optimization variables are allocated according to the real-time state of the network to ensure the better performance of the task allocation algorithm in the multi-user scenario of IoV. Finally, simulation experiments show that the proposed algorithm can effectively realize the effective distribution of computing tasks in IoV.

关键词： Mobile edge computing  

摘要： We consider a mobile edge computing scenario where users want to perform a linear inference operation Wx on local data x for some network-side matrix W. The inference is performed in a distributed fashion over multiple servers at the network edge. For this scenario, we propose a coding scheme that combines a rateless code to provide resiliency against straggling servers-hence reducing the computation latency-and an irregular-repetition code to provide spatial diversity-hence reducing the communication latency. We further derive a lower bound on the total latency-comprising computation latency, communication latency, and decoding latency. The proposed scheme performs remarkably close to the bound and yields significantly lower latency than the scheme based on maximum distance separable codes recently proposed by Zhang and Simeone. Copyright © 2021, The Authors. All rights reserved.

关键词： Mobile edge computing   Popularity prediction  

摘要： In this paper, we study the cache prediction problem for mobile edge networks where there exist one base station (BS) and multiple relays. For the proposed mobile edge computing (MEC) network, we propose a cache prediction framework to solve the problem of contents prediction and caching based on neural networks and relay selection, by exploiting users' history request data and channels between the relays and users. The proposed framework is then trained to learn users' preferences by using the users' history requested data, and several caching policies are proposed based on the channel conditions. The cache hit rate and latency are used to measure the performance of the proposed framework. Simulation results demonstrate the effectiveness of the proposed framework, which can maximize the cache hit rate and meanwhile minimize the latency for the considered MEC networks.

关键词： Mobile edge computing  

摘要： An intelligent reflecting surface (IRS)-aided wireless powered mobile edge computing (WP-MEC) system is conceived, where each device's computational task can be divided into two parts for local computing and offloading to mobile edge computing (MEC) servers, respectively. Both time division multiple access (TDMA) and non-orthogonal multiple access (NOMA) schemes are considered for uplink (UL) offloading. Given the capability of IRSs in intelligently reconfiguring wireless channels over time, it is fundamentally unknown which multiple access scheme is superior for MEC UL offloading. To answer this question, we first investigate the impact of three different dynamic IRS beamforming (DIBF) schemes on the computation rate of both offloading schemes, based on the flexibility for the IRS in adjusting its beamforming (BF) vector in each transmission frame. Under the DIBF framework, computation rate maximization problems are formulated for both the NOMA and TDMA schemes, respectively, by jointly optimizing the IRS passive BF and the resource allocation. We rigorously prove that offloading adopting TDMA can achieve the same computation rate as that of NOMA, when all the devices share the same IRS BF vector during the UL offloading. By contrast, offloading exploiting TDMA outperforms NOMA, when the IRS BF vector can be flexibly adapted for UL offloading. Despite the non-convexity of the computation rate maximization problems for each DIBF scheme associated with highly coupled optimization variables, we conceive computationally efficient algorithms by invoking alternating optimization. Our numerical results demonstrate the significant performance gains achieved by the proposed designs over various benchmark schemes and also unveil that the optimal time allocated to downlink wireless power transfer can be effectively reduced with the aid of IRSs, which is beneficial for both the system's spectral efficiency and its energy efficiency. Copyright © 2021, The Authors. All rights re