关键词： Combinatorial optimization problem   indoor localization   mobile edge computing   multimodel framework   NP-hardness  

摘要： Location estimation technology under the wireless environment has become a vital technology in the field of mobile edge computing. Especially, under the mobile edge of entire networks environment, indoor location estimation is gradually getting the interest research and application topic, due to technical constraints of global positioning system technology for indoor environment and the popularity of the mobile edge computing servers. In this paper, the widely used single-model framework for indoor localization is presented as an introduction, which consists of three stages: 1) sample data collection;2) model building;and 3) localization estimation. And then, through analyzing of the actual scene of indoor localization, a new framework for indoor localization under mobile edge computing environment, named Multimodel, is proposed from the theoretical perspective. It is mainly based on the observation that the environment of the sample data collection and that of localization data collection may change seriously. In order to make up for the shortcomings of this framework, two combinatorial optimization problems are proposed. Later, we discuss the NP-hardness of them in several different cases. In addition, two heuristic algorithms are given, and the performance of which are illustrated by the corresponding experimental results.

关键词： VANET   mobile edge computing   SDN   offloading   latency   5G  

摘要： Vehicular ad hoc networks (VANETs) are a recent class of peer-to-peer wireless networks that are used to organize the communication and interaction between cars (V2V), between cars and infrastructure (V2I), and between cars and other types of nodes (V2X). These networks are based on the dedicated short-range communication (DSRC) IEEE 802.11 standards and are mainly intended to organize the exchange of various types of messages, mainly emergency ones, to prevent road accidents, alert when a road accident occurs, or control the priority of the roadway. Initially, it was assumed that cars would only interact with each other, but later, with the advent of the concept of the Internet of things (IoT), interactions with surrounding devices became a demand. However, there are many challenges associated with the interaction of vehicles and the interaction with the road infrastructure. Among the main challenge is the high density and the dramatic increase of the vehicles' traffic. To this end, this work provides a novel system based on mobile edge computing (MEC) to solve the problem of high traffic density and provides and offloading path to vehicle's traffic. The proposed system also reduces the total latency of data communicated between vehicles and stationary roadside units (RSUs). Moreover, a latency-aware offloading algorithm is developed for managing and controlling data offloading from vehicles to edge servers. The system was simulated over a reliable environment for performance evaluation, and a real experiment was conducted to validate the proposed system and the developed offloading method.

关键词： mobile edge computing   active safe driving   cooperative platoon driving   cooperative adaptive cruise control  

摘要： Cooperative adaptive cruise control (CACC) for human and autonomous self-driving aims to achieve active safe driving that avoids vehicle accidents or traffic jam by exchanging the road traffic information (e.g., traffic flow, traffic density, velocity variation, etc.) among neighbor vehicles. However, in CACC, the butterfly effect is encountered while exhibiting asynchronous brakes that easily lead to backward shock-waves and are difficult to remove. Several critical issues should be addressed in CACC, including (i) difficulties with adaptive steering of the inter-vehicle distances among neighbor vehicles and the vehicle speed, (ii) the butterfly effect, (iii) unstable vehicle traffic flow, etc. To address the above issues in CACC, this paper proposes the mobile edge computing-based vehicular cloud of the cooperative adaptive driving (CAD) approach to avoid shock-waves efficiently in platoon driving. Numerical results demonstrate that the CAD approach outperforms the compared techniques in the number of shock-waves, average vehicle velocity, average travel time and time to collision (TTC). Additionally, the adaptive platoon length is determined according to the traffic information gathered from the global and local clouds.

关键词： Mobile edge computing   Cloudlet   Computation offloading   Offloading failure   Reinforcement learning  

摘要： Cloud of Things (CoT) is a significant paradigm for bridging cloud resource and mobile terminals. Mobile edge computing (MEC) is a supporting architecture for CoT. The objectives of this paper are to describe and evaluate a method to handle the computation offloading problem during user mobility which minimizes the offloading failure rate in heterogeneous network. Furthermore, users' mobility and their choices for offloading lead to the everchanging condition of wireless network and opportunistic resource available. By modeling such dynamic mobile edge environment, quantizing the user cost, failure penalty and diversified QoS requirements, computation offloading problem is converted into an online decision-making problem in a stochastic process. We divide the decision-making into two phases: offloading planning phase and offloading running phase. In both phases the learning agent can continuously improve the control policy. We also conduct a failure recovery policy to tackle different types of failure and is included in the decision-making process. The numerical results show that the proposed online learning offloading method for mobile users can derive the optimal offloading scheme compared with the baseline algorithms. (C) 2019 Published by Elsevier Inc.

关键词： Distributed machine learning   federated learning   mobile edge computing   wireless networking  

摘要： Emerging technologies and applications including Internet of Things, social networking, and crowd-sourcing generate large amounts of data at the network edge. Machine learning models are often built from the collected data, to enable the detection, classification, and prediction of future events. Due to bandwidth, storage, and privacy concerns, it is often impractical to send all the data to a centralized location. In this paper, we consider the problem of learning model parameters from data distributed across multiple edge nodes, without sending raw data to a centralized place. Our focus is on a generic class of machine learning models that are trained using gradient-descent-based approaches. We analyze the convergence bound of distributed gradient descent from a theoretical point of view, based on which we propose a control algorithm that determines the best tradeoff between local update and global parameter aggregation to minimize the loss function under a given resource budget. The performance of the proposed algorithm is evaluated via extensive experiments with real datasets, both on a networked prototype system and in a larger-scale simulated environment. The experimentation results show that our proposed approach performs near to the optimum with various machine learning models and different data distributions.

关键词： Mobile edge computing  

摘要： To support emerging real-time monitoring and control applications, the timeliness of computation results is of critical importance to mobile-edge computing (MEC) systems. We propose a performance metric called age of task (AoT) based on the concept of age of information (AoI), to evaluate the temporal value of computation tasks. In this paper, we consider a system consisting of a single MEC server and one mobile device running several applications. We study an age minimization problem by jointly considering task scheduling, computation offloading and energy consumption. To solve the problem efficiently, we propose a light-weight task scheduling and computation offloading algorithm. Through performance evaluation, we show that our proposed age-based solution is competitive when compared with traditional strategies. Copyright © 2019, The Authors. All rights reserved.

关键词： 5G   Computation offloading   Mobile edge computing   Energy saving   Internet of Things  

摘要： Mobile edge computing (MEC), as the key technology to improve user experience in a 5G network, can effectively reduce network transmission delay. Task migration can migrate complex tasks to remote edge servers through wireless networks, solving the problems of insufficient computing capacity and limited battery capacity of mobile terminals. Therefore, in order to solve the problem of how to realize low-energy migration of complex dependent applications, a subtask partitioning model with minimum energy consumption is constructed based on the relationship between the subtasks. Aiming at the problem of execution time constraints, the genetic algorithm is used to find the optimal solution, and the migration decision results of each subtask are obtained. In addition, an improved algorithm based on a genetic algorithm is proposed to dynamically adjust the optimal solution obtained by genetic algorithm by determining the proportion of task energy consumption and mobile phone residual power. According to the experimental results, it can be concluded that the fine-grained task migration strategy combines the advantages of mobile edge computing, not only satisfies the smooth execution of tasks, but also reduces the energy consumption of terminal mobile devices. In addition, experiments show that the improved algorithm is more in line with users' expectations. When the residual power of mobile devices is reduced to a certain value, tasks are migrated to MEC server to prolong standby time.

关键词： Mobile edge computing  

摘要： Mobile edge computing (MEC) is an emerging paradigm where users offload computationally intensive job s to the Access Point (AP). Given that the AP's resources are shared by selfish mobile users, pricing is a useful tool for incentivising users to internalize the negative externality of delay they cause to other users. Different users have different negative valuations towards delay as some are more delay sensitive. To serve heterogeneous users, we propose a priority pricing scheme where users can get served first for a higher price. Our goal is to find the prices such that in decision making, users will choose the class and the offloading frequency that jointly maximize social welfare. With the assumption that the AP knows users' profit functions, we derive in semi-closed form the optimal prices. However in practice, the reporting of users's profit information incurs a large signalling overhead. Besides, in reality users might falsely report their private profit information. To overcome this, we further propose learning-based pricing mechanisms wher e no knowledge of individual user profit functions is required. At equilibrium, the optimal prices and average edge delays are learnt, and users have chosen the correct priority class and offload at the socially optimal frequency. Copyright © 2019, The Authors. All rights reserved.

关键词： Mobile edge computing  

摘要： Federated Learning (FL) is capable of leveraging massively distributed private data, e.g., on mobile phones and IoT devices, to collaboratively train a shared machine learning model with the help of a cloud server. However, its iterative training process results in intolerable communication latency, and causes huge burdens on the backbone network. Thus, reducing the communication overhead is critical to implement FL in practice. Meanwhile, the model performance degradation due to the unique non-IID data distribution at different devices is another big issue for FL. In this paper, by introducing the mobile edge computing platform as an intermediary structure, we propose a hierarchical FL architecture to reduce the communication rounds between users and the cloud. In particular, a Hierarchical Federated Averaging (HierFAVG) algorithm is proposed, which allows multiple local aggregations at each edge server before one global aggregation at the cloud. We establish the convergence of HierFAVG for both convex and non-convex objective functions with non-IID user data. It is demonstrated that HierFAVG can reach a desired model performance with less communication, and outperform the traditional Federated Averaging algorithm. Copyright © 2019, The Authors. All rights reserved.

关键词： Congestion awareness   Dynamic offloading   Mobile edge computing  

摘要： In this paper, we focus on the problem of assigning resources for offloading the computationally intensive tasks of mobile applications from resource-scarce mobile devices to servers located in edge networks to minimize the average completion time of the applications. We consider a system model in which a set of mobile devices is connected to an edge server via a shared communication channel. In addition, we study for the first time the offloading problem for general applications by accounting for the capacity limitations of both the communication channel and the edge server. We first formulate a static offloading problem as a mixed-integer linear programming problem. Then, we extend the static problem to a dynamic offloading problem in which an application can be executed at any time. Due to the complexity of the problem, it is difficult to obtain a solution within a realistic time period. Therefore, we propose an efficient heuristic approach based on congestion awareness. We demonstrate that our proposed heuristic algorithm significantly outperforms previous offloading algorithms in terms of the average completion time.