关键词： Full-duplex   mobile edge computing   offload   simultaneous wireless information and power transfer   group iterative optimization  

摘要： In view of the growing contradiction between the intensive computation demands and the resource limitations of mobile users, mobile edge computing (MEC) and simultaneous wireless information and power transfer (SWIPT) have emerged as new paradigms towards 5G communication. However, coordinating the communication and computation between users and edge servers proves to be challenging for MEC. In this paper, we propose a novel multi-user full-duplex (FD) communication system that combines MEC and SWIPT technology in order to take the advantage of high-speed mobile computing and long-lasting self-sustainability. Through MEC technology, users are able to calculate local computation tasks using their batteries, and can offload partial computation tasks to the base station (BS) to reduce their energy shortage. Moreover, users can refill their batteries while receiving the computation result sent by the BS, thus benefiting from SWIPT technology. The FD mode can potentially increase the system performance by allowing the simultaneous transmitting and receiving of computation tasks. Our work aims to minimize the energy consumption of the system, while formulating resource allocation as a joint non-linear optimization problem. We decouple the original non-convex problem into two subproblems and solve them using a proposed algorithm that applies group iterative optimization. Numerical results prove that the proposed algorithm is superior to other two comparison schemes and can significantly reduce the system energy consumption and the latency.

关键词： Cloud computing   Edge computing   Internet of Things   Market research   Computer architecture   Licenses   Libraries   Fog computing   cloud computing   edge computing   Internet of Things   mobile cloud computing   mobile edge computing  

摘要： In the last few years, Internet of Things, Cloud computing, Edge computing, and Fog computing have gained a lot of attention in both industry and academia. However, a clear and neat definition of these computing paradigms and their correlation is hard to find in the literature. This makes it difficult for researchers new to this area to get a concrete picture of these paradigms. This work tackles this deficiency, representing a helpful resource for those who will start next. First, we show the evolution of modern computing paradigms and related research interest. Then, we address each paradigm, neatly delineating its key points and its relation with the others. Thereafter, we extensively address Fog computing, remarking its outstanding role as the glue between IoT, Cloud, and Edge computing. In the end, we briefly present open challenges and future research directions for IoT, Cloud, Edge, and Fog computing.

关键词： Mobile edge computing   non-orthogonal multiple access   unmanned aerial vehicles   trajectory design  

摘要： Mobile edge computing (MEC) has been considered as a promising technique to address the explosively growing computation-intensive applications. Thanks to the flexibility of the unmanned aerial vehicles (UAVs), the UAV-assisted MEC can serve mobile terminals (MTs) effectively, i.e., the computing server installed on the UAV can flexibly change its location to serve MTs. Moreover, since non-orthogonal multiple access (NOMA) is able to accommodate massive connectivity, the NOMA-based and UAV-assisted MEC can provide flexible computing services for MTs in large-scale access networks (e.g., sensor networks and Internet of Things). However, due to the diversity of the UAV's trajectory and the interference among MTs introduced by NOMA, the performance (e.g., energy consumption and delay) of the NOMA-based and UAV-assisted MEC system is adversely affected. Therefore, in this paper, we formulate an optimization problem to minimize the largest energy consumption among MTs by jointly optimizing the trajectory, task data and computing resource allocations, and then propose an iterative algorithm to solve the optimization problem. Furthermore, to minimize the largest energy consumption among MTs with lower complexity, we propose a fixed point service scheme and optimize the location of the fixed point. The simulation results show that the proposed optimization algorithms can effectively reduce the largest energy consumption among MTs and ensure the fairness among MTs.

关键词： Mobile edge computing   mobile edge caching   5G   content management   content delivery  

摘要： With the widespread adoption of various mobile applications, the amount of traffic in wireless networks is growing at an exponential rate, which exerts a great burden on mobile core networks and backhaul links. Mobile edge caching, which enables mobile edges with cache storages, is a promising solution to alleviate this problem. In this paper, we aim to review the state-of-the-art of mobile edge caching. We first present an overview of mobile edge caching and its advantages. We then discuss the locations where mobile edge caching can be realized in the network. We also analyze different caching criteria and their respective effects on the caching performances. Moreover, we compare several caching schemes and discuss their pros and cons. We further present a detailed and in-depth discussion on the caching process, which can be delineated into four phases including content request, exploration, delivery, and update. For each phase, we identify different issues and review related works in addressing these issues. Finally, we present a number of challenges faced by current mobile edge caching architectures and techniques for further studies.

关键词： Accident detection   fog computing   mobile edge computing   cloud computing   Internet of Things   emergency alerts   disaster management system  

摘要： Emergencies, by definition, are unpredictable and rapid response is a key requirement in emergency management. Globally, a significant number of deaths occur each year, caused by excessive delays in rescue activities. Vehicles embedded with sophisticated technologies, along with roads equipped with advanced infrastructure, can play a vital role in the timely identification and notification of roadside incidents. However, such infrastructure and technologically-rich vehicles are rarely available in less developed countries. Hence, in such countries, low-cost solutions are required to address the issue. Systems based on the Internet of Things (IoT) have begun to be used to detect and report roadside incidents. The majority of the systems designed for this purpose involve the use of the cloud to compute, manage, and store information. However, the centralization and remoteness of cloud resources can result in an increased delay that raises serious concerns about its feasibility in emergency situations;in life-threatening situations, all delays should be minimized where feasible. To address the problem of latency, fog computing has emerged as a middleware paradigm that brings the cloud-like resources closer to end devices. In light of this, the research proposed here leverages the advantages of sophisticated features of smartphones and fog computing to propose and develop a low-cost and delay-aware accident detection and response system, which we term Emergency Response and Disaster Management System (ERDMS). An Android application is developed that utilizes smartphone sensors for the detection of incidents. When an accident is detected, a plan of action is devised. Initially, a nearby hospital is located using the Global Positioning System (GPS). The emergency department of the hospital is notified about the accident that directs an ambulance to the accident site. In addition, the family contacts of the victim are also informed about the accident. All the required comp

关键词： Correlated mobility   edge computing   host deployment   mobile edge computing   resource distribution   user mobility  

摘要： Mobile edge computing (MEC) is the next paradigm to support the enormous growth of diverse mobile applications that require high computational power, low latency, and high bandwidth. The user experience can be enhanced beyond the constrained resources limited by the user equipment by offloading computation-intensive tasks to the mobile edge (ME) hosts. However, ensuring service continuity of the offloaded tasks while users are moving from cell to cell is a critical factor to be kept in mind before selecting the locations suitable for ME host deployment. Therefore, in this paper, we present an optimization model to address the resource distribution problem of mobile users in an ME system so that the total number of tasks accomplished is maximized by considering the user mobility patterns in the network. Despite the fact that the correlation of human mobility during peak hours is present in the traces of urban cities, there is a lack of a correlated human mobility model in the existing literature. We first developed a synthetic correlated mobility model to reproduce the human traces of mobility during morning peak hours. We then developed a utilitarian resource distribution algorithm to solve the ME host deployment and the resource distribution problem. We anticipate that the findings of this paper will contribute to the development of a real-time network management and monitoring system in the ME system to detect changes in MEC resource requirements and optimize MEC resource orchestration based on the predicted user mobility and services' usage demands. Our proposed methods will: 1) become a critical part of the future network management system of 5G that employs MEC;2) contribute to the standardization of the MEC and 5G platforms;and 3) help network operators reduce the massive cost of deploying and operating a large number of distributed servers in the next-generation mobile networks.

关键词： Cost optimization   low latency   mobile edge computing   proximity detection   time-aware road networks   time distance  

摘要： Given a set of moving objects as well as their friend relationships, a time-aware road network, and a time threshold per friend pair, the proximity detection problem in time-aware road networks is to find each pair of moving objects such that the time distance (defined as the shortest time needed for two moving objects to meet each other) between them is within the given threshold. The problem of proximity detection is often encountered in autonomous driving and traffic safety related applications, which require low-latency, real time proximity detection with relatively low communication cost. However, (i) most existing proximity detection solutions focus on the Euclidean space which cannot be used in road network space, (ii) the solutions for road networks focus on static road networks and do not consider time distance and thus cannot be applied in time-aware road networks, and (iii) there are no works aiming to simultaneously reduce the communication cost, the communication latency, and computational cost. Motivated by these, we first design a low-latency proximity detection architecture based on Mobile Edge Computing (MEC) with the purpose of achieving low communication latency, then propose a proximity detection method including a client-side algorithm and a server-side algorithm, aiming at reducing the communication cost, and subsequently propose server-side computational cost optimization techniques to reduce the computational cost. Experimental results show that our MEC enhanced proximity detection architecture, our proximity detection method, and the server-side computational cost optimization techniques can reduce the communication latency, the communication cost, and the computational cost effectively.

关键词： Mobile edge computing   offloading decision   node clustering   optimal strategy   Nash equilibrium  

摘要： Mobile edge computing is a new cloud computing paradigm that utilizes small-sized edge clouds to provide real-time services to users. These mobile edge clouds (MECs) are located near users, thereby enabling users to seamlessly access applications that are running on MECs and to easily access MECs. Terminal devices can transfer tasks to MEC servers nearby to improve the quality of computing. In this paper, we study multi-user computation offloading problem for mobile-edge computing in a multichannel wireless interference environment. Then, we analyze the overhead of each mobile devices, and we propose strategies for task scheduling and offloading in a multi-user MEC system. For reducing the energy consumption, we propose a server partitioning algorithm that is based on clustering. We formulate the task offloading decision problem as a multi-user game, which always has a Nash equilibrium. The simulation results demonstrate that our scheme outperforms the traditional offloading strategy in terms of energy consumption.

关键词： Vehicular networks   mobile edge computing   reinforcement learning   resource allocation  

摘要： The latest research of network and computing contributes greatly to the development of vehicular networks. However, in existing works, these two important enabling technologies are studied separately. To reduce the delay, in this paper, we propose a multi-platform intelligent offloading and resource allocation algorithm which can dynamically organize the computing resources to improve the performance of the next-generation vehicular networks. Considering the task calculation problem, the K-nearest neighbor algorithm is used to select the task offloading platform (i.e., cloud computing, mobile edge computing, or local computing). For the computational resource allocation problem and system complexity in non-local computing, reinforcement learning is used to solve the optimization problem of resource allocation. The simulation results show that compared with the baseline algorithm that all tasks are offloaded to the local or mobile edge computing server, the resource allocation scheme achieves a significant reduction in latency cost, and the average system cost can be saved by 80%.

关键词： Mobile edge computing   resource allocation   unmanned aerial vehicle communications   computation efficiency  

摘要： Energy-efficient computation is important in mobile edge computing (MEC) systems. However, the computation efficiency problem in the unmanned aerial vehicle (UAV)-enabled MEC systems has been rarely researched. In this paper, a UAV-enabled MEC system under partial computation offloading mode is investigated. The computation efficiency is maximized by jointly optimizing the offloading times, the central processing unit frequencies, the transmit powers of the user and the trajectory of the UAV. For the non-convex computation efficiency problem, a two-stage iterative algorithm is proposed. Moreover, we derive the closed-form expressions for the local computation frequency and the transmit power of the user, which facilitates our algorithm implementation. Simulation results demonstrate that the computation efficiency of our proposed joint optimization scheme is better than those of other benchmark schemes.