关键词： Mobile edge computing   vehicular networks   computation offloading   resource allocation  

摘要： The emergence of computation-intensive and delay-sensitive vehicular applications poses a great challenge for individual vehicles with limited computation resources. Mobile edge computing (MEC) is a new paradigm shift that can enhance vehicular services through computation offloading. However, the high mobility of vehicles will affect offloading performance. In this paper, we investigate the vehicular user (VU) computation overhead minimization problem in MEC-enabled vehicular networks by jointly optimizing the computation and communication resources' allocation (transmit power and uploading time for communication, and the offloading ratio and local CPU frequency for computation). This optimization problem is nonconvex and difficult to solve directly. To deal with this issue, we first transform the original problem into an equivalent one. Then, we decompose the equivalent problem into a two-level problem. In addition, we develop a low-complexity algorithm to obtain the optimal solution. The numerical results demonstrate that the proposed algorithm can significantly outperform benchmark algorithms in terms of computation overhead.

关键词： Sharing economy   cognitive processing at the edge   mobile edge computing   Blockchain   smart city  

摘要： In this paper, we propose a Blockchain-based infrastructure to support security- and privacy-oriented spatio-temporal smart contract services for the sustainable Internet of Things (IoT)-enabled sharing economy in mega smart cities. The infrastructure leverages cognitive fog nodes at the edge to host and process offloaded geo-tagged multimedia payload and transactions from a mobile edge and IoT nodes, uses AI for processing and extracting significant event information, produces semantic digital analytics, and saves results in Blockchain and decentralized cloud repositories to facilitate sharing economy services. The framework offers a sustainable incentive mechanism, which can potentially support secure smart city services, such as sharing economy, smart contracts, and cyber-physical interaction with Blockchain and IoT. Our unique contribution is justified by detailed system design and implementation of the framework.

关键词： Pricing   mobile cloud computing   mobile edge computing  

摘要： With offloading the tasks that mobile users (MUs) running in their mobile devices (MDs) to the data centers of remote public clouds, mobile cloud computing (MCC) can greatly improve the computing capacity and prolong the battery life of MDs. However, the data centers of remote public cloud are generally far from the MUs, thus long delay will be caused due to the transmission from the base station to the public clouds over the Internet. Mobile edge computing (MEC) is recognized as a promising technique to augment the computation capabilities of MDs and shorten the transmission delay. Nevertheless, compared with the traditional MCC and MEC generally has a limited number of cloud resources. Therefore, making a choice on offloading task to the MCC or MEC is a challenging issue for each MU. In this paper, we investigate service selection in a mobile cloud architecture, in which MUs select cloud services from two cloud service providers (CSPs), i.e., public cloud service provider (PSP) and an edge cloud service provider (ESP).We use M/M/infinity queue and M/M/1 queue to model PSP and ESP, respectively. We analyze the interaction of the two CSPs and MUs by adopting Stackelberg game, in which PSP and ESP set the prices first, and then the MUs decide to select cloud services based on performances and prices. In particular, we study the relationship between PSP and ESP in the simultaneous-play game (SPG) scenario, in which they compete to set prices of their cloud services simultaneously. Our numerical results show that MUs prefer to select service from the edge cloud if the number of tasks they run is small. In another hand, more tasks will be offloaded to the remote public cloud if the number of tasks they run becomes large.

关键词： Intelligent transportation systems   vehicular and wireless technologies   Internet of Things   5G   DSRC   SDN   mobile cloud computing   mobile edge computing  

摘要： Internet of Vehicles is a specific application of Internet of Things technology in intelligent transportation systems, and has attracted attention of relevant research institutions, automobile manufacturers and communication technology suppliers all over the world. The US government has spent hundreds of millions of dollars on DSRC development, which is a technology that can help achieve V2V and V2I communications, and enable vehicles to communicate with intelligent traffic lights to mitigate traffic congestion and accidents or bad weather on the road. Automakers such as GM and Toyota have well deployed Wi-Fi-based DSRC technology. But with the continuous development of 5G and D2D communications, cellular technology tends to be a strong candidate for V2X communications..Although 5G communication technology does not yet have a unified standard, its related concepts and trends have been recognized by industry area. Automobile manufacturers represented by Ford and BMW expect to achieve a leap in automotive networking through 5G. In the face of the 5G era, the dispute between DSRC and LIE standards has become a hot topic in the field of car networking. DSRC, a dedicated short-range communication developed and tested by automotive technology suppliers is under threat, and the next generation of 5G technology might be the final answer. Based on this point, this paper firstly introduces the development history of the Internet of Vehicles communication standard, and analyzes the advantages and disadvantages of DSRC and cellular network communication technology. Then, the three core elements of the Internet of Vehicles (Node Performance, local Network, and Internet of Things) are discussed for the development trend of vehicle networking communication technology in the context of 5G. Finally, this chapter explains the impact of the development of 5G communication technology on the future development of vehicle networking, and proposes the possible development directions of fu

关键词： Energy Internet   mobile edge computing   wind turbine   unmanned aerial vehicle   task offloading  

摘要： As a promising choice of alternative energy, wind power will account for a major part of energy generation in future Energy Internet. With the exploitation of wind power, multiple wind turbines (WTs) are deployed at remote and harsh areas, in which the adverse working environment may lead to enormous WT operating and maintenance costs. Deploying unmanned aerial vehicles (UAVs) for WT detection and sensory data processing in wind farms has been considered as a promising technology to reduce the costs and improve inspection efficiency. In this paper, a mobile edge computing (MEC) driven UAV routine inspection scheme is proposed, in which the UAV not only detects WTs in multiple sorties, but also provides computing and offloading services. To provide seamless communication service, UAV can offload the sensory data to the ground station or satellite optimally. In order to minimize the total completion time, we jointly optimize the UAV trajectory and computation operations, while guaranteeing the data processing accuracy. In the proposed scheme, in order to overcome the influence of wind on UAV trajectory planning, a low complexity WT routine inspection trajectory and UAV scheduling approach is designed firstly. Then, we present an iterative optimization solution to minimize the energy consumption of computation processing, via finding the optimal offloading trajectory and computation offloading parameters. Finally, simulation results show that the proposed scheme can effectively improve the efficiency of UAV routine inspection system performance.

关键词： Service migration   service functions virtualization   mobile edge computing   QoS-awareness  

摘要： 5G systems are envisioned to support numerous delay-sensitive applications such as the tactile Internet, mobile gaming, and augmented reality. Such applications impose new demands on service providers in terms of the quality of service (QoS) provided to the end-users. Achieving these demands in mobile 5G-enabled networks represent a technical and administrative challenge. One of the solutions proposed is to provide cloud computing capabilities at the edge of the network. In such vision, services are cloudified and encapsulated within the virtual machines or containers placed in cloud hosts at the network access layer. To enable ultrashort processing times and immediate service response, fast instantiation, and migration of service instances between edge nodes are mandatory to cope with the consequences of user's mobility. This paper surveys the techniques proposed for service migration at the edge of the network. We focus on QoS-aware service instantiation and migration approaches, comparing the mechanisms followed and emphasizing their advantages and disadvantages. Then, we highlight the open research challenges still left unhandled.

关键词： Mobile edge computing   cognitive radio   wireless power transfer   cooperative relay   semi-closed form  

摘要： Traditional mobile edge computing (MEC) methods always assume that the wireless devices (WDs) can offload their data to the base stations (BSs) or the access points (APs) at any time, which are not practical due to the tension between a large number of the WDs and the limited spectrum resources. In this paper, a framework for MEC-enabled cognitive radio (CR) networks is proposed, which integrates three technologies: MEC, CR, and wireless power transfer (WPT). To obtain the spectrum for offloading, cooperative relaying is considered. Optimization problems are formulated to study the upper bound of the energy efficiency (EE) of the WD and to maximize the practical EE in both partial offloading and local computing scenarios, which are non-convex and intractable. In order to tackle these problems, a two-phase method is proposed. The transmit power, the time for energy harvesting (EH) and MEC, and the central processing unit (CPU) frequency of the WD are jointly optimized. Semi-closed-form solutions are obtained in partial offloading scenario by using fractional programming theory, Lagrangian dual decomposition, and successive pseudo-convex approximation (SPCA) methods. Closed-form solutions are obtained for local computing scenarios. The simulation results show the effects of the different parameters on the system performance.

关键词： Computing capabilities   Hybrid firefly algorithm   Mobile edge computing   Mobile switching center  

摘要： Mobile edge computing (MEC) is a recent technology that brings the computing resources too closer to the network edge with reduced response rate in computationally intensive applications. With increasing mobile devices with limited computing and wireless capabilities, the response time of MSC deteriorates. Therefore it is necessary to jointly manage the computing and wireless resources in order to avoid deterioration due to limited capabilities. Also, the joint optimisation techniques limits its capability due to computational complexity and incompatible devices. To avoid all these limitations, in this paper, we propose a joint optimisation model that suitably optimizes the cloud and wireless resources in an optimal way, thereby reducing its response time. A game base theoretical model is developed to maintain the equilibrium between the resources and computational capabilities. To improve the resources allocation capability, we use the Hybrid Firefly Algorithm (HFA) for better allocation of resources in MEC. The performance is compared with other existing methods to test the effectiveness of the model. © 2019, Institute of Advanced Scientific Research, Inc. All rights reserved.

关键词： Quality of service   Streaming media   Delays   Multimedia systems   Scheduling algorithms   Heuristic algorithms   Optimization   Deep-Q-network   deep reinforcement learning   mobile edge computing   multimedia   quality of service  

摘要： By offloading storage and computing resources to the edge of networks, mobile edge computing (MEC) is emerged as a promising architecture to reduce the transmission delay and bandwidth waste for mobile multimedia services. This paper focuses on a multi-service scenario in the MEC systems, where the MEC server can provide three multimedia services including live streaming, buffered streaming and low latency enhanced mobile broadband applications for edge users at the same time. In order to satisfy various quality of service (QoS) requirements for different multimedia applications, the 5G QoS model is applied. Notably, the packets from the multimedia applications with the same or similar requirements are mapped into the same QoS flow, and each QoS flow is processed individually. Therefore, how to effectively schedule the limited radio resource for QoS flows is an intractable problem. To address the problem above, a QoS evaluation model is designed, and a QoS maximization problem is formulated. Furthermore, a deep reinforcement learning method, deep-Q-network, is adopted to make decisions to allocate radio resource dynamically. Compared with round-robin and priority-based scheduling algorithms, the simulation results validate that the proposed algorithm outperforms other resource scheduling algorithms for multi-service scenario.

关键词： Computing Capabilities   Hybrid Firefly Algorithm   Mobile edge computing   Mobile Switching Center  

摘要： Mobile edge computing (MEC) is a recent technology that brings the computing resources too closer to the network edge with reduced response rate in computationally intensive applications. With increasing mobile devices with limited computing and wireless capabilities, the response time of MSC deteriorates. Therefore it is necessary to jointly manage the computing and wireless resources in order to avoid deterioration due to limited capabilities. Also, the joint optimisation techniques limits its capability due to computational complexity and incompatible devices. To avoid all these limitations, in this paper, we propose a joint optimisation model that suitably optimizes the cloud and wireless resources in an optimal way, thereby reducing its response time. A game base theoretical model is developed to maintain the equilibrium between the resources and computational capabilities. To improve the resources allocation capability, we use the Bees Swarm Optimisation (BSO) model for better allocation of resources in MEC. The performance is compared with other existing methods to test the effectiveness of the model. © 2019, Institute of Advanced Scientific Research, Inc.. All rights reserved.