关键词： Cloudlets   deep learning   Internet of Things (IoT)   mobile edge computing   mobile healthcare   preventive healthcare   traffic classification   traffic prediction   survey   fog computing   cloud computing   multimedia applications   smart cities  

摘要： Smart city advancements are driving massive transformations of healthcare, the largest global industry. The drivers include increasing demands for ubiquitous, preventive, and personalized healthcare, to be provided to the public at reduced risks and costs. Mobile cloud computing could potentially meet the future healthcare demands by enabling anytime, anywhere capture and analyses of patients' data. However, network latency, bandwidth, and reliability are among the many challenges hindering the realization of next-generation healthcare. This paper proposes a ubiquitous healthcare framework, UbeHealth, that leverages edge computing, deep learning, big data, high-performance computing (HPC), and the Internet of Things (IoT) to address the aforementioned challenges. The framework enables an enhanced network quality of service using its three main components and four layers. Deep learning, big data, and HPC are used to predict network traffic, which in turn are used by the Cloudlet and network layers to optimize data rates, data caching, and routing decisions. Application protocols of the traffic flows are classified, enabling the network layer to meet applications' communication requirements better and to detect malicious traffic and anomalous data. Clustering is used to identify the different kinds of data originating from the same application protocols. A proof of concept UbeHealth system has been developed based on the framework. A detailed literature review is used to capture the design requirements for the proposed system. The system is described in detail including the algorithmic implementation of the three components and four layers. Three widely used data sets are used to evaluate the UbeHealth system.

关键词： Internet of Vehicles   social networks   unmanned aerial vehicles   mobile edge computing   resource allocation  

摘要： Social Internet of Vehicles (SIoV) is a new paradigm that enables social relationships among vehicles by integrating vehicle-to-everything communications and social networking properties into the vehicular environment. Through the provision of diverse socially-inspired applications and services, the emergence of SIoV helps to improve the road experience, traffic efficiency, road safety, travel comfort, and entertainment along the roads. However, the computation performance for those applications have been seriously affected by resource-limited on-board units as well as deployment costs and workloads of roadside units. Under such context, an unmanned aerial vehicle (UAV)-assisted mobile edge computing environment over SIoV with a three-layer integrated architecture is adopted in this paper. Within this architecture, we explore the energy-aware dynamic resource allocation problem by taking into account partial computation offloading, social content caching, and radio resource scheduling. Particularly, we develop an optimization framework for total utility maximization by jointly optimizing the transmit power of vehicle and the UAV trajectory. To resolve this problem, an energy-aware dynamic power optimization problem is formulated under the constraint of the evolution law of energy consumption state for each vehicle. By considering two cases, i.e., cooperation and noncooperation among vehicles, we obtain the optimal dynamic power allocation of the vehicle with a fixed UAV trajectory via dynamic programming method. In addition, under the condition of fixed power, a search algorithm is introduced to derive the optimized UAV trajectory based on acceptable ground-UAV distance metric and the optimal offloaded data size of the vehicle. Simulation results are presented to demonstrate the effectiveness of the proposed framework over alternative benchmark schemes.

关键词： Mobile edge computing   edge service   service maintenance   skyline   skyline graph  

摘要： The emergence of many new computing applications, such as Internet of Vehicles (IoV) and smart homes, has been made possible by the large pool of cloud resources and services. However, the cloud computing paradigm is unable to meet the requirements of delay-sensitive business applications, such as low latency, mobility support, and location awareness. In this context, Mobile Edge Computing (MEC) is introduced to improve the quality of experience (QoE) by bringing cloud resources and services closer to the user by leveraging available resources in the edge networks. However, the performance of MEC is dynamic in nature due to its location awareness, mobility and proximity. As a result, an effective mechanism is needed for providing efficient dynamic service maintenance for edge services. In this paper, we propose applying the Skyline Graph Model and employing the Directed Acyclic Graph theory to store and update mobile edge services. Specifically, the Skyline Graph (SG) algorithm is designed to solve the insertion, deletion, updating and searching of mobile edge services to achieve efficient maintenance for edge services. Comprehensive experiments are conducted on both real-world web services and simulated datasets to evaluate the effectiveness and efficiency of our approaches. The results show that our algorithms can achieve significantly better performance and robustness than the baseline algorithm.

关键词： Computation offloading   mobile edge computing   resource management   optimization  

摘要： Mobile edge computing (MEC) can augment the computation capabilities of mobile terminals (MTs) through offloading the computational tasks from the MTs to the MEC-enabled base station (MEC-BS) covering them. However, the load of MEC-BS will rise as the increase of the scale of tasks. Existing schemes try to alleviate the load of MEC-BS through refusing, postponing, or queuing the offloading requests of the MTs;thus, the users' QoS will largely deteriorate due to service interruption and prolonged waiting and execution time. In this paper, we investigate the cooperations of multiple MEC-BSs and propose a novel scheme to enhance the computation offloading service of an MEC-BS through further offloading the extra tasks to other MEC-BSs connected to it. An optimization algorithm is proposed to efficiently solve the optimization problem which maximizes the total benefits of time and energy consumptions gained by all the MTs covered by the MEC-BS. A balance factor is used to flexibly adjust the bias of optimization between minimizations of time and energy consumption. Extensive simulations are carried out in eight different scenarios, and the results demonstrate that our scheme can largely enhance the system performance, and it outperforms the reference scheme in all scenarios.

关键词： Mobile crowdsensing   mobile edge computing   MCS functional architecture   MEC reference architecture  

摘要： Mobile crowdsensing (MCS) is a human-driven Internet of Things service empowering citizens to observe the phenomena of individual, community, or even societal value by sharing sensor data about their environment while on the move. Typical MCS service implementations utilize cloud-based centralized architectures, which consume a lot of computational resources and generate significant network traffic, both in mobile networks and toward cloud-based MCS services. Mobile edge computing (MEC) is a natural choice to distribute MCS solutions by moving computation to network edge, since an MEC-based architecture enables significant performance improvements due to the partitioning of problem space based on location, where real-time data processing and aggregation is performed close to data sources. This in turn reduces the associated traffic in mobile core and will facilitate MCS deployments of massive scale. This paper proposes an edge computing architecture adequate for massive scale MCS services by placing key MCS features within the reference MEC architecture. In addition to improved performance, the proposed architecture decreases privacy threats and permits citizens to control the flow of contributed sensor data. It is adequate for both data analytics and real-time MCS scenarios, in line with the 5G vision to integrate a huge number of devices and enable innovative applications requiring low network latency. Our analysis of service overhead introduced by distributed architecture and service reconfiguration at network edge performed on real user traces shows that this overhead is controllable and small compared with the aforementioned benefits. When enhanced by interoperability concepts, the proposed architecture creates an environment for the establishment of an MCS marketplace for bartering and trading of both raw sensor data and aggregated/processed information.

关键词： Mobile edge computing   self-organization   small cell   power optimization   coverage and spectral efficiency  

摘要： Mobile edge computing (MEC) technology migrates the cloud computing platform to the edge of the mobile access network, which reduces the end-to-end delay of mobile service delivery and improves the user experience. However, since the number of the deployed small cell base stations (BSs) is larger than the macro BSs, automatic deployment can be used to control deployment costs. In this paper, we study the adaptive boundary algorithm of indoor small cell BSs and propose a power optimization scheme in self-organizing MEC-based heterogeneous small cell networks. According to the actual environment, the indoor small cell BS can be deployed quickly and flexibly. And the coverage of the small cell BS can be optimized by automatically adjusting the power parameters. Considering the coverage and spectral efficiency of the macro BS and the small cell BS, the performance indicators of the optimization program and results evaluation is proposed. Finally, the simulation results show that the proposed algorithm and scheme can control the deployment cost and improve the optimization coverage of macro BSs and small cell BSs under the control cost.

关键词： Mobile edge computing  

摘要： With the advances of new-generation wireless and mobile communication systems such as the fifth-generation (5G) mobile networks and Internet of Things (loT) networks, demanding applications such as Ultra-High-Definition video applications is becoming ever popular. These applications require real-time monitoring and processing to meet the mission-critical quality of service requirements and are expected to be supported by the emerging fog or edge computing paradigms. This paper presents NFVMon, a novel monitoring architecture to enable flow monitoring capabilities of network traffic in a 5G multioperator mobile edge computing environment. The proposed NFVMon is integrated with the management plane of the Cloud Computing. NFVMon has been prototyped and a reference implementation is presented. It provides novel capabilities to provide disaggregated metrics related to the different 5G mobile operators sharing infrastructures and also about the different 5G subscribers of each of such mobile operators. Extensive experiments for evaluating the performance of the system have been conducted on a mid-sized infrastructure testbed.

关键词： Computation offloading   Mobile edge computing   Vehicular ad-hoc networks   Vehicular cloud Computing  

摘要： Smart vehicles of today on road are equipped with advanced computational units, multiple communication technologies, intelligent sensing platforms, and human-computer interaction devices which utilize Vehicular Edge Networks to support services offered by the remote cloud. This being named as Opportunistic Vehicular Edge Computing recently, has the possibility to supplement the services provided by the Edge gadgets. Many Vehicular Edge Computing architectures have been proposed as of late which support task offloading. One among the premier difficulties in these networks is efficiently utilizing the resources available at the vehicular nodes. The present work uses APEATOVC, a conveyed and versatile protocol for economical, efficient and effective task offloading in these networks which address the adaptability of vehicular clouds. The results obtained by extensive simulations are presented to assess and contrast its performance with existing protocols. © 2018, International Journal of Computer Networks & Communications (IJCNC).

关键词： Mobile edge computing   resource allocation   energy efficient   ADMM  

摘要： Mobile edge computing (MEC) is a new promising technique to provide cloud-computing capabilities at the edge of cellular networks in close proximity to mobile users. In this paper, we consider joint optimization of the communication and computation resources in a multiuser, multiserver MEC system. The objective of this optimization problem is to minimize the total energy consumption of mobile devices under the time-sharing constraint. Given the fact that no coordination is involved between mobile devices, we propose a light-weight and decentralized algorithm based on the alternating direction method of multipliers (ADMM) framework. Experimental results demonstrate that the proposed algorithm performs well in terms of convergence and outperforms the conventional centralized approach.