关键词： Smart Buildings   Urban Areas   Smart Cities   Communities   Multiaccess Communication   Telecommunication Services   Resource Management   Telecommunication Traffic   Internet Web Technologies   Network Function Virtualization   NFV   Software Defined Networking   SDN   Central Office Rearchitected As A Datacenter   CORD   Wireless Optical Broadband Access Network   WOBAN   Telco Cloud   Smart Community   Green ICT   IMS   Mobile Edge Computing  

摘要： Providing multitenant, multiaccess, and multiservice solutions in communities is key to sustaining innovations and economic development. This article investigates a solution for rearchitecting a telecommunications company's (telco's) central office to offer services in a smart community, enabled by virtual network function elements running on a smart edge. These elements manage a multiaccess underlying infrastructure and deploy telco services with minimal resource consumption. The virtual smart edge flexibly routes traffic, aggregates, and splits flows across a heterogeneous network to achieve network operational efficiency, and optimizes computing resource allocation for last-mile Internet of Things services hosted close to the end user.

关键词： Mobile edge computing   Mobility management   Path selection   Handover   Small cell cloud   Energy efficiency  

摘要： Convergence of mobile networks and cloud computing enables to offload heavy computation from a user equipment (UE) to the cloud. The offloading can reduce energy consumption of the UEs. Nevertheless, delivery of data to a centralized cloud leads to high latency and to overloading backhaul network. To overcome these constrains, computing capabilities can be brought closer to the user and integrated into small cell base stations deployed in mobile networks. This concept of cloud-enabled small cells is known as small cell cloud (SCC). In the SCC, the UEs benefit from proximity to the computing stations resulting in both lower latency and alleviating load of backhaul. In this paper, we propose a path selection algorithm finding the most suitable way for data delivery between the mobile UE and the cells performing computation for this particular UE. The path selection algorithm estimates transmission delay and energy consumed by the transmission of offloaded data and selects the most suitable base station for radio communication accordingly. The path selection problem is formulated as Markov Decision Process (MDP). The algorithm is suitable for parallel computation in dynamic scenarios with mobile users and handles mobility for users exploiting computing services in the SCC. Comparing to conventional approach for delivery of data to computing cells, the proposed algorithm reduces the delay up to 54.3% and UE's energy consumption is decreased by up to 7.5%. Moreover, users' satisfaction with data transmission delay is increased by up to 28% and load of small cell's backhaul is lowered by up to 29%. (C) 2016 Elsevier B.V. All rights reserved.

关键词： Cloud computing   Mobile edge computing   Health monitoring   Map reduce   Decision systems   Reactive   Proactive  

摘要： Rapid development of wearable devices and mobile cloud computing technologies has led to new opportunities for large scale e-healthcare systems. In these systems, individuals' health information are remotely detected using wearable sensors and forwarded through wireless devices to a dedicated computing system for processing and evaluation where a set of specialists namely, hospitals, healthcare agencies and physicians will take care of such health information. Real-time or semi-real time health information are used for online monitoring of patients at home. This in fact enables the doctors and specialists to provide immediate medical treatments. Large scale e-healthcare systems aim at extending the monitoring coverage from individuals to include a crowd of people who live in communities, cities, or even up to a whole country. In this paper, we propose a large scale e-healthcare monitoring system that targets a crowd of individuals in a wide geographical area. The system is efficiently integrating many emerging technologies such as mobile computing, edge computing, wearable sensors, cloud computing, big data techniques, and decision support systems. It can offer remote monitoring of patients anytime and anywhere in a timely manner. The system also features some unique functions that are of great importance for patients' health as well as for societies, cities, and countries. These unique features are characterized by taking long-term, proactive, and intelligent decisions for expected risks that might arise by detecting abnormal health patterns shown after analyzing huge amounts of patients' data. Furthermore, it is using a set of supportive information to enhance the decision support system outcome. A rigorous set of evaluation experiments are conducted and presented to validate the efficiency of the proposed model. The obtained results show that the proposed model is scalable by handling a large number of monitored individuals with minimal overhead. Moreover, exploi

关键词： Centralised mobile networks   Light data Centre   Small cells   Mobile edge computing   5G  

摘要： Current trends in broadband mobile networks are addressed towards the placement of different capabilities at the edge of the mobile network in a centralised way. On one hand, the split of the eNB between baseband processing units and remote radio headers makes it possible to process some of the protocols in centralised premises, likely with virtualised resources. On the other hand, mobile edge computing makes use of processing and storage capabilities close to the air interface in order to deploy optimised services with minimum delay. The confluence of both trends is a hot topic in the definition of future 5G networks. The full centralisation of both technologies in cloud data centres imposes stringent requirements to the fronthaul connections in terms of throughput and latency. Therefore, all those cells with limited network access would not be able to offer these types of services. This paper proposes a solution for these cases, based on the placement of processing and storage capabilities close to the remote units, which is especially well suited for the deployment of clusters of small cells. The proposed cloud-enabled small cells include a highly efficient microserver with a limited set of virtualised resources offered to the cluster of small cells. As a result, a light data centre is created and commonly used for deploying centralised eNB and mobile edge computing functionalities. The paper covers the proposed architecture, with special focus on the integration of both aspects, and possible scenarios of application.

关键词： Compressed sampling   Internet of Things (IoT)   long-term evolution (LTE)   memory replication   mobile edge computing  

摘要： Augmenting the long-term evolution (LTE)-evolved NodeB (eNB) with cloud resources offers a low-latency, resilient, and LTE-aware environment for offloading the Internet of Things (IoT) services and applications. By means of devices memory replication, the IoT applications deployed at an LTE-integrated edge cloud can scale its computing and storage requirements to support different resource-intensive service offerings. Despite this potential, the massive number of IoT devices limits the LTE edge cloud responsiveness as the LTE radio interface becomes the major bottleneck given the unscalability of its uplink access and data transfer procedures to support a large number of devices that simultaneously replicate their memory objects with the LTE edge cloud. We propose REPLISOM;an LTE-aware edge cloud architecture and an LTE-optimized memory replication protocol which relaxes the LTE bottlenecks by a delay and radio resource-efficient memory replication protocol based on the device-to-device communication technology and the sparse recovery in the theory of compressed sampling. REPLISOM effectively schedules the memory replication occasions to resolve contentions for the radio resources as a large number of devices simultaneously transmit their memory replicas. Our analysis and numerical evaluation suggest that this system has significant potential in reducing the delay, energy consumption, and cost for cloud offloading of IoT applications given the massive number of devices with tiny memory sizes.

关键词： Mobile Cloud Computing   Mobile Edge Computing   Context Adaptation   Context Awareness  

摘要： Markets for mobile applications offer myriads of apps ranging from simple to quite demanding ones. The latter are on the rise since every new generation of smartphones is equipped with more resources (CPU, memory, bandwidth, energy) to even allow resource-demanding services like speech- or face recognition to be executed locally on a device. But compared to their stationary counterparts, mobile devices remain comparatively limited in terms of resources. Because of this, current approaches aim at extending mobile device capabilities with computation and storage resources offered by cloud services or other nearby devices. This paradigm, known as Mobile Cloud Computing (MCC), is challenged by the dynamically changing context of mobile devices, which developers are required to take into account to decide, e.g., which application parts are when to offload. To rise to such and similar challenges we introduce the concept of Generic Context Adaptation (GCA), a data mining process that facilitates the adaptation of (mobile) applications to their current and future context. Moreover, we evaluate our approach with real usage data provided by the Nokia Mobile Data Challenge (MDC) as well as with CloudAware , a context-adaptive mobile middleware for MCC that supports automated context-aware self-adaptation techniques.

关键词： Energy-efficiency   offloading   mobile edge computing   5G  

摘要： Mobile edge computing (MEC) is a promising paradigm to provide cloud-computing capabilities in close proximity to mobile devices in fifth-generation (5G) networks. In this paper, we study energy-efficient computation offloading (EECO) mechanisms for MEC in 5G heterogeneous networks. We formulate an optimization problem to minimize the energy consumption of the offloading system, where the energy cost of both task computing and file transmission are taken into consideration. Incorporating the multi-access characteristics of the 5G heterogeneous network, we then design an EECO scheme, which jointly optimizes offloading and radio resource allocation to obtain the minimal energy consumption under the latency constraints. Numerical results demonstrate energy efficiency improvement of our proposed EECO scheme.