10,424 research outputs found
Self-Sustaining Caching Stations: Towards Cost-Effective 5G-Enabled Vehicular Networks
In this article, we investigate the cost-effective 5G-enabled vehicular
networks to support emerging vehicular applications, such as autonomous
driving, in-car infotainment and location-based road services. To this end,
self-sustaining caching stations (SCSs) are introduced to liberate on-road base
stations from the constraints of power lines and wired backhauls. Specifically,
the cache-enabled SCSs are powered by renewable energy and connected to core
networks through wireless backhauls, which can realize "drop-and-play"
deployment, green operation, and low-latency services. With SCSs integrated, a
5G-enabled heterogeneous vehicular networking architecture is further proposed,
where SCSs are deployed along roadside for traffic offloading while
conventional macro base stations (MBSs) provide ubiquitous coverage to
vehicles. In addition, a hierarchical network management framework is designed
to deal with high dynamics in vehicular traffic and renewable energy, where
content caching, energy management and traffic steering are jointly
investigated to optimize the service capability of SCSs with balanced power
demand and supply in different time scales. Case studies are provided to
illustrate SCS deployment and operation designs, and some open research issues
are also discussed.Comment: IEEE Communications Magazine, to appea
A software-defined architecture for next-generation cellular networks
In the recent years, mobile cellular networks are undergoing fundamental changes and many established concepts are being revisited. New emerging paradigms, such as Software-Defined Networking (SDN), Mobile Cloud Computing (MCC), Network Function Virtualization (NFV), Internet of Things (IoT),and Mobile Social Networking (MSN), bring challenges in the design of cellular networks architectures. Current Long-Term Evolution (LTE) networks are not able to accommodate these new trends in a scalable and efficient way. In this paper, first we discuss the limitations of the current LTE architecture. Second, driven by the new communication needs and by the advances in aforementioned areas, we propose a new architecture for next generation cellular networks. Some of its characteristics include support for distributed content routing, Heterogeneous Networks(HetNets) and multiple Radio Access Technologies (RATs). Finally, we present simulation results which show that significant backhaul traffic savings can be achieved by implementing caching and routing functions at the network edge
Sharing Human-Generated Observations by Integrating HMI and the Semantic Sensor Web
Current âInternet of Thingsâ concepts point to a future where connected objects gather meaningful information about their environment and share it with other objects and people. In particular, objects embedding Human Machine Interaction (HMI), such as mobile devices and, increasingly, connected vehicles, home appliances, urban interactive infrastructures, etc., may not only be conceived as sources of sensor information, but, through interaction with their users, they can also produce highly valuable context-aware human-generated observations. We believe that the great promise offered by combining and sharing all of the different sources of information available can be realized through the integration of HMI and Semantic Sensor Web technologies. This paper presents a technological framework that harmonizes two of the most influential HMI and Sensor Web initiatives: the W3Câs Multimodal Architecture and Interfaces (MMI) and the Open Geospatial Consortium (OGC) Sensor Web Enablement (SWE) with its semantic extension, respectively. Although the proposed framework is general enough to be applied in a variety of connected objects integrating HMI, a particular development is presented for a connected car scenario where driversâ observations about the traffic or their environment are shared across the Semantic Sensor Web. For implementation and evaluation purposes an on-board OSGi (Open Services Gateway Initiative) architecture was built, integrating several available HMI, Sensor Web and Semantic Web technologies. A technical performance test and a conceptual validation of the scenario with potential users are reported, with results suggesting the approach is soun
Car-to-Cloud Communication Traffic Analysis Based on the Common Vehicle Information Model
Although connectivity services have been introduced already today in many of
the most recent car models, the potential of vehicles serving as highly mobile
sensor platform in the Internet of Things (IoT) has not been sufficiently
exploited yet. The European AutoMat project has therefore defined an open
Common Vehicle Information Model (CVIM) in combination with a cross-industry,
cloud-based big data marketplace. Thereby, vehicle sensor data can be leveraged
for the design of entirely new services even beyond traffic-related
applications (such as localized weather forecasts). This paper focuses on the
prediction of the achievable data rate making use of an analytical model based
on empirical measurements. For an in-depth analysis, the CVIM has been
integrated in a vehicle traffic simulator to produce CVIM-complaint data
streams as a result of the individual behavior of each vehicle (speed, brake
activity, steering activity, etc.). In a next step, a simulation of vehicle
traffic in a realistically modeled, large-area street network has been used in
combination with a cellular Long Term Evolution (LTE) network to determine the
cumulated amount of data produced within each network cell. As a result, a new
car-to-cloud communication traffic model has been derived, which quantifies the
data rate of aggregated car-to-cloud data producible by vehicles depending on
the current traffic situations (free flow and traffic jam). The results provide
a reference for network planning and resource scheduling for car-to-cloud type
services in the context of smart cities
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