10 research outputs found
Lightweight Simulation of Hybrid Aerial- and Ground-based Vehicular Communication Networks
Cooperating small-scale Unmanned Aerial Vehicles (UAVs) will open up new
application fields within next-generation Intelligent Transportation Sytems
(ITSs), e.g., airborne near field delivery. In order to allow the exploitation
of the potentials of hybrid vehicular scenarios, reliable and efficient
bidirectional communication has to be guaranteed in highly dynamic
environments. For addressing these novel challenges, we present a lightweight
framework for integrated simulation of aerial and ground-based vehicular
networks. Mobility and communication are natively brought together using a
shared codebase coupling approach, which catalyzes the development of novel
context-aware optimization methods that exploit interdependencies between both
domains. In a proof-of-concept evaluation, we analyze the exploitation of UAVs
as local aerial sensors as well as aerial base stations. In addition, we
compare the performance of Long Term Evolution (LTE) and Cellular
Vehicle-to-Everything (C-V2X) for connecting the ground- and air-based
vehicles
The AutoMat CVIM - A Scalable Data Model for Automotive Big Data Marketplaces
In the past years, connectivity has been introduced in automotive production
series, enabling vehicles as highly mobile Internet of Things sensors and
participants. The Horizon 2020 research project AutoMat addressed this scenario
by building a vehicle big data marketplace in order to leverage and exploit
crowd-sourced sensor data, a so far unexcavated treasure. As part of this
project the Common Vehicle Information Model (CVIM) as harmonized data model
has been developed. The CVIM allows a common understanding and generic
representation, brand-independent throughout the whole data value and
processing chain. The demonstrator consists of CVIM vehicle sensor data, which
runs through the different components of the whole AutoMat vehicle big data
processing pipeline. Finally, at the example of a traffic measurement service
the data of a whole vehicle fleet is aggregated and evaluated
An Efficient Distributed Task Offloading Scheme for Vehicular Edge Computing Networks
With the recent advancement of vehicular ad-hoc networks (VANETs) or the internet of vehicles (IoVs), vehicles are getting more powerful and generating huge amount of traffic data, including computation-intensive and delay-sensitive applications in the vehicular edge computing (VEC) networks, which are difficult to be processed by an individual vehicular node. These resource-demanding tasks can be transferred to another vehicular node with idle computing resources for processing. Due to high mobility and limited resources of vehicular nodes, it is challenging to execute lengthy computation-intensive tasks until completion within the delay constraint. There is a need to provide an efficient task offloading strategies to support these applications. In this paper, an efficient distributed task offloading scheme is proposed to select nearby vehicles with idle computing resources, to process the tasks in parallel by considering some vital metrics, including link reliability, distance, available computing resources, and relative velocity. In order to complete the lengthy computation-intensive tasks in vehicular edge computing networks, a task is divided into several subtasks before offloading. The performance of the proposed scheme is evaluated in several VEC network conditions. Results show that the proposed computation task offloading scheme achieves better performance in latency, throughput, resource utilization and packet delivery ratio than the existing schemes
Simu5G – An OMNeT++ library for end-to-end performance evaluation of 5G networks
In this paper we introduce Simu5G, a new OMNeT++-based model library to simulate 5G networks. Si-mu5G allows users to simulate the data plane of 5G New Radio deployments, in an end-to-end perspective and including all protocol layers, making it a valuable tool for researchers and practitioners interested in the performance evaluation of 5G networks and services. We discuss the modelling of the protocol layers, network entities and functions, and validate our abstraction of the physical layer using 3GPP-based sce-narios. Moreover, we show how Simu5G can be used to evaluate Multi-access Edge Computing (MEC) and Cellular Vehicle-to-everything (C-V2X) services offered through a 5G network
Applications
Volume 3 describes how resource-aware machine learning methods and techniques are used to successfully solve real-world problems. The book provides numerous specific application examples: in health and medicine for risk modelling, diagnosis, and treatment selection for diseases in electronics, steel production and milling for quality control during manufacturing processes in traffic, logistics for smart cities and for mobile communications
Applications
Volume 3 describes how resource-aware machine learning methods and techniques are used to successfully solve real-world problems. The book provides numerous specific application examples: in health and medicine for risk modelling, diagnosis, and treatment selection for diseases in electronics, steel production and milling for quality control during manufacturing processes in traffic, logistics for smart cities and for mobile communications