arXiv subjects

Benjamin Sliwa

Publications and source records attributed to Benjamin Sliwa.

47 records · Page 3Linked to original sources

arXiv2018

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.

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arXiv2018

Resource-efficient Transmission of Vehicular Sensor Data Using Context-aware Communication

Upcoming Intelligent Traffic Control Systems (ITSCs) will base their optimization processes on crowdsensing data obtained for cars that are used as mobile sensor nodes. In conclusion, public cellular networks will be confronted with massive increases in Machine-Type Communication (MTC) and will require efficient communication schemes to minimize the interference of Internet of Things (IoT) data traffic with human communication. In this demonstration, we present an Open Source framework for context-aware transmission of vehicular sensor data that exploits knowledge about the characteristics of the transmission channel for leveraging connectivity hotspots, where data transmissions can be performed with a high grade if resource efficiency. At the conference, we will present the measurement application for acquisition and live-visualization of the required network quality indicators and show how the transmission scheme performs in real-world vehicular scenarios based on measurement data obtained from field experiments.

cs.NI↗

arXiv2018

B.A.T.Mobile: Leveraging Mobility Control Knowledge for Efficient Routing in Mobile Robotic Networks

Efficient routing is one of the key challenges of wireless networking for unmanned autonomous vehicles (UAVs) due to dynamically changing channel and network topology characteristics. Various well known mobile-ad-hoc routing protocols, such as AODV, OLSR and B.A.T.M.A.N. have been proposed to allow for proactive and reactive routing decisions. In this paper, we present a novel approach which leverages application layer knowledge derived from mobility control algorithms guiding the behavior of UAVs to fulfill a dedicated task. Thereby a prediction of future trajectories of the UAVs can be integrated with the routing protocol to avoid unexpected route breaks and packet loss. The proposed extension of the B.A.T.M.A.N. routing protocol by a mobility prediction component - called B.A.T.Mobile - has shown to be very effective to realize this concept. The results of in-depth simulation studies show that the proposed protocol reaches a distinct higher availability compared to the established approaches and shows robust behavior even in challenging channel conditions.

cs.NI↗

arXiv2018

Lightweight Joint Simulation of Vehicular Mobility and Communication with LIMoSim

The provision of reliable and efficient communication is a key requirement for the deployment of autonomous cars as well as for future Intelligent Transportation Systems (ITSs) in smart cities. Novel communications technologies will have to face highly-complex and extremely dynamic network topologies in a Vehicle-to-Everything (V2X)-context and will require the consideration of mobility information into decision processes for routing, handover and resource allocation. Consequently, researches and developers require simulation tools that are capable of providing realistic representations for both components as well as means for leveraging the convergence of mobility and communication. In this paper, we present a lightweight framework for the simulation of vehicular mobility, which has a communications-oriented perspective by design and is intended to be used in combination with a network simulator. In contrast to existing approaches, it works without requiring Interprocess Communication (IPC) using an integrated approach and is therefore able to reduce the complexity of simulation setups dramatically. Since mobility and communication share the same codebase, it is able to model scenarios with a high level of interdependency between those two components. In a proof-of-concept study, we evaluate the proposed simulator in different example scenarios in an Long Term Evolution (LTE)- context using real-world map data.

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arXiv2018

Empirical evaluation of predictive channel-aware transmission for resource efficient car-to-cloud communication

Nowadays vehicles are by default equipped with communication hardware. This enables new possibilities of connected services, like vehicles serving as highly mobile sensor platforms in the Internet of Things (IoT) context. Hereby, cars need to upload and transfer their data via a mobile communication network into the cloud for further evaluation. As wireless resources are limited and shared by all users, data transfers need to be conducted efficiently. Within the scope of this work three car-to-cloud data transmission algorithms Channel-Aware Transmission (CAT), predictive CAT (pCAT) and a periodic scheme are evaluated in an empirical setup. CAT leverages channel quality measurements to start data uploads preferably when the channel quality is good. CAT's extension pCAT uses past measurements in addition to estimate future channel conditions. For the empirical evaluation, a research vehicle was equipped with a measurement platform. On test drives along a reference route vehicle sensor data was collected and subsequently uploaded to a cloud server via a Long Term Evolution (LTE) network.

cs.NI↗

arXiv2018

A Simple Scheme for Distributed Passive Load Balancing in Mobile Ad-hoc Networks

Efficient routing is one of the key challenges for next generation vehicular networks in order to provide fast and reliable communication in a smart city context. Various routing protocols have been proposed for determining optimal routing paths in highly dynamic topologies. However, it is the dilemma of those kinds of networks that good paths are used intensively, resulting in congestion and path quality degradation. In this paper, we adopt ideas from multipath routing and propose a simple decentral scheme for Mobile Ad-hoc Network (MANET) routing, which handles passive load balancing without requiring additional communication effort. It can easily be applied to existing routing protocols to achieve load balancing without changing the routing process itself. In comprehensive simulation studies, we apply the proposed load balancing technique to multiple example protocols and evaluate its effects on the network performance. The results show that all considered protocols can achieve significantly higher reliability and improved Packet Delivery Ratio (PDR) values by applying the proposed load balancing scheme.

cs.NI↗

arXiv2018

Radio-based Traffic Flow Detection and Vehicle Classification for Future Smart Cities

Intelligent Transportation Systems (ITSs) providing vehicle-related statistical data are one of the key components for future smart cities. In this context, knowledge about the current traffic flow is used for travel time reduction and proactive jam avoidance by intelligent traffic control mechanisms. In addition, the monitoring and classification of vehicles can be used in the field of smart parking systems. The required data is measured using networks with a wide range of sensors. Nevertheless, in the context of smart cities no existing solution for traffic flow detection and vehicle classification is able to guarantee high classification accuracy, low deployment and maintenance costs, low power consumption and a weather-independent operation while respecting privacy. In this paper, we propose a radiobased approach for traffic flow detection and vehicle classification using signal attenuation measurements and machine learning algorithms. The results of comprehensive measurements in the field prove its high classification success rate of about 99%.

cs.NI↗

arXiv2018

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.

cs.NI↗

arXiv2018

Payload-Size and Deadline-Aware Scheduling for Time-critical Cyber Physical Systems

Compared to former mobile networks, Long Term Evolution (LTE) offers higher transfer speeds, significantly lower latencies and a widespread availability, qualifying LTE for a wide range of different applications and services in the field of conventional Human-to-Human (H2H) as well as fast growing Vehicle-To-X (V2X) and Cyber Physical Systems (CPS) communications. As a result, a steady growth of mobile data traffic, which is reflected in an increased interaction between different traffic classes, can be observed. In order to ensure timely transmissions of time-critical data in the future, we propose the novel Payload-Size and Deadline-Aware (PayDA) scheduling approach and compare its performance regarding the compliance for deadlines with those of other common packet scheduling mechanisms. The performance analysis is done with the complex and open-source LTE simulation environment LTE-Sim. The results show that the average latency can be reduced by the factor of 20 and the mean data rates can be enhanced by a factor of about 3.5 for a high miscellaneous data traffic. In the case of a heavy homogeneous and time-critical data traffic the mean Deadline-Miss-Ratio (DMR) can be decreased by about 35%.

cs.NI↗

arXiv2017

LIMoSim: A Lightweight and Integrated Approach for Simulating Vehicular Mobility with OMNeT++

Reliable and efficient communication is one of the key requirements for the deployment of self-driving cars. Consequently, researchers and developers require efficient and precise tools for the parallel development of vehicular mobility and communication. Although current state-of-the-art approaches allow the coupled simulation of those two components, they are making use of multiple specialized simulators that are synchronized using interprocess communication, resulting in highly complex simulation setups. Furthermore, the compatibility of those simulators requires constant attention as they are developed independently. In this paper, we present a lightweight and integrated approach for simulating vehicular mobility directly in Objective Modular Network Testbed in C++ (OMNeT++) and INET without the need for external tools or Interprocess Communication (IPC). The proposed framework Lightweight ICT-centric Mobility Simulation (LIMoSim) is available as Open Source software and can easily be combined with other third-party extension frameworks for providing vehicular mobility based on well-known microscopical models. In contrast to existing approaches, the amount of necessary preprocessing steps for simulation setups is significantly reduced. The capabilities of LIMoSim are demonstrated by a proof of concept evaluation in combination with the Long Term Evolution (LTE) simulation framework SimuLTE.

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arXiv2016

An OMNeT++ based Framework for Mobility-aware Routing in Mobile Robotic Networks

In this paper, we propose a cross-layer extension for the INETMANET framework of OMNeT++, which utilizes mobility control knowledge to enhance the forwarding of routing messages. The well-known mobility meta-model from Reynolds is used to provide a realistic representation of the mobility behavior of autonomous agents with respect to the various influences those agents have to face in real-world applications. Knowledge about the current mobility properties and its predicted development is used by mobility-aware routing mechanisms in order to optimize routing decisions and avoid path losses at runtime. In a proof of concept evaluation, we show that our proposed methods can achieve significant improvements to the robustness of communication paths.

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