Computational Algorithms Implemented in Marine Navigation Electronic Systems

Weintrit, Adam; Kopacz, Piotr

doi:10.1007/978-3-642-34050-5_18

Cited by 11 publications

(6 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…1) Mobility Prediction: In the following, multiple prediction approaches, that exploit different sensors and information types and differ in the implementation complexity are discussed. Since Global Positioning System (GPS) coordinates and the World Geodetic System 1984 (WGS84) reference frame are used in the live-system, all calculations have to be performed in the orthodromic domain [39]. Nevertheless, the formulas are presented in a cartesian coordinate system for better understanding here.…”

Section: B Context-predictive Data Transmission Exploiting Multilayer...mentioning

confidence: 99%

Boosting Vehicle-to-Cloud Communication by Machine Learning-Enabled Context Prediction

Sliwa

Falkenberg

Liebig

et al. 2020

IEEE Trans. Intell. Transport. Syst.

View full text Add to dashboard Cite

The exploitation of vehicles as mobile sensors acts as a catalyst for novel crowdsensing-based applications such as intelligent traffic control and distributed weather forecast. However, the massive increases in Machine-type Communication (MTC) highly stress the capacities of the network infrastructure.With the system-immanent limitation of resources in cellular networks and the resource competition between human cell users and MTC, more resource-efficient channel access methods are required in order to improve the coexistence of the different communicating entities. In this paper, we present a machine learningenabled transmission scheme for client-side opportunistic data transmission. By considering the measured channel state as well as the predicted future channel behavior, delay-tolerant MTC is performed with respect to the anticipated resource-efficiency. The proposed mechanism is evaluated in comprehensive field evaluations in public Long Term Evolution (LTE) networks, where it is able to increase the mean data rate by 194% while simultaneously reducing the average power consumption by up to 54%. Index Terms-Context-predictive Communication, Machine Learning, Crowdsensing, Intelligent Transportation Systems, Mobile Sensors {Nico.Piatkowski, Thomas.Liebig}@tu-dortmund.dedata packets. However, this technology is not able to provide internet-based vehicle-to-cloud connectivity, as there are practically no deployments of Roadside Units (RSUs), which offer the required gateway functionalities. Therefore, delay-tolerant and data-intense messaging is intended to be carried out based on existing cellular communication technologies (e.g., LTE and upcoming 5G networks), which already offer large-scale coverage. With the expected massive increase in vehicular MTC [7] and the general growth of cellular data traffic [8], the network infrastructure is facing the challenge of resourcecompetition between human cell users and Internet of Things (IoT)-related data transmissions [9]. Fig. 1 gives an overview about the requirements of different vehicular and IoT-based communication systems and the resulting challenges that arise from the channel dynamics and the limited cell resources.A promising approach to address these issues is the application of context-aware communication [10] that exploits the dynamics of the communication channel to schedule delaytolerant transmissions in an opportunistic way for increasing the transmission efficiency with regard to data rate, packet loss probability and energy consumption. As a consequence, communication resources are occupied for shorter time intervals and can early be used by other cell users, which enables a better coexistence and overall system performance [11].In this paper, we extend and bring together the methods, results and insights of previous work [12], [13], [14], [15], [16] on context-aware car-to-cloud communication and propose a client-side opportunistic transmission scheme that applies Energy Efficiency Application Requirements Latency Vehicle-as-a-sensor

show abstract

Section: B Context-predictive Data Transmission Exploiting Multilayer...mentioning

confidence: 99%

Boosting Vehicle-to-Cloud Communication by Machine Learning-Enabled Context Prediction

Sliwa

Falkenberg

Liebig

et al. 2020

IEEE Trans. Intell. Transport. Syst.

View full text Add to dashboard Cite

show abstract

“…For simplicity, the following calculations are presented in a cartesian coordinate system. For using raw Global Positioning System (GPS) coordinates, all calculations have to be performed in the orthodromic dimension [13].…”

Section: A Mobility Predictionmentioning

confidence: 99%

Machine Learning Based Context-Predictive Car-to-Cloud Communication Using Multi-Layer Connectivity Maps for Upcoming 5G Networks

Sliwa

Falkenberg

Liebig

et al. 2018

2018 IEEE 88th Vehicular Technology Conference (VTC-Fall)

View full text Add to dashboard Cite

While cars were only considered as means of personal transportation for a long time, they are currently transcending to mobile sensor nodes that gather highly upto-date information for crowdsensing-enabled big data services in a smart city context. Consequently, upcoming 5G communication networks will be confronted with massive increases in Machine-type Communication (MTC) and require resourceefficient transmission methods in order to optimize the overall system performance and provide interference-free coexistence with human data traffic that is using the same public cellular network. In this paper, we bring together mobility prediction and machine learning based channel quality estimation in order to improve the resource-efficiency of car-to-cloud data transfer by scheduling the transmission time of the sensor data with respect to the anticipated behavior of the communication context. In a comprehensive field evaluation campaign, we evaluate the proposed context-predictive approach in a public cellular network scenario where it is able to increase the average data rate by up to 194% while simultaneously reducing the mean uplink power consumption by up to 54%.

show abstract

“…Despite the fact that contemporary computers are fast enough to handle more complete geodetic formulas of sub meter accuracy, a basic principle for the design of navigational systems is the avoidance of unnecessary consumption of computing power. Saving and reserving computer resources is always beneficial for the improvement of the systems effectiveness on the evolving new navigational functions and applications such as the handling of greater amounts of cartographic and navigational information, the capability for data presentation [Weintrit & Kopacz, 2012].…”

Section: Multi-system Shipborne Radionavigation Receivers Dealing Witmentioning

confidence: 99%

The Identification of Possible Applications of the E-Loran System

Czaplewski

Weintrit

2018

Annual of Navigation

Self Cite

View full text Add to dashboard Cite

Global Navigation Satellite Systems (GNSS) more and more affect many areas of human activity in the world. Every day human activity around the world depends upon satellite systems for positioning, navigation and timing. As these systems are commonly used further efforts must be made to make GNSS more immune to on occurring more and more frequently incidents of jamming and spoofing. It seems that eLoran system is currently the best the technical and scientific solution to allowing for effective protection of the Global Navigation Satellite Systems. Last year the authors presented eLoran system as a potential tool for transmission of the national time signal [Curry et al., 2017]. This time the authors present abilities of additional use of the eLoran as a back-up system for GNSS.

show abstract

Computational Algorithms Implemented in Marine Navigation Electronic Systems

Cited by 11 publications

References 6 publications

Boosting Vehicle-to-Cloud Communication by Machine Learning-Enabled Context Prediction

Boosting Vehicle-to-Cloud Communication by Machine Learning-Enabled Context Prediction

Machine Learning Based Context-Predictive Car-to-Cloud Communication Using Multi-Layer Connectivity Maps for Upcoming 5G Networks

The Identification of Possible Applications of the E-Loran System

Contact Info

Product

Resources

About