Efficient Floating Car Data Transmission via LTE for Travel Time Estimation of Vehicles

Proceedings of the 51st Annual Design Automation Conference

Ide

Dusza

2014

Self Cite

Due to continuously growing communication traffic of emerging mobile phone applications, resource-efficient communication is a key to affordable network services with high Quality of Experience. While some communication traffic requires immediate resource allocations (such as voice services), an increasing number of mobile phone applications produce a lot of background communication traffic (e.g. social network apps, crowd sensing services). In this paper we discuss the predictive Channel-Aware Transmission (pCAT) scheme, which leverages the fact, that favorable channel conditions require much less spectrum resource allocation than bad channel conditions. By leveraging the knowledge of user trajectories and recurring spots with favorable channel conditions, the so-called LTE connectivity hot spots, background traffic transmissions can be scheduled by the client according to expected channel quality and application data priority. Thereby, the spectrum consumption of background traffic of applications can be reduced significantly. At the same time, the efficient usage of spectrum resources has also an impact on the battery lifetime of the mobile devices. By introducing the Context-Aware Power Consumption Model (CoPoMo) for LTE communications, we highlight, how decisions about the spectrum resource allocation by the network will impact the battery lifetime. One case study will show, that by simply changing the resource allocation scheme and without the need for spending more spectrum resources, the power consumption can be reduced by more than 70% using the Energy-Efficient Scheduling (EES) introduced in this paper.

Section: Introductionmentioning

confidence: 99%

Resource Efficient Mobile Communications for Crowd-Sensing

Proceedings of the 51st Annual Design Automation Conference

Ide

Dusza

2014

Self Cite

“…These values are used to evaluate the impact of different penetration rates (e.g., 100 cars for 5%) on the load of the LTE cell. Studies of the necessary penetration rate (several percent) required to estimate travel times are shown in [2] and [31].…”

Section: B Scenario Descriptionmentioning

confidence: 99%

“…The monitored sensor data could be velocity, braking force, steering wheel position, or light. All these data are important to reliably estimate the current traffic situation (e.g., average velocity and traveling time [2]) or to predict future traffic situations (e.g., a bottleneck due to high traffic density and bad weather conditions).…”

Section: Introduction and Problem Statementmentioning

confidence: 99%

Client-Based Control of the Interdependence Between LTE MTC and Human Data Traffic in Vehicular Environments

Ide

Dusza

IEEE Trans. Veh. Technol.

2015

Self Cite

The interdependence between machine-type communication (MTC) and human-to-human (H2H) communication has become a major topic for the development of cellular communication systems. One example of MTC application is dynamic traffic forecast, which uses sensors that are mounted on cars as an information source (so-called extended floating car data). To reduce the impact of MTC traffic on the quality of service (QoS) of human users, this paper presents a client-controlled channel-aware transmission (CAT) strategy. A new Markovian model of the Long Term Evolution (LTE) radio resources assuming heterogeneous MTC and H2H traffic is used to evaluate the performance of this approach. The close-to-reality parameterization of the model is achieved by laboratory LTE data rate measurement campaigns and ray-tracing analyses. The model demonstrates that the CAT scheme decreases the LTE cell utilization and improves the QoS in terms of blocking probability of H2H communication. The results regarding CAT are validated in an independent simulation and by LTE field measurements. Beyond this, the influence of different MTC traffic models, including best-and worst-case investigations, is provided.

“…In order not to collect all devices from the vehicles at night, the MSP must have a data connection to send the fleet movement data to a back-end system for storage and evaluation. Another application scenario for data collection of vehicle movements are traffic prognosis as they will be analyzed by the Collaborative Research Center SFB 876 within the project B4 "Analysis and Communication for the Dynamic Traffic Prognosis" [4]. For such a traffic prognosis many other parameters beside the positioning data will increase the accuracy of the prediction, e.g.…”

Section: Related Workmentioning

confidence: 99%

Metropol-E: A System for Analyzing and Optimizing Urban Electric Vehicle Fleets

Lewandowski

Groning

2012 International Conference on Connected Vehicles and Expo (ICCVE)

2012

Self Cite

Electric mobility becomes very important due to a lack of fossil fuels in the future. Hence, it is obvious to replace conventional vehicles with gasoline or diesel engines with Electric Vehicles (EVs). An important area of such replacements are vehicle fleets of companies or urban administrations. The goal of the research project metropol-E is to develop a concept for integrating Electric Mobility and renewable energy resources in the urban fleet of the City of Dortmund. The developed concept can be easily transferred to other municipalities. This work shortly introduces the metropol-E concept and concentrates on an Information System for Mobile Electric Mobility Services. On the one hand, this Information System is able to analyze the fleet movement by installing smartphones in the vehicles. On the other hand it connects the EV with the rest of the infrastructure services. With the help of the collected fleet movement data, the fleet and the charging infrastructure can be optimized. Exemplary results and mechanisms for such optimizations are also discussed in this work.