Connected Vehicular Transportation: Data Analytics and Traffic-Dependent Networking

Chen, Cailian; Luan, Tom H.; Guan, Xinping; Liu, Ning; Liu, Yunshu

doi:10.1109/mvt.2016.2645318

Cited by 40 publications

(14 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For mobile wireless networks, the dynamics of the communication channel are highly affected by the mobility char- acteristics of the moving vehicle [33]. Therefore, mobilityawareness allows the explicit consideration of these impact factors e.g., for handover optimization [34] and improved routing in vehicular ad-hoc networks [35].…”

Section: Related Workmentioning

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: Related Workmentioning

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

“…Another approach [5] proposes an extension of the well-established Intelligent Driver Model (IDM) to consider cooperative behaviors for lane-merging. While forecast and optimization of vehicular traffic flow has traditionally been isolatedly addressed by the traffic physics community, the arising challenges of the hybrid traffic scenario require the joint consideration of communication systems and data analysis methods [6]. In this paper, we present an overall systemof-systems model that brings together the three mentioned disciplines for modeling and analyzing hybrid vehicular traffic with the goal of data-driven traffic flow optimization.…”

Section: Introduction and Related Workmentioning

confidence: 99%

System-of-Systems Modeling, Analysis and Optimization of Hybrid Vehicular Traffic

Sliwa¹,

Liebig

Vranken

et al. 2019

2019 IEEE International Systems Conference (SysCon)

View full text Add to dashboard Cite

While the development of fully autonomous vehicles is one of the major research fields in the Intelligent Transportation Systems (ITSs) domain, the upcoming longterm transition period -the hybrid vehicular traffic -is often neglected. However, within the next decades, automotive systems with heterogeneous autonomy levels will share the same road network, resulting in new problems for traffic management systems and communication network infrastructure providers. In this paper, we identify key challenges of the upcoming hybrid traffic scenario and present a system-of-systems model, which brings together approaches and methods from traffic modeling, data science, and communication engineering in order to allow data-driven traffic flow optimization. The proposed model consists of data acquisition, data transfer, data analysis, and data exploitation and exploits real world sensor data as well as simulative optimization methods. Based on the results of multiple case studies, which focus on individual challenges (e.g., resource-efficient data transfer and dynamic routing of vehicles), we point out approaches for using the existing infrastructure with a higher grade of efficiency.

show abstract

“…Basically vehicles are equipped with sensor nodes with dedicated short-range communication (DSRC) protocol [2][3][4]. Sensed messages are communicated through wireless access (WAVE) IEEE 802.11p standard specially developed for vehicles to adapt adhoc network, messages include real-time road condition, traffic congestion, weather condition and audio/video streams [5][6][7][8] message gives driver a better understanding of current traffic events for safe driving. Road side unit (RSU) additionally provides value added services like, nearby fuel station, restaurants etc, which significantly enhances V2RSU communications.…”

Section: Introductionmentioning

confidence: 99%

MAR_Worm: Secure and Efficient Wormhole Detection Scheme through Trusted Neighbour Nodes in VANETs

2019

IJITEE

View full text Add to dashboard Cite

VANET is an application and subclass of MANET’s, in which nodes are mobiles and considered as moving, communicating vehicles in a wireless adhoc network. Vehicles communicate through dedicated short rage communication (DSRC) via IEEE 802.11p protocol. With the progress of wireless technology, vehicular ad hoc network has become emerging technology to support real-time traffic condition, safety, entertainment, enhance driver experience and emergency navigation in intelligent transport system (ITS). Core of VANETs application is the communication between vehicle to vehicle (V2V), vehicle to roadside unit (V2RSU) and securing the data messages from malicious activities and attackers in the network. Securing V2V and V2RSU communication has raised challenging issues in detecting and avoiding malicious attackers for secure communications. VANET’s are exposed to different threats while routing data, wormhole attack is the most threatening routing attack which severely effects VANET routing data and causes incorrect routing by private tunnels and damages to VANET’s communication in terms of data leakage, data dropping, and delayed delivery. However existing attack detection schemes have failed to meet secured VANETs communication leading to packet loss. In this paper we propose an efficient wormhole detection mechanism by creating potential and trusted neighbour nodes discovery (TNND) in VANETs, which can detect malicious nodes through enabling common forwarding neighbour nodes as witness to monitor data packets are forwarded by malicious nodes. Basically this mechanism is based on trust management. This scheme is resilient and resistant against attackers launching malicious nodes to corrupt entire network. Simulation is carried on event driven network simulator and results shows efficient detection of wormhole nodes, increases packet delivery and performs better than existing detection scheme.

show abstract

Connected Vehicular Transportation: Data Analytics and Traffic-Dependent Networking

Cited by 40 publications

References 12 publications

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

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

System-of-Systems Modeling, Analysis and Optimization of Hybrid Vehicular Traffic

MAR_Worm: Secure and Efficient Wormhole Detection Scheme through Trusted Neighbour Nodes in VANETs

Contact Info

Product

Resources

About