2,561 research outputs found
Supporting Beacon and Event-Driven Messages in Vehicular Platoons through Token-Based Strategies
[EN] Timely and reliable inter-vehicle communications is a critical requirement to support traffic safety applications, such as vehicle platooning. Furthermore, low-delay communications allow the platoon to react quickly to unexpected events. In this scope, having a predictable and highly effective medium access control (MAC) method is of utmost importance. However, the currently available IEEE 802.11p technology is unable to adequately address these challenges. In this paper, we propose a MAC method especially adapted to platoons, able to transmit beacons within the required time constraints, but with a higher reliability level than IEEE 802.11p, while concurrently enabling efficient dissemination of event-driven messages. The protocol circulates the token within the platoon not in a round-robin fashion, but based on beacon data age, i.e., the time that has passed since the previous collection of status information, thereby automatically offering repeated beacon transmission opportunities for increased reliability. In addition, we propose three different methods for supporting event-driven messages co-existing with beacons. Analysis and simulation results in single and multi-hop scenarios showed that, by providing non-competitive channel access and frequent retransmission opportunities, our protocol can offer beacon delivery within one beacon generation interval while fulfilling the requirements on low-delay dissemination of event-driven messages for traffic safety applications.This work was partially supported by the Knowledge Foundation (KKS) via the ELECTRA project, the SafeCOP project, which is funded from the ECSEL Joint Undertaking under grant agreement n0 692529, and from national funding.Balador, A.; Uhlemann, E.; Tavares De Araujo Cesariny Calafate, CM.; Cano, J. (2018). Supporting Beacon and Event-Driven Messages in Vehicular Platoons through Token-Based Strategies. Sensors. 18(4):1-17. https://doi.org/10.3390/s18040955S117184Omar, H. A., Zhuang, W., & Li, L. (2013). VeMAC: A TDMA-Based MAC Protocol for Reliable Broadcast in VANETs. IEEE Transactions on Mobile Computing, 12(9), 1724-1736. doi:10.1109/tmc.2012.142Bergenhem, C., Hedin, E., & Skarin, D. (2012). Vehicle-to-Vehicle Communication for a Platooning System. Procedia - Social and Behavioral Sciences, 48, 1222-1233. doi:10.1016/j.sbspro.2012.06.1098Hadded, M., Muhlethaler, P., Laouiti, A., Zagrouba, R., & Saidane, L. A. (2015). TDMA-Based MAC Protocols for Vehicular Ad Hoc Networks: A Survey, Qualitative Analysis, and Open Research Issues. IEEE Communications Surveys & Tutorials, 17(4), 2461-2492. doi:10.1109/comst.2015.2440374Fernandes, P., & Nunes, U. (2012). Platooning With IVC-Enabled Autonomous Vehicles: Strategies to Mitigate Communication Delays, Improve Safety and Traffic Flow. IEEE Transactions on Intelligent Transportation Systems, 13(1), 91-106. doi:10.1109/tits.2011.2179936Hassanabadi, B., & Valaee, S. (2014). Reliable Periodic Safety Message Broadcasting in VANETs Using Network Coding. IEEE Transactions on Wireless Communications, 13(3), 1284-1297. doi:10.1109/twc.2014.010214.122008OMNeT++http://www.omnetpp.orgSommer, C., German, R., & Dressler, F. (2011). Bidirectionally Coupled Network and Road Traffic Simulation for Improved IVC Analysis. IEEE Transactions on Mobile Computing, 10(1), 3-15. doi:10.1109/tmc.2010.133Akhtar, N., Ergen, S. C., & Ozkasap, O. (2015). Vehicle Mobility and Communication Channel Models for Realistic and Efficient Highway VANET Simulation. IEEE Transactions on Vehicular Technology, 64(1), 248-262. doi:10.1109/tvt.2014.231910
CLEVER: a cooperative and cross-layer approach to video streaming in HetNets
We investigate the problem of providing a video streaming service to mobile users in an heterogeneous cellular network composed of micro e-NodeBs (eNBs) and macro e-NodeBs (MeNBs). More in detail, we target a cross-layer dynamic allocation of the bandwidth resources available over a set of eNBs and one MeNB, with the goal of reducing the delay per chunk experienced by users. After optimally formulating the problem of minimizing the chunk delay, we detail the Cross LayEr Video stReaming (CLEVER) algorithm, to practically tackle it. CLEVER makes allocation decisions on the basis of information retrieved from the application layer aswell as from lower layers. Results, obtained over two representative case studies, show that CLEVER is able to limit the chunk delay, while also reducing the amount of bandwidth reserved for offloaded users on the MeNB, as well as the number of offloaded users. In addition, we show that CLEVER performs clearly better than two selected reference algorithms, while being very close to a best bound. Finally, we show that our solution is able to achieve high fairness indexes and good levels of Quality of Experience (QoE)
Quality of Information in Mobile Crowdsensing: Survey and Research Challenges
Smartphones have become the most pervasive devices in people's lives, and are
clearly transforming the way we live and perceive technology. Today's
smartphones benefit from almost ubiquitous Internet connectivity and come
equipped with a plethora of inexpensive yet powerful embedded sensors, such as
accelerometer, gyroscope, microphone, and camera. This unique combination has
enabled revolutionary applications based on the mobile crowdsensing paradigm,
such as real-time road traffic monitoring, air and noise pollution, crime
control, and wildlife monitoring, just to name a few. Differently from prior
sensing paradigms, humans are now the primary actors of the sensing process,
since they become fundamental in retrieving reliable and up-to-date information
about the event being monitored. As humans may behave unreliably or
maliciously, assessing and guaranteeing Quality of Information (QoI) becomes
more important than ever. In this paper, we provide a new framework for
defining and enforcing the QoI in mobile crowdsensing, and analyze in depth the
current state-of-the-art on the topic. We also outline novel research
challenges, along with possible directions of future work.Comment: To appear in ACM Transactions on Sensor Networks (TOSN
Heterogeneous V2V Communications in Multi-Link and Multi-RAT Vehicular Networks
Connected and automated vehicles will enable advanced traffic safety and
efficiency applications thanks to the dynamic exchange of information between
vehicles, and between vehicles and infrastructure nodes. Connected vehicles can
utilize IEEE 802.11p for vehicle-to-vehicle (V2V) and vehicle-to-infrastructure
(V2I) communications. However, a widespread deployment of connected vehicles
and the introduction of connected automated driving applications will notably
increase the bandwidth and scalability requirements of vehicular networks. This
paper proposes to address these challenges through the adoption of
heterogeneous V2V communications in multi-link and multi-RAT vehicular
networks. In particular, the paper proposes the first distributed (and
decentralized) context-aware heterogeneous V2V communications algorithm that is
technology and application agnostic, and that allows each vehicle to
autonomously and dynamically select its communications technology taking into
account its application requirements and the communication context conditions.
This study demonstrates the potential of heterogeneous V2V communications, and
the capability of the proposed algorithm to satisfy the vehicles' application
requirements while approaching the estimated upper bound network capacity
Synergizing Roadway Infrastructure Investment with Digital Infrastructure for Infrastructure-Based Connected Vehicle Applications: Review of Current Status and Future Directions
The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.The safety, mobility, environmental and economic benefits of Connected and Autonomous Vehicles (CAVs) are potentially dramatic. However, realization of these benefits largely hinges on the timely upgrading of the existing transportation system. CAVs must be enabled to send and receive data to and from other vehicles and drivers (V2V communication) and to and from infrastructure (V2I communication). Further, infrastructure and the transportation agencies that manage it must be able to collect, process, distribute and archive these data quickly, reliably, and securely. This paper focuses on current digital roadway infrastructure initiatives and highlights the importance of including digital infrastructure investment alongside more traditional infrastructure investment to keep up with the auto industry's push towards this real time communication and data processing capability. Agencies responsible for transportation infrastructure construction and management must collaborate, establishing national and international platforms to guide the planning, deployment and management of digital infrastructure in their jurisdictions. This will help create standardized interoperable national and international systems so that CAV technology is not deployed in a haphazard and uncoordinated manner
Image tag completion by local learning
The problem of tag completion is to learn the missing tags of an image. In
this paper, we propose to learn a tag scoring vector for each image by local
linear learning. A local linear function is used in the neighborhood of each
image to predict the tag scoring vectors of its neighboring images. We
construct a unified objective function for the learning of both tag scoring
vectors and local linear function parame- ters. In the objective, we impose the
learned tag scoring vectors to be consistent with the known associations to the
tags of each image, and also minimize the prediction error of each local linear
function, while reducing the complexity of each local function. The objective
function is optimized by an alternate optimization strategy and gradient
descent methods in an iterative algorithm. We compare the proposed algorithm
against different state-of-the-art tag completion methods, and the results show
its advantages
Localisation of mobile nodes in wireless networks with correlated in time measurement noise.
Wireless sensor networks are an inherent part of decision making, object tracking and location awareness systems. This work is focused on simultaneous localisation of mobile nodes based on received signal strength indicators (RSSIs) with correlated in time measurement noises. Two approaches to deal with the correlated measurement noises are proposed in the framework of auxiliary particle filtering: with a noise augmented state vector and the second approach implements noise decorrelation. The performance of the two proposed multi model auxiliary particle filters (MM AUX-PFs) is validated over simulated and real RSSIs and high localisation accuracy is demonstrated
DxNAT - Deep Neural Networks for Explaining Non-Recurring Traffic Congestion
Non-recurring traffic congestion is caused by temporary disruptions, such as
accidents, sports games, adverse weather, etc. We use data related to real-time
traffic speed, jam factors (a traffic congestion indicator), and events
collected over a year from Nashville, TN to train a multi-layered deep neural
network. The traffic dataset contains over 900 million data records. The
network is thereafter used to classify the real-time data and identify
anomalous operations. Compared with traditional approaches of using statistical
or machine learning techniques, our model reaches an accuracy of 98.73 percent
when identifying traffic congestion caused by football games. Our approach
first encodes the traffic across a region as a scaled image. After that the
image data from different timestamps is fused with event- and time-related
data. Then a crossover operator is used as a data augmentation method to
generate training datasets with more balanced classes. Finally, we use the
receiver operating characteristic (ROC) analysis to tune the sensitivity of the
classifier. We present the analysis of the training time and the inference time
separately
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