1,891 research outputs found
Advances on Network Protocols and Algorithms for Vehicular Ad Hoc Networks
Vehicular Ad Hoc Network (VANET) is an emerging area of
wireless ad hoc networks that facilitates ubiquitous connectivity
between smart vehicles through Vehicle-to-Vehicle
(V2V) or Vehicle-to-Roadside (V2R) and Roadside-to-
Vehicle (R2V) communications. This emerging field of technology
aims to improve safety of passengers and traffic flow,
reduces pollution to the environment and enables in-vehicle
entertainment applications. The safety-related applications
could reduce accidents by providing drivers with traffic information
such as collision avoidances, traffic flow alarms and
road surface conditions. Moreover, the passengers could exploit
an available infrastructure in order to connect to the
internet for infomobility and entertainment applications.Lloret, J.; Ghafoor, KZ.; Rawat, DB.; Xia, F. (2013). Advances on Network Protocols and Algorithms for Vehicular Ad Hoc Networks. Mobile Networks and Applications. 18(6):749-754. doi:10.1007/s11036-013-0490-7S749754186Lloret J, Canovas A, Catalá A, Garcia M (2013) Group-based protocol and mobility model for VANETs to offer internet access. J Netw Comput Appl 36(3):1027–1038. doi: 10.1016/j.jnca.2012.02.009Khokhar RH, Zia T, Ghafoor KZ, Lloret J, Shiraz M (2013) Realistic and efficient radio propagation model for V2X communications. KSII Trans Internet Inform Syst 7(8):1933–1953. doi: 10.3837/tiis.2013.08.011Ghafoor KZ (2013) Routing protocols in vehicular ad hoc networks: survey and research challenges, Netw Protocol Algorithm 5(4). doi: 10.5296/npa.v5i4.4134Ghafoor KZ, Bakar KA, Lloret J, Ke C-H, Lee KC (2013) Intelligent beaconless geographical routing for urban vehicular environments. Wirel Netw 19(3):345–362. doi: 10.1007/s11276-012-0470-zGhafoor KZ, Bakar KA, Lee K, AL-Hashimi H (2010) A novel delay- and reliability- aware inter-vehicle routing protocol. Netw Protocol Algorithms 2(2):66–88. doi: 10.5296/npa.v2i2.427Dias JAFF, Rodrigues JJPC, Isento JN, Pereira PRBA, Lloret J (2011) Performance assessment of fragmentation mechanisms for vehicular delay-tolerant networks. EURASIP J Wirel Commun Netw 2011(195):1–14. doi: 10.1186/1687-1499-2011-195Zhang D, Yang Z, Raychoudhury V, Chen Z, Lloret J (2013) An energy-efficient routing protocol using movement trend in vehicular Ad-hoc networks. Comput J 58(8):938–946. doi: 10.1093/comjnl/bxt028Ghafoor KZ, Lloret J, Bakar KA, Sadiq AS, Mussa SAB (2013) Beaconing approaches in vehicular Ad Hoc networks: a survey. Wirel Pers Commun. doi: 10.1007/s11277-013-1222-9Sadiq AS, Bakar KA, Ghafoor KZ, Lloret J (2013) An intelligent vertical handover scheme for audio and video streaming in heterogeneous vehicular networks. Mobile Netw Appl. doi: 10.1007/s11036-013-0465-8Khamayseh YM (2013) Network size estimation in VANETs. Netw Protocol Algorithm 5(3):136–152. doi: 10.5296/npa.v5i6.3838Rawat DB, Popescu DC, Yan G, Olariu S (2011) Enhancing VANET performance by joint adaptation of transmission power and contention window size. IEEE Trans Parallel Distrib Syst 22(9):1528–1535Yan G, Rawat DB, Bista BB. Provisioning vehicular ad hoc networks with quality of services. Int J Space-Based Situated Comput 2(2):104–111Rawat DB, Bista BB, Yan G, Weigle MC (2011) Securing vehicular ad-hoc networks against malicious drivers: a probabilistic approach, International Conference on Complex, Intelligent, and Software Intensive Systems Pp. 146–151. June 30, 2011Sun W, Xia F, Ma J, Fu T, Sun Y. An optimal ODAM-based broadcast algorithm for vehicular Ad-Hoc Networks. KSII Trans Internet Inform Syst 6(12): 3257–3274Vinel AV, Dudin AN, Andreev SD, Xia F (2010) Performance modeling methodology of emergency dissemination algorithms for vehicular ad-hoc networks, 6th Communication Systems, Networks & Digital Signal Processing (CSNDSP 2010), Pp. 397–400AL-Hashimi HN, Bakar KA, Ghafoor KZ (2010) Inter-domain proxy mobile IPv6 based vehicular network. Netw Protocol Algorithm 2(4):1–15. doi: 10.5296/npa.v2i4.488Ghafoor KZ, Bakar KA, Mohammed MA, Lloret J (2013) Vehicular cloud computing: trends and challenges, in the book “mobile computing over cloud: technologies, services, and applications”. IGI GlobalYan G, Rawat DB, Bista BB (2012) Towards secure vehicular clouds, Sixth International Conference on Complex, Intelligent and Software Intensive Systems (CISIS 2012), Pp. 370–375Fernández H, Rubio L, Reig J, Rodrigo-Peñarrocha VM, Valero A (2013) Path loss modeling for vehicular system performance and communication protocols evaluation. Mobile Netw Appl. doi: 10.1007/s11036-013-0463-xAllouche Y, Segal M (2013) A cluster-based beaconing approach in VANETs: near optimal topology via proximity information. Mobile Netw Appl. doi: 10.1007/s11036-013-0468-5Merah AF, Samarah S, Boukerche A, Mammeri A (2013) A sequential patterns data mining approach towards vehicular route prediction in VANETs. Mobile Netw Appl. doi: 10.1007/s11036-013-0459-6Zhang D, Huang H, Zhou J, Xia F, Chen Z (2013) Detecting hot road mobility of vehicular Ad Hoc Networks. Mobile Netw Appl. doi: 10.1007/s11036-013-0467-6El Ajaltouni H, Boukerche A, Mammeri A (2013) A multichannel QoS MAC with dynamic transmit opportunity for. Mobile Netw Appl. doi: 10.1007/s11036-013-0475-6Reñé S, Esparza O, Alins J, Mata-DĂaz J, Muñoz JL (2013) VSPLIT: a cross-layer architecture for V2I TCP services over. Mobile Netw Appl. doi: 10.1007/s11036-013-0473-8Blanco B, Liberal F (2013) Amaia Aguirregoitia, application of cognitive techniques to adaptive routing for VANETs in city environments. Mobile Netw Appl. doi: 10.1007/s11036-013-0466-7Kim J, Krunz M (2013) Spectrum-aware beaconless geographical routing protocol for cognitive radio enabled vehicular networks. Mobile Netw Appl. doi: 10.1007/s11036-013-0476-5Dias JAFF, Rodrigues JJPC, Isento JNG, Niu J (2013) The impact of cooperative nodes on the performance of vehicular delay-tolerant networks. Mobile Netw Appl. doi: 10.1007/s11036-013-0464-9Sadiq AS, Bakar KA, Ghafoor KZ, Lloret J, Khokhar R (2013) An intelligent vertical handover scheme for audio and video streaming in heterogeneous vehicular networks. Mobile Netw Appl. doi: 10.1007/s11036-013-0465-8Machado S, OzĂłn J, González AJ, Ghafoor KZ (2013) Structured peer-to-peer real time video transmission over vehicular Ad Hoc networks. Mobile Netw Appl. doi: 10.1007/s11036-013-0461-zLin C, Wu G, Xia F, Yao L (2013) Enhance the attacking efficiency of the node compromise attack in vehicular Ad-hoc network using connected dominating set. Mobile Netw Appl. doi: 10.1007/s11036-013-0469-
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
VANET Applications: Hot Use Cases
Current challenges of car manufacturers are to make roads safe, to achieve
free flowing traffic with few congestions, and to reduce pollution by an
effective fuel use. To reach these goals, many improvements are performed
in-car, but more and more approaches rely on connected cars with communication
capabilities between cars, with an infrastructure, or with IoT devices.
Monitoring and coordinating vehicles allow then to compute intelligent ways of
transportation. Connected cars have introduced a new way of thinking cars - not
only as a mean for a driver to go from A to B, but as smart cars - a user
extension like the smartphone today. In this report, we introduce concepts and
specific vocabulary in order to classify current innovations or ideas on the
emerging topic of smart car. We present a graphical categorization showing this
evolution in function of the societal evolution. Different perspectives are
adopted: a vehicle-centric view, a vehicle-network view, and a user-centric
view; described by simple and complex use-cases and illustrated by a list of
emerging and current projects from the academic and industrial worlds. We
identified an empty space in innovation between the user and his car:
paradoxically even if they are both in interaction, they are separated through
different application uses. Future challenge is to interlace social concerns of
the user within an intelligent and efficient driving
Fog Computing for Detecting Vehicular Congestion, An Internet of Vehicles based Approach: A review
Vehicular congestion is directly impacting the efficiency of the transport
sector. A wireless sensor network for vehicular clients is used in Internet of
Vehicles based solutions for traffic management applications. It was found that
vehicular congestion detection by using Internet of Vehicles based connected
vehicles technology are practically feasible for congestion handling. It was
found that by using Fog Computing based principles in the vehicular wireless
sensor network, communication in the system can be improved to support larger
number of nodes without impacting performance. In this paper, connected
vehicles technology based vehicular congestion identification techniques are
studied. Computing paradigms that can be used for the vehicular network are
studied to develop a practically feasible vehicular congestion detection system
that performs accurately for a large coverage area and multiple scenarios. The
designed system is expected to detect congestion to meet traffic management
goals that are of primary importance in intelligent transportation systems
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