Towards faster big data analytics for anti‐jamming applications in vehicular ad‐hoc network
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Mouzhi Ge | Le Hong Trang | Hind Bangui | Barbora Buhnova | Mouzhi Ge | L. Trang | Hind Bangui | Barbora Buhnova
[1] Antonios Argyriou,et al. Jamming attack detection in a pair of RF communicating vehicles using unsupervised machine learning , 2018, Veh. Commun..
[2] K. P. Vijayakumar,et al. A novel jammer detection framework for cluster-based wireless sensor networks , 2016, EURASIP J. Wirel. Commun. Netw..
[3] Zhi Xue,et al. Estimating the number of multiple jamming attackers in Vehicular Ad Hoc Network , 2017, 2017 6th International Conference on Computer Science and Network Technology (ICCSNT).
[4] K. P. Vijayakumar,et al. Jamming detection approach based on fuzzy assisted multicriteria decision-making system for wireless sensor networks , 2019, Int. J. Commun. Syst..
[5] Rong Chai,et al. Adaptive K-Harmonic Means clustering algorithm for VANETs , 2014, 2014 14th International Symposium on Communications and Information Technologies (ISCIT).
[6] Pingzhi Fan,et al. An Unsupervised Cluster-Based VANET-Oriented Evolving Graph (CVoEG) Model and Associated Reliable Routing Scheme , 2019, IEEE Transactions on Intelligent Transportation Systems.
[7] Mohammad S. Obaidat,et al. Edge Computing-Based Security Framework for Big Data Analytics in VANETs , 2019, IEEE Network.
[8] Attahiru Sule Alfa,et al. A Statistical Approach to Detect Jamming Attacks in Wireless Sensor Networks , 2018, Sensors.
[9] James Gross,et al. Experimental Characterization and Modeling of RF Jamming Attacks on VANETs , 2015, IEEE Transactions on Vehicular Technology.
[10] Zhi Xue,et al. Localization of multiple jamming attackers in vehicular ad hoc network , 2017, Int. J. Distributed Sens. Networks.
[11] Yuhua Xu,et al. A hierarchical learning approach to anti-jamming channel selection strategies , 2019, Wirel. Networks.
[12] Dan Feldman,et al. The single pixel GPS: learning big data signals from tiny coresets , 2012, SIGSPATIAL/GIS.
[13] Alvin S. Lim,et al. Jamming and anti-jamming techniques in wireless networks: a survey , 2014, Int. J. Ad Hoc Ubiquitous Comput..
[14] Kartik Shankar,et al. Secure Data Transmission Through Reliable Vehicles in VANET Using Optimal Lightweight Cryptography , 2019 .
[15] Gregory L. Mayhew. Dynamic message prioritization in tactical wireless MANET , 2010, 2010 IEEE Aerospace Conference.
[16] Tuaha Nomani,et al. A Survey on Location Privacy Techniques Deployed in Vehicular Networks , 2019, 2019 16th International Bhurban Conference on Applied Sciences and Technology (IBCAST).
[17] Lin Yao,et al. V2X Routing in a VANET Based on the Hidden Markov Model , 2018, IEEE Transactions on Intelligent Transportation Systems.
[18] Dongmei Zhang,et al. A Streaming Algorithm for k-Means with Approximate Coreset , 2019, Asia Pac. J. Oper. Res..
[19] Mouzhi Ge,et al. Improving Big Data Clustering for Jamming Detection in Smart Mobility , 2020, SEC.
[20] S Sivaprakash,et al. A design and development of an intelligent jammer and jamming detection methodologies using machine learning approach , 2018, Cluster Computing.
[21] Fabio Massimo Zennaro. Analyzing and Storing Network Intrusion Detection Data using Bayesian Coresets: A Preliminary Study in Offline and Streaming Settings , 2019, PKDD/ECML Workshops.
[22] Klaus Wehrle,et al. Machine learning-based jamming detection for IEEE 802.11: Design and experimental evaluation , 2014, Proceeding of IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks 2014.
[23] Sujata Pandey,et al. Accessible review of internet of vehicle models for intelligent transportation and research gaps for potential future directions , 2020, Peer-to-Peer Netw. Appl..
[24] Sherali Zeadally,et al. VANET-cloud: a generic cloud computing model for vehicular Ad Hoc networks , 2015, IEEE Wireless Communications.
[25] Feten Slimeni,et al. Cooperative Q-learning based channel selection for cognitive radio networks , 2019, Wirel. Networks.
[26] Mohamed Abdel-Aty,et al. Big Data and Road Safety: A Comprehensive Review , 2019, Mobility Patterns, Big Data and Transport Analytics.
[27] Wenchao Xu,et al. Big Data Driven Vehicular Networks , 2018, IEEE Network.
[28] Zhihui Lu,et al. An efficient key distribution system for data fusion in V2X heterogeneous networks , 2019, Inf. Fusion.
[29] Senlin Luo,et al. Named Data Networking in Vehicular Ad Hoc Networks: State-of-the-Art and Challenges , 2020, IEEE Communications Surveys & Tutorials.
[30] Kamalrulnizam Abu Bakar,et al. Fog Based Intelligent Transportation Big Data Analytics in The Internet of Vehicles Environment: Motivations, Architecture, Challenges, and Critical Issues , 2018, IEEE Access.
[31] Luliang Jia,et al. A Collaborative Multi-Agent Reinforcement Learning Anti-Jamming Algorithm in Wireless Networks , 2018, IEEE Wireless Communications Letters.
[32] Christopher Leckie,et al. Deep Learning Based Game-Theoretical Approach to Evade Jamming Attacks , 2018, GameSec.
[33] Kok-Lim Alvin Yau,et al. Comprehensive Survey of Machine Learning Approaches in Cognitive Radio-Based Vehicular Ad Hoc Networks , 2020, IEEE Access.
[34] Kartik Shankar,et al. An effect of big data technology with ant colony optimization based routing in vehicular ad hoc networks: Towards smart cities , 2019, Journal of Cleaner Production.
[35] Mouzhi Ge,et al. Scaling Big Data Applications in Smart City with Coresets , 2019, DATA.
[36] Alagan Anpalagan,et al. A Game-Theoretic Learning Approach for Anti-Jamming Dynamic Spectrum Access in Dense Wireless Networks , 2019, IEEE Transactions on Vehicular Technology.
[37] Jianping Pan,et al. Joint optimization of spectrum access and power allocation in uplink OFDMA CR-VANETs , 2019, Wirel. Networks.
[38] Dan Feldman,et al. Turning Big Data Into Tiny Data: Constant-Size Coresets for k-Means, PCA, and Projective Clustering , 2020, SIAM J. Comput..
[39] Yalin E. Sagduyu,et al. Deep Learning for Launching and Mitigating Wireless Jamming Attacks , 2018, IEEE Transactions on Cognitive Communications and Networking.
[40] E. Hajrizi,et al. Use of IoT Technology to Drive the Automotive Industry from Connected to Full Autonomous Vehicles , 2016 .
[41] Abdel Lisser,et al. Jamming Attacks Reliable Prevention in a Clustered Wireless Sensor Network , 2015, Wirel. Pers. Commun..
[42] Jalel Ben-Othman,et al. DJAVAN: Detecting jamming attacks in Vehicle Ad hoc Networks , 2015, Perform. Evaluation.
[43] F. Richard Yu,et al. A novel Intrusion Detection System for Vehicular Ad Hoc Networks (VANETs) based on differences of traffic flow and position , 2019, Appl. Soft Comput..
[44] Ping Li,et al. M-cluster and X-ray: Two methods for multi-jammer localization in wireless sensor networks , 2014, Integr. Comput. Aided Eng..
[45] MengChu Zhou,et al. A Fluid Mechanics-Based Data Flow Model to Estimate VANET Capacity , 2020, IEEE Transactions on Intelligent Transportation Systems.
[46] Mouzhi Ge,et al. Multi-Criteria Decision Analysis Methods in the Mobile Cloud Offloading Paradigm , 2017, J. Sens. Actuator Networks.
[47] Shahid Mumtaz,et al. Social Big-Data-Based Content Dissemination in Internet of Vehicles , 2018, IEEE Transactions on Industrial Informatics.
[48] Haeyoung Lee,et al. Implementation of a Collision Avoidance System To Assist Safe Driving Based on Data Fusion in Vehicular Networks , 2020, 2020 International Conference on Information and Communication Technology Convergence (ICTC).
[49] Mouzhi Ge,et al. Exploring Big Data Clustering Algorithms for Internet of Things Applications , 2018, IoTBDS.
[50] Mohsen Guizani,et al. V2V Routing in a VANET Based on the Autoregressive Integrated Moving Average Model , 2019, IEEE Transactions on Vehicular Technology.
[51] Saleh Faruque,et al. Smart Jamming Attacks in 5G New Radio: A Review , 2020, 2020 10th Annual Computing and Communication Workshop and Conference (CCWC).
[52] Dan Feldman,et al. Coresets for Differentially Private K-Means Clustering and Applications to Privacy in Mobile Sensor Networks , 2017, 2017 16th ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN).
[53] Meng Wang,et al. Power Control in Relay-Assisted Anti-Jamming Systems: A Bayesian Three-Layer Stackelberg Game Approach , 2019, IEEE Access.
[54] Xiaojiang Du,et al. Intersection Fog-Based Distributed Routing for V2V Communication in Urban Vehicular Ad Hoc Networks , 2020, IEEE Transactions on Intelligent Transportation Systems.
[55] Anis Laouiti,et al. VANet security challenges and solutions: A survey , 2017, Veh. Commun..
[56] Nei Kato,et al. Future Intelligent and Secure Vehicular Network Toward 6G: Machine-Learning Approaches , 2020, Proceedings of the IEEE.
[57] Wenchao Xu,et al. Internet of vehicles in big data era , 2018, IEEE/CAA Journal of Automatica Sinica.
[58] Wei Liu,et al. A Cost-Efficient Greedy Code Dissemination Scheme Through Vehicle to Sensing Devices (V2SD) Communication in Smart City , 2019, IEEE Access.
[59] Sibaram Khara,et al. Balanced Cluster Head Selection Based on Modified k-Means in a Distributed Wireless Sensor Network , 2016, Int. J. Distributed Sens. Networks.
[60] Alagan Anpalagan,et al. Anti-Jamming Communications Using Spectrum Waterfall: A Deep Reinforcement Learning Approach , 2017, IEEE Communications Letters.
[61] Cunqing Hua,et al. Machine Learning-based RF Jamming Detection in Wireless Networks , 2018, 2018 Third International Conference on Security of Smart Cities, Industrial Control System and Communications (SSIC).
[62] Sherali Zeadally,et al. Data analytics for Cooperative Intelligent Transport Systems , 2019, Veh. Commun..
[63] Serge Guillaume,et al. ProTraS: A probabilistic traversing sampling algorithm , 2018, Expert Syst. Appl..
[64] Montserrat Ros,et al. A Comparative Survey of VANET Clustering Techniques , 2017, IEEE Communications Surveys & Tutorials.
[65] Walid Saad,et al. A Vision of 6G Wireless Systems: Applications, Trends, Technologies, and Open Research Problems , 2019, IEEE Network.
[66] Alagan Anpalagan,et al. A Hierarchical Learning Solution for Anti-Jamming Stackelberg Game With Discrete Power Strategies , 2017, IEEE Wireless Communications Letters.
[67] Sungwoo Bae,et al. Electric vehicle charging demand forecasting model based on big data technologies , 2016 .