Towards faster big data analytics for anti‐jamming applications in vehicular ad‐hoc network

[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 .