A Fuzzy-Rule Based Data Delivery Scheme in VANETs with Intelligent Speed Prediction and Relay Selection

Data delivery in vehicular networks (VANETs) is a challenging task due to the high mobility and constant topological changes. In common routing protocols, multihop V2V communications suffer from higher network delay and lower packet delivery ratio (PDR), and excessive dependence on GPS may pose threat on individual privacy. In this paper, we propose a novel data delivery scheme for vehicular networks in urban environments, which can improve the routing performance without relying on GPS. A fuzzy-rule-based wireless transmission approach is designed to optimize the relay selection considering multiple factors comprehensively, including vehicle speed, driving direction, hop count, and connection time. Wireless V2V transmission and wired transmissions among RSUs are both utilized, since wired transmissions can reduce the delay and improve the reliability. Each RSU is equipped with a machine learning system (MLS) to make the selected relay link more reliably without GPS through predicting vehicle speed at next moment. Experiments show the validity and rationality of the proposed method.

[1]  Luciana Arantes,et al.  Applying Machine Learning to Reduce Overhead in DTN Vehicular Networks , 2014, 2014 Brazilian Symposium on Computer Networks and Distributed Systems.

[2]  Anna Maria Vegni,et al.  Forwarder smart selection protocol for limitation of broadcast storm problem , 2015, J. Netw. Comput. Appl..

[3]  Ozan K. Tonguz,et al.  On the Broadcast Storm Problem in Ad hoc Wireless Networks , 2006, 2006 3rd International Conference on Broadband Communications, Networks and Systems.

[4]  Andrew W. Moore,et al.  Locally Weighted Learning , 1997, Artificial Intelligence Review.

[5]  Tan Yan,et al.  A Grid-Based On-Road Localization System in VANET with Linear Error Propagation , 2014, IEEE Transactions on Wireless Communications.

[6]  Durga Lal Shrestha,et al.  Instance‐based learning compared to other data‐driven methods in hydrological forecasting , 2008 .

[7]  Guo-Tan Liao,et al.  A Multi-level Fuzzy Comprehensive Evaluation Approach for Message Verification in VANETs , 2012, 2012 Third FTRA International Conference on Mobile, Ubiquitous, and Intelligent Computing.

[8]  Myung-Ki Kim,et al.  The Effect of Swiss Ball Stabilisation Exercise on Deep and Superficial Cervical Muscle and Pain in Patients with Chronic Neck Pain , 2015 .

[9]  Xuemin Shen,et al.  Opportunistic Spectrum Access for CR-VANETs: A Game-Theoretic Approach , 2014, IEEE Transactions on Vehicular Technology.

[10]  Robert LIN,et al.  NOTE ON FUZZY SETS , 2014 .

[11]  W. R. Schucany,et al.  Gaussian‐based kernels , 1990 .

[12]  Wei Kuang Lai,et al.  A Machine Learning System for Routing Decision-Making in Urban Vehicular Ad Hoc Networks , 2015, Int. J. Distributed Sens. Networks.

[13]  T. Sivakumar,et al.  Position Prediction based Multicast Routing (PPMR) using Kalman Filter over VANET , 2016, 2016 IEEE International Conference on Engineering and Technology (ICETECH).

[14]  Zhili Sun,et al.  An evaluation of routing in vehicular networks using analytic hierarchy process , 2016, Wirel. Commun. Mob. Comput..

[15]  Stephen D. Clark,et al.  Traffic Prediction Using Multivariate Nonparametric Regression , 2003 .

[16]  Byoung-Jo Yoon,et al.  Dynamic near-term traffic flow prediction: system- oriented approach based on past experiences , 2012 .

[17]  Xuemin Shen,et al.  Toward Multi-Radio Vehicular Data Piping for Dynamic DSRC/TVWS Spectrum Sharing , 2016, IEEE Journal on Selected Areas in Communications.

[18]  Hesham A. Rakha,et al.  Applying Machine Learning Techniques to Transportation Mode Recognition Using Mobile Phone Sensor Data , 2015, IEEE Transactions on Intelligent Transportation Systems.

[19]  Zhou Su,et al.  The Next Generation Vehicular Networks: A Content-Centric Framework , 2017, IEEE Wireless Communications.

[20]  Hongke Zhang,et al.  Enhancing Crowd Collaborations for Software Defined Vehicular Networks , 2017, IEEE Communications Magazine.

[21]  Xuemin Shen,et al.  Vehicular WiFi offloading: Challenges and solutions , 2014, Veh. Commun..

[22]  Yasmine A. Fahmy,et al.  Two-way TOA with limited dead reckoning for GPS-free vehicle localization using single RSU , 2013, 2013 13th International Conference on ITS Telecommunications (ITST).

[23]  Dharani Kumari Nooji Venkatramana,et al.  AMGRP: AHP-based Multimetric Geographical Routing Protocol for Urban environment of VANETs , 2017, J. King Saud Univ. Comput. Inf. Sci..

[24]  Gabriel-Miro Muntean,et al.  Towards Reasoning Vehicles , 2017, ACM Comput. Surv..

[25]  Wanjiun Liao,et al.  Intersection-based routing for urban vehicular communications with traffic-light considerations , 2012, IEEE Wireless Communications.

[26]  V. Fedorov,et al.  Moving Local Regression: The Weight Function , 1993 .

[27]  Michael J Demetsky,et al.  Multiple-Interval Freeway Traffic Flow Forecasting , 1996 .

[28]  Neeraj Tyagi,et al.  Fuzzy Logic based Greedy Routing (FLGR) in Multi-Hop Vehicular Ad hoc Networks , 2015 .

[29]  C. Siva Ram Murthy,et al.  An Analytic Hierarchy Process Based Approach for Optimal Road Side Unit Placement in Vehicular Ad Hoc Networks , 2014, 2014 IEEE 79th Vehicular Technology Conference (VTC Spring).

[30]  Tuo Shen,et al.  The analytic hierarchy process-based optimal forwarder selection in multi-hop broadcasting scheme for vehicular safety , 2014, 2014 IEEE Intelligent Vehicles Symposium Proceedings.

[31]  Eneko Osaba,et al.  A Hybrid Method for Short-Term Traffic Congestion Forecasting Using Genetic Algorithms and Cross Entropy , 2016, IEEE Transactions on Intelligent Transportation Systems.

[32]  Chao Chen,et al.  Short‐Term Traffic Speed Prediction for an Urban Corridor , 2017, Comput. Aided Civ. Infrastructure Eng..

[33]  Jung-Shian Li,et al.  Intelligent Adjustment Forwarding: A compromise between end-to-end and hop-by-hop transmissions in VANET environments , 2013, J. Syst. Archit..

[34]  Xin Wang,et al.  Self-Adaptive On-Demand Geographic Routing for Mobile Ad Hoc Networks , 2012, IEEE Transactions on Mobile Computing.

[35]  Zuzana Komínková Oplatková,et al.  Tracking progress of African Peer Review Mechanism (APRM) using fuzzy comprehensive evaluation method , 2014, Kybernetes.

[36]  Sungzoon Cho,et al.  Locally linear reconstruction for instance-based learning , 2008, Pattern Recognit..

[37]  Dongjoo Park,et al.  Dynamic multi-interval bus travel time prediction using bus transit data , 2010 .

[38]  Donghyun Kim,et al.  Cognitive radio based connectivity management for resilient end-to-end communications in VANETs , 2016, Comput. Commun..