On The IoV Based Traffic Congestion Detection And Management For Intelligent Transportation

The Internet of Vehicles(IoV)provides the basis and the underlying framework to realize the intelligent transport systems(ITS).Nonetheless,there are few research efforts to exploit the surveillance and management capability of the IoV in terms of the traffic congestion related to information collection and processing.In a realistic transport system,information like the inter-vehicle spacing and dynamic platooning plays an important role in the traffic congestion assessment.At the same time,the inter-vehicle spacing provides a good reference for the driver to maintain the driving safety.In this thesis,we focus on IoV based traffic congestion status monitoring techniques,which comprise of traffic data collection,aggregation,congestion detection,congestion prediction,and speed estimation.Meanwhile,the sparse network characteristics,the routing and security challenges in the IoV network are also addressed.Firstly,a microscopic congestion detection protocol(MCDP)was put forward to make the vehicle-to-vehicle communication capable of monitoring vehicle density,and identifying the traffic jam.By introducing transportation control domain in the existing network protocol header,each vehicle can count its neighbors and estimate the time spacing among vehicles.The proposed MCDP aims to provide an infrastructure-less solution to the estimation of vehicle density,flow,and average velocity in a microscopically manner.Moreover,the safety speed limit is introduced to make each vehicle calculate its time to cover the intervehicle distance,such that every vehicle can assess the transportation congestion by comparing the estimated time headway with some predefined safety time threshold.Monte Carlo simulations of MCDP protocol over four typical Chinese highways are presented to compare the MCDP scheme with the existing Green-Shield congestion detection scheme.Moreover,real road traces generated by Simulation of Urban MObility(SUMO)over NS2 are utilized to assess the achieved performance in terms of throughput,end-to-end delay,and packet delivery rate(PDR)in comparison to DSR and AOMDV in IEEE 802.11 p and IEEE 802.11 ac scenarios.By all these results,we conclude that MCDP is an inexpensive transport congestion detection technique for Vehicular Ad Hoc Networks(VANETs).Secondly,with V2 V communication capability,vehicle platoon on the highway can help to reduce traffic congestion.However,the dynamic nature of vehicles imposes challenges on the V2 V based platoon management.In this thesis,by considering the characteristics of Vehicular Ad-hoc Network(VANET),a microscopic platoon management design is proposed to deal with three basic dynamic platoon maneuvers of merging,splitting and speed-change.The platoon stability probability analysis is presented to reveal the relationship between the platoon stability probability and the key transportation parameters.Finally,a VANET based platoon platform is built by using NS2 network simulation to assess the performance over some real road traces generated by SUMO.It is shown that V2V-based dynamic vehicle platoon management can provide an inexpensive technique to cope with the dynamic platoon management requirement.Thirdly,with the increasing number of vehicles,traffic jam is one of the major problems of the fast-growing world.Intelligent Transportation System(ITS)is capable of disseminating perilous warnings and information on forthcoming traffic jams to all vehicles in a given region without any visual contact.Real-time traffic information is the prerequisite for ITS applications development.Therefore,a novel V2I-based Infrastructure-based Vehicular Congestion Detection(IVCD)scheme is proposed to utilize Content Oriented Communication(COC)for vehicular congestion detection and speed estimation.The proposed IVCD computes the safety time(time headway)between vehicles by using iterative COC contents.Furthermore,road-side-sensor(RSS)provides an infrastructure framework by integrating macroscopic traffic properties to calculate the traffic congestion and to estimate safe driving speed for vehicles.The main responsibilities of RSS in IVCD are to preserve privacy,data aggregation,store information,broadcast routing table,estimate safe driving speed,traffic jam detection and Session ID(S-ID)generation for vehicles.Herein Monte Carlo simulations over four typical Chinese highways are used to compare with the existing Greenshield's and Greenberg's macroscopic congestion detection schemes.Besides,real road traces generated by SUMO over NS-3.29 are utilized to compare IVCD performance in term of density and speed health with previous schemes.Simulation results confirm us that,the proposed IVCD scheme provides us with an effective method to precisely control traffic congestion in both single and multi-lane roads.Finally,in the application of the optimized link state routing(OLSR)protocol,neighboring nodes frequently exchange HELLO messages to select their multipoint relay(MPR).Although topology control(TC)messages are exchanged only between MPRs to share their MPR selectors,the exchange of such messages may lead to OLSR vulnerabilities.Therefore,we propose a SOLSR protocol to deal with the secure exchange of control messages by including a global secret key(GSK),authentication,encryption,and timestamp approaches.Moreover,the assessment of the SOLSR protocol in terms of node speed,authentication-then-encryption(ATE)time and node density are presented.The performance comparison between SOLSR and OLSR in terms of data overhead(packet and routing overhead),packet delivery ratio(PDR),security and End-to-End delay(E2E-delay)in different network scenarios are presented to show that,the proposed SOLSR outperforms the OLSR.The analysis and work in this thesis provide a useful reference for the following exploration of the IoV based traffic congestion detection and management techniques.