NGWN final project Team 6

Topic: Vehicular Networks

Team 6

R99922041 R99922083 R99945051

1CARS: Context-Aware Rate Selection for Vehicular NetworksP. Shankar , T. Nadeem , J. Rosca , L. Iftode , Proc. IEEE ICNP , Oct. 2008 , pp. 1 12

Speaker:

OutlineIntroductionKey challengesContext Aware Rate SelectionPerformanceConclusionRate SelectionIEEE 802.11 allows multiple transmission rate at the physical layer (PHY).

Low bitrateHigh bitrateLow link qualityGoodHigh error rateHigh link qualityUnderutilizationGoodLink Quality

Low link quality due to long distance.High link quality.OutlineIntroductionKey challengesContext Aware Rate SelectionPerformanceConclusionKey challengesRapid variations of the link quality.

Mobility at vehicular speed.Key challengesFew or no packets transmitted in estimation window during infrequent and bursty transmission.

No past history to estimate link quality.Key challengesDistinguish losses due to environment from hidden-station induced collision.

Loss due to hidden station: rate transmission time contention

OutlineIntroductionKey challengesContext Aware Rate SelectionPerformanceConclusionArchitecture

Positions and speeds of itself and its neighbors.Past transmission history.AlgorithmEcEhContext informationTransmission ratePacket lengthPast frame transmission statisticsinputinputfor all rate doPacket error rateThroughputend forFind the rate that maximize the throughput.(1-) is assigned based on the vehicle speed.AlgorithmEcEhExponentially weighted moving average (EWMA) of past frame transmission statistics.Empirical model.Measurements from extensive outdoor vehicular experiments.Use multivariate linear regression as the learning approach.

http://en.wikipedia.org/wiki/File:Exponential_moving_average_weights_N%3D15.pngOutlineIntroductionKey challengesContext Aware Rate SelectionPerformanceConclusionExperimental Result

Simulation Result

OutlineIntroductionKey challengesContext Aware Rate SelectionPerformanceConclusionConclusionContext Aware Rate SelectionUse context information to perform fast rate adaption in vehicular network.Connectivity-Aware Routing (CAR) in Vehicular Ad Hoc NetworksValery Naumov & Thomas R. GrossETH Zurich, Switzerland IEEE INFOCOM 2007speaker:OutlineIntroductionRelated Works (GPSR)Connection-Aware Routing (CAR)SimulationConclusionIntroductionVehicular ad hoc networks (VANETs) using 802.11-based WLAN technology have recently received considerable attention in many projects

Several geographic routing (GR) protocols use an idealized mechanism such that for every originated data packet the true position of the destination is known

IntroductionAnother problem is that, all of the GR protocols do not take into account if a path between source and destination is populated.

This paper presents a novel position-based routing scheme called Connectivity-Aware Routing (CAR) to address these kind of problemsOutlineIntroductionRelated Works (GPSR)Connection-Aware Routing (CAR)SimulationConclusionGreedy Perimeter Stateless Routing

Greedy Perimeter Stateless RoutingPerimeter Mode

Greedy Perimeter Stateless Routing

OutlineIntroductionRelated Works (GPSR)Connection-Aware Routing (CAR)SimulationConclusionConnection-Aware Routing (CAR)The CAR protocol consists of four main parts: (1) destination location and path discovery(2) data packet forwarding along the found path (3) path maintenance with the help of guards (4) error recoveryDestination location discoveryA source broadcast a path discovery (PD)Each node forwarding the PD updates some entries of PD packetsIf two velocity vectorsangle > 18, anchor is set.

Greedy forwarding over the anchored pathA neighbor that is closer to the next anchor point is chosen (greedy) , instead of destination.

Path maintenanceIf an end node (source or destination) changes position or direction, standing guard will be activated to maintain the path.

Path maintenanceIf end node changes direction against the direction of communication, traveling guard will be activated.

A traveling guard runs as end nodes old direction and speed, and reroute the packets to the destination.

Path maintenance

Routing error recoveryThe reason for routing errorA temporary gap between vehicles

(1) Timeout algorithmWhen a node detects a gap buffer the packets(2) Walk-around error recoveryWhen Timeout algorithm fail , do location discoveryWhether the location discovery is successful, the result will be reported to the source node.

OutlineIntroductionRelated Works (GPSR)Connection-Aware Routing (CAR)SimulationConclusionSimulationScenariosCity Highway

Traffic densityLow less than 15 vehicles/kmMedium 30-40 vehicles/kmHigh more then 50 vehicles/km

Simulation-Packet Delivery Ratio

Simulation-Average data packet delay

Simulation-Routing overhead

OutlineIntroductionRelated Works (GPSR)Connection-Aware Routing (CAR)SimulationConclusionConclusionAddress the populated problem about paths.

Path discovery & Anchor pointsPath maintenance with guards Error recovery

Higher performance and lower routing overhead than GPSR

Delay-bounded Routing in Vehicular Ad-hoc NetworksAntonios Skordylis, Niki TrigoniOxford University Computing LaboratoryACM International Symposium on Mobile Ad hoc Networking and Computing, 2008Speaker : R99945051 OutlineIntroductionVANETsDelay-bounded RoutingObjective and ModelAlgorithmD-GreedyD-MinCostEvaluation and resultConclusion

IntroductionVANETsvehicles equipped with wireless transceivers that will enable them to communicate with each other form a special class of wireless networkDelay-bounded Routingtimely and bandwidth efficient data dissemination from vehicles to an access point, given statistical information about road traffictradeoff : timely data delivery v.s. low bandwidth utilization

VANETswireless transceivers,

Delay-bounded Routing: datavehicleaccess point, vehicletraffic, bandwidth

dissemination

VANET-based applications1.require broadcasting of information from one vehicle to many nearby vehicles vehicleinfomationvehicles Ex. collision2. those that require the propagation of information hop-by-hop to a single destination point or area hop-by-hop informationdestination Ex. fixed networkunit

44OutlineIntroductionVANETsDelay-bounded RoutingObjective and ModelAlgorithmD-GreedyD-MinCostEvaluation and resultConclusionObjectiveObjectivecarry-and-forward algorithms leverage knowledge of traffic statistics in an urban settingenable timely delivery of messages from vehicles to APsminimizing wireless transmissions/optimizing bandwidth utilizationWireless communication reliable,high mobility multi-path fading, shadowing, Doppler shifts

//Our focus is not on designing physical and MAC protocols for reliable packet transmission; we assume that such protocols are already in place, and our goal is to design routing protocols for propagating messages along multiple hops to a convenient AP.

46ModelUrban scenarioVehicles (mobile nodes ):geographical position(GPS receiver )digital map(G(V,E)) : historical traffic statistics u: average speed,d: average vehicle density per road segmentcommunication range: 250mAPs(stationary access points ):infrastructure nodes whose absolute location in known to all vehiclesMessage informations:tg : message generation time : time-to-live value, message delay threshold

urban scenario: mobile nodes (vehicles) stationary access points (APs)

: geographical position : GPS receiver digital map: G(V,E) Map preloaded traffic statistics (u : ; d : road segmentdensity)communication range250m (short to mid range transceivers)

APcommunicate, APinfrastructure nodes, AP

msg: tg : msg : message delay threshold AP

47OutlineIntroductionVANETsDelay-bounded RoutingObjective and ModelAlgorithmD-GreedyD-MinCostEvaluation and resultConclusionAlgorithmForwarding a messageminimize the number of transmissionswithin the message-specific delay thresholdAlternate between two forwarding strategies:Multihop ForwardingData MulingAlgorithmsD-GreedyD-MinCost

Our algorithms intend to minimize the number of transmissions while forwarding a message to an access point within the message-specific delay thresholdtransmissions , delay thresholdforward message

forwarding strategies1) Multihop Forwarding : which refers to the aggressive forwarding of messages to vehicles that are better positioned to deliver them to an access point and (AP)node2) Data Muling : which refers to buffering messages in local memory and carrying them at the vehicles speed. msg bufferlocal memory, vehicles speed 49

D-GreedyDelay-bounded Greedy ForwardingNo knowledge of global traffic conditionsAvailable location information, ex. Node speedBest path: shortest path

D-Greedyglobal, pathshortest pathAP, D-Greedy

delay budgetuniformly distributedAPshortest pathedgeedgedelay budgetedge

50D-GreedyEach vehicle maintains a neighbor list by periodically broadcasting beaconsid : unique vehicle identifierdistToAP : the length of the shortest path between the vehicles current location and the location of the closest access point (Dijkstra)TTL : delay threshold value for messagedistToInt : the remaining length, until the next intersection, of the current street segment e

vehicleneighborlist (broadcast beacons) Beacon: 1) id : vehicle ID 2)distToAP : APTTL: msg delay thresholddisToInt : edge e

each edge on the path is allocated a delay budget that is proportional to its length51D-Greedy:FormulationData Muling strategyDelDM DelMultihop Forwarding strategyotherwiseDel : available delay budgetDel = TTL distToInt/distToAPDelDM = distToInt/u

Del: edgebudgetDelDM : distToInt/u (/) Data Muling (buffer) DelDM Del : Data MulingOtherwise : Multihop Forwarding :node Data Muling AP Multihop Forwarding

?transmissions (bandwidth)

52D-MinCostDelay-Bounded Minimum Cost ForwardingKnowledge of global traffic statisticsex. estimated values of average vehicle speed u and density d for all edges of the street graph G.bandwidth-efficient delay-constrained paths

D-MinCostAnnotate each edge with two metricsC : cost , #message transmissions along the edgeDel : delay , the time required to forward a message along the edgeGraph extensionDel Data Muling Multihop Forwarding 54D-MinCostData Muling strategyDelDM = l/u , CDM = 1l : the length of the edge u : the average vehicle speed along that edgeMultihop Forwarding strategyif l > R and d l/R CMH = l/R, DelMH = CMH qR : communication ranged : the average vehicle density for the edgeq : the time required for the node to check its neighbor list and identify the best next hop

Cost & DelayData Muling DelDM = = l/u CDM = 1 intersetionMultihop Forwarding edgecommunication range edge density >= edge/communication range CMH = = edge/communication range DelMH = = checkhop

55D-MinCost:Path selectionThe minimum cost pathThe delay-constrained least-cost routing problem is known to be NP-completeHeuristics: the Delay Scaling Algorithm(DSA) [7]The AP that can be reached with the least cost.The exact min-cost path to that AP.The strategy that should be followed at each edge of the path in order to adhere to the messages remaining delay budget.

AP min-cost path strategy 56OutlineIntroductionVANETsDelay-bounded RoutingObjective and ModelAlgorithmD-GreedyD-MinCostEvaluation and resultConclusionEvaluation and resultCompared with Epidemic routing : achieves optimal delay and delivery ratio under our scenarioMinDelay : a greedy delay minimizing scheme inspired by [16]Compared with Epidemic & MinDelay

58Delivery Ratio

Epidemic packet delivery ratio upper bound MinDelaypaper: D-GreedyD-MinCost Epidemic10%

59Transmitted Bytes

Transmitted Bytes bandwidth utilizationEpidemicmultiple-copy scheme

,D-MinCost transmitted byte MinDelay75%D-Greedy MinDelay 45%

60Message Delay

Epidemicminimum delay path path

D-MinCostD-Greedymsg delaybudget

D-MinCostD-Greedy D-MinCostminimum cost path Data muling, ; D-Greedyshortest path

MinDelayData muling D-MinCostD-Greedy

61Strategy chosen

D-Greedy, APMultihop Forwarding

D-MinCost62OutlineIntroductionVANETsDelay-bounded RoutingObjective and ModelAlgorithmD-GreedyD-MinCostEvaluation and resultConclusionConclusionTwo novel packet forwarding schemes :D-GreedyD-MinCostAchievementForwarding strategyMinimize communication costAdhere to defined delay thresholdOutperformcommunication costSimilar delivery ratio to EpidemicCompared withEpidemic &MinDelay