VANET (Vehicular Ad-hoc Network) is a self-organizing ad-hoc network wherein vehicles act as a node and every node consists of On Board Unit (OBU) which forwards data packets to another node through Road-Side Unit by using routing. VANET has several concerns among which routing is one of the significant concerns to route the packets efficiently from source to final destination.
VANET has frequently changing topology which results in a frequent disconnection between nodes (Vehicles). A routing protocol is responsible for communication between nodes (Vehicles) by establishing the route, forwarding packets, and maintaining the routes. Communication in VANET can be done by two types; [11] 1. Vehicle to Vehicle (V-V). i.e. Node-Node. VANET [17] is planned to act self-arranging in a environment of moving nodes/ vehicles, enduring a variety of organization exhibitions, for example, Node mobility, Link failure, and Packet loss. Vanet has higher versatility and much of the time-evolving geography. The mobility models of VANET are responsible for the pattern of nodes on the road in real-time.
In AODV, [17] at every node, it maintains a routing table. The routing table is a passage for a destination node that contains the accompanying three significant fields; [17] 1. A next-hop node: All packets that are going to be sent to the destination are directed to the next hop node. 2. A sequence number: Method of time stamping & it is a degree of freshness & route. 3. A hop-count: Contemporary distance to the destination node.
In AODV, Nodes choose courses/routes in the requesting response cycle; 1. A node requests a way to the endpoint node by spread of RREQ (Route Request) message to each neighboring node. 2. When the node gets the RREQ message anyway doesn't have the way to the referenced destination it, thus, broadcast the RREQ message.This measure rehashes until the RREQ arrives at a node that has a substantial path to the destination. Also, it brings out a converse course to the requesting node which can be used to progress resulting responses to this RREQ. 3. This measure reiterates until the RREQ shows up at a hub that has a considerable way to the destination. 4. The end node reacts with RREP (Route Reply) message. The RREP message is unicast along the contrary course of the transitory node until it appears at the demanding node. 5. When the node setbacks its accessibility to its next bounce, the node invalidates its route by sending RERR (Route Error) message to all nodes that potentially received the RREQ message. 6. On acknowledgement of these three messages: RREQ, RREP, RERR; The node updates the next hop, sequence number, and the hop counts for its route.
7. The remaining paper includes: Section 2 presents the literature study and different routing protocols, approaches to investigate network parameters for different scenarios in VANET. Section 3 presents proposed work and methodology. Section 4 presents the performance of quality of service parameters. Section 5 includes the study of AODV routing protocol and results. Section 6 includes the conclusion of this paper and gives an idea about the scope of VANET in the future and last, section 7 includes references.
Authors in [5] and [14] import the real-time map from OSM (Open Street Map) Wizard for vehicle mobility patterns constrained to realistic map topology. SUMO does not consider some parameters such as Left-Handed Traffic, Traffic signals, speed limit, and other details. The author considers two radio propagation models in [13] i.e. Two Ray Ground model and the Nakagami model. The platform used by the author in [13] is NS-2 along with MOVE & SUMO. The author considers the AODV & OLSR routing protocol for investigating packet-delivery ratio and end-to-end delay for two different radio propagation models.
1. Browsing openstreetmap.org, select the specific region physically then export it.. 2. map.osm record will be downloaded for that specific zone. Rename it according to your comfort. 3. By utilizing sumo commands, for example, netconvert and polyconvert we changed over the osm map in a setup document for example .cfg. You will discover the osmpolyconvert.typ.xml document in sumo/information/typemap path. Netconvert imports advanced street networks from different sources and makes street organizations. Polyconvert imports polygons from different sources and converts them into the representable pictured structure utilizing sumogui. 4. We have downloaded map.osm file just and map.net.xml, map.poly.xml, polyconvert.typ.xml records are produced through map.osm file. 5. The next step is to create a route file using the randomTrips.py file and the further configuration file is created. 6. The FCD command is useful for integrating the map with NS-2. The FCD is a floating vehicle information trade that incorporates area and speed alongside other related data for each vehicle in that selected region of map at each time step.
Various applications read "vehicular traces", files containing mainly position of vehicles over the time. traceExporter.py produces such records by changing over SUMO's fcd yield into a few upheld designs $python/home/user/sumo/tools/traceExporter.py -fcd-input coepmap.sumo.xml -ns2config-output coepmap.tcl -ns2mobility-output mobility.tcl -ns2activity-output activity.tcl We can likewise examine the vehicular behavior in SUMO while running the simulation. An illustration of it is appeared in the accompanying figure. 1. With a network density of 100 vehicular nodes, we have made a traffic situation utilizing SUMO and coordinated the map into NS-2. The Trace exporter file created by SUMO is traded to NS-2 for investigating the network parameters. The accompanying figure shows the way toward incorporating the traffic simulator system and network simulator; Utilizing SUMO varying topology pattern is produced. The produced traffic design is then incorporated with Network Simulator-2 (NS-2) with the assistance of Netconvert and Net.Polyconvert.
The code is open source with the goal that the examination local area can alter it according to prerequisite. NS-2 backings the quantity of hubs up to 20000 with the goal that it is simpler to examine the outcomes for a real-time.
In the wake of incorporating traffic design with Network Simulator, We will make an awk content for dissecting and plotting the consequences of quality of service parameters like delay, throughput, packet delivery ratio, end-to-end delay, and so on.
The tcl file is to be modified and need to set network parameters like Channel type, Transport protocol, MAC type, propagation model, number of nodes, speed, Application type, etc.
The tcl file can be changed according to client prerequisite and can be controlled by utilizing NS-2 simulator by providing the accompanying command: $ ns filename.tcl If your code is correct then you will get NS Sorting List…Done message.
The following figure illustrates how to run tcl script in NS-2; The next step is to visualize the animation of your tcl script by using NetAnimation with the help of the following command: $ nam filename.nam NetAnim is an offline animator which animates XML trace files and user can observe the animation with the help of its GUI.
The following figure demonstrates the simulation of the selected map with the help of Net Animation (NetAnim); It is defined as the ratio of the number of packets received by the destination node to the number of packets sent by the source node. The higher the packet delivery ratio, the better the performance of the network is. Equation 12. Throughput:
It is characterized as the measure of information imparted from the source node to the destination node per unit time. Equation 23. End-To-End Delay:
It can be termed as the time occupied by the packet to be transmitted from the source node to the destination node.
In the graph given underneath, we considered random waypoint mobility model. The propagation model considered is Two-Ray-Ground model and investigate the throughput. Likewise we considered TCP transport protocol since it has retransmission facility which would expand the throughput when contrasted with UDP transport protocol.
The accompanying figures shows the graph for throughput utilizing GNUPlot.
The Packet Delivery Ratio can be observed by using GNUPlot for varying node density. Here, we acquired PDR=97.996596 for 100 moving nodes which is very better. Packet Delivery Ratio
The principle objective of this paper is to understand the VANET behavior for the real-time scenario. The detailed literature review is carried out in accordance to improve the quality of service parameters. We set up the real traffic scenario of COEP / Sancheti Bridge, Shivaji Nagar, Pune considering 100 node density which will help us analyzing performance metrics of VANET such as packet delivery ratio, end-to-end delay, throughput, etc. The main uniqueness of this paper is to implement a realistic movement scenario of vehicles and improving their network parameters with the help of SUMO and NS-2. We have selected the AODV protocol for improving its performance metrics because it can be applied to large-scale ad-hoc networks and its responses to link failure. In the future, we will concern about improving the security, mobility, and reliability of the VANET network using the AODV routing protocol.