Efficient Packet Forwarding In Vehicular Ad Hoc Computer Science Essay

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Abstract- In recent years, vehicular communications are one of the hottest research topics. It has also gained much attention in industry as well as academia. Vehicular Ad Hoc Networks (VANETs) are advances of the wireless communication technologies. Routing is one of the key research issues in VANETs as long as it plays an important role in public safety and commercial applications. In VANET, routing of data is a challenging task due to high speed of nodes (i.e., vehicles) movement and rapidly changing topology. Recent research showed that existing routing algorithm solutions for Mobile Ad Hoc Networks (MANETs) are not able to meet the unique requirements of vehicular networks. In this paper, we propose Gateway Node-Based Greedy Routing (GNGR), a reliable greedy position based routing approach. In GNGR, we forward the packets to any of the nodes in the corner of the transmission range of source/forwarding node as most suitable next hop. With this consideration, the nodes move towards the direction of the destination. We propose Dynamic Transition Mobility Model (DTMM) to evaluate our routing technique. This paper gives a complete description of our packet forwarding approach and simulation results. The simulation results are carried out based on Packet Delivery Ratio (PDR). Our routing technique is compared with other routing techniques; the PDR is improved significantly compared with other routing techniques of VANET.

Introduction

Vehicular Ad Hoc Networks (VANETs) are special cases Mobile Ad Hoc Networks (MANETs). VANETs are distributed, self-organizing communication networks between moving vehicles. The Intelligent Transportation Systems (ITS) have been developed to improve safety, security and efficiency of transportation systems for traveling, which apply rapidly emerging information technologies in vehicles and transportation infrastructures. Inter-Vehicle Communication (IVC) is essential to the ITS, which aims at enhancing the public and private safety as well as increasing the efficiency of the transportation system. The Dedicated Short Range Communications (DSRC) system is developed based on IEEE 802.11 WLAN technologies for the purpose of exchanging information among vehicles. The field of IVC, including both Vehicle-to-Vehicle communications (V-V) and Vehicle-to-Roadside communications (V-R), also known as VANETs, is recognized as an important component of ITS.

Fig. 1 shows the vehicular networks reference architecture. This reference architecture is distinguished between three domains: in-vehicle domain, ad hoc domain, and infrastructure domain [1].

Fig. 1: Vehicular Networks Reference Architecture

The rest of the paper is organized as follows: In Section 2, the background and related work will be presented. In Section 3, we propose new routing algorithm called Gateway Node Based Greedy Routing Algorithm (GNGR). In Section 4, we present the simulation results and analysis.

BACK GROUND AND RELATED WORK

In this section, we briefly summarize the characteristics of VANETs related to routing and also survey the existing routing schemes of VANETs.

VANETs Characteristics

Vehicular networks have special behavior and characteristics and it is distinguished from other types of mobile networks [2]. The characteristics of VANETs are Unlimited transmission power, High computational capability, Predicable Mobility, Potentially large scale, High Mobility, Partitioned network, Network connectivity.

MANETs Routing Protocols

The routing protocols in MANETs can be classified into two categories, proactive and reactive and they can be classified by their properties.

Proactive routing protocols: The proactive routing protocols are based on the table-driven approach. In Table-Driven routing algorithm (e.g., DSDV, OLSR), every node maintains the network topology information in the form of routing tables by periodically exchanging routing information. Routing information is generally flooded in the whole network. Whenever a node requires a path to a destination, it runs an appropriate path-finding algorithm on the topology information it maintains, even if these paths are not currently used. The main drawback here is that the maintenance of un-used paths may become a significant part of the available bandwidth if the network topology changes frequently. In the case of vehicular networks, the movement of vehicles is extremely dynamic so that we did not further investigate proactive approaches.

Reactive routing protocols: Reactive routing protocols are based on on-demand approach (e.g., DSR, TORA, and AODV). Protocols that fall under this category do not maintain network topology information. They obtain the necessary path when it is required, by using a connection establishment process. These protocols do not exchange routing information periodically. These protocols perform well in static and low-mobility environments. However, when mobility of nodes increases speed, the performance of protocol decreases. Hence, reactive routing approach is fit into very limited number of routes of vehicular communication application. On the other hand, the routing protocol is classified into two approaches: Topology based routing, Position based (geographic) routing. Topology-based routing (e.g., AODV) only considers topology connection of the nodes. The drawback is its large latency. To overcome this kind of limitation, a Position-based routing algorithm has been introduced (e.g., GPSR, Terminodes routing). Position based routing protocol requires information about the physical position of the participating nodes. Routing decision at each node is then based on destination's position contained in the packet and the position of forwarding node's neighbors.

VANETs Routing Protocols

Following are a summary of representative VANETs routing algorithms.

GSR (Geographic Source Routing): Lochert et al. in [3] proposed GSR, a position-based routing with topological information. This approach employs greedy forwarding along a pre-selected shortest path. The simulation results show that GSR outperforms topology based approaches (AODV and DSR) with respect to packet delivery ratio and latency by using realistic vehicular traffic. But this approach neglects the case that there are not enough nodes for forwarding packets when the traffic density is low. Low traffic density will make it difficult to find an end-to-end connection along the preselected path.

GPCR (Greedy Perimeter Coordinator Routing): To deal with the challenges of city scenarios, Lochert et al. designed GPCR in [4]. This protocol employs a restricted greedy forwarding procedure along a preselected path. When choosing the next hop, a coordinator (the node on a junction) is preferred to a non coordinator node, even if it is not the geographical closest node to destination. Similar to GSR, GPCR neglects the case of low traffic density.

A-STAR (Anchor-based Street and Traffic Aware Routing): To guarantee an end-to-end connection even in a vehicular network with low traffic density, Seet et al. proposed A-STAR [5]. A-STAR uses information on city bus routes to identify an anchor path with high connectivity for packet delivery. By using an anchor path, A-STAR guarantees to find an end-to-end connection even in the case of low traffic density. This position-based scheme also employs a route recovery strategy when the packets are routed to a local optimum by computing a new anchor path from local maximum to which the packet is routed. The simulation results show A-STAR achieves obvious network performance improvement compared with GSR and GPSR. But the routing path may not be optimal because it is along the anchor path. It results in large delay.

MDDV (Mobility-Centric Data Dissemination Algorithm for Vehicular Networks): To achieve reliable and efficient routing, Wu et al. proposed MDDV [6] that combines opportunistic forwarding, geographical forwarding, and trajectory-based forwarding. MDDV takes into account the traffic density. A forwarding trajectory is specified extending from the source to the destination (trajectory-based forwarding), along which a message that will be moved geographically closer to the destination (geographical forwarding). The selection of forwarding trajectory uses geographical knowledge and traffic density. MDDV assumes traffic density is static. Messages are forwarded along forwarding trajectory through intermediate nodes which store and forward messages opportunistically. This approach focuses on reliable routing. But trajectory-based forwarding will lead to large delay if the traffic density varies by time.

5) VADD (Vehicle-Assisted Data Delivery): network with tolerable delay, Zhao and Cao proposed VADD [7] based on the idea of carry and forward by using predicable mobility specific to sparse networks. Instead of routing along a preselect path, VADD chooses next hop based on highest pre-defined direction priority by selecting the closest one to destination. Their simulation results show VADD outperforms GPSR in terms of packet delivery ratio, data packet delay, and traffic overhead. This approach predicts the directions of vehicles movement. But it doesn't predict the environmental change in the future.

6) PDGR (Predictive Directional Greedy Routing): Jiayu Gong et al. proposed PDGR [8], in which the weighted score is calculated from two strategies namely, position first forwarding and direction first forwarding. With these strategies, the current neighbors and possible future neighbors of packet carrier are found. The next two hops away are calculated from weighted scores of next hops using PDGR. Here next hop selection is done on prediction and it is not reliable, at all situations. The delivery of packet does not guarantee to the node present in the corner of transmission range of forwarding node, which is considered as most suitable next hop, due to high dynamics of vehicles. This will lead to low packet delivery ratio, high end to end delay and increased packet drops.

The various routing protocols of MANETs and VANETs are analyzed and drawbacks of those routing protocols are described below.

D. Gaps of MANETs and VANETs Routing Protocols

MANETs Routing Protocol

AODV - Large latency of packet transmission

DSR - Large latency of packet transmission

OLSR - High bandwidth consumption due to dynamic topology.

GPSR - Frequent network disconnection, Routing loops, Too many hops and Wrong direction.

VANETs Routing Protocol

GSR - End to end connection is difficult in low traffic density

GPCR - End to end connection is difficult in low traffic density

A-STAR - Routing paths are not optimal and results in large delay of packet transmission.

MDDV- Large delay if the traffic density varies by time

VADD - Large delay due to varying topology and varying traffic density

PDGR - Too many hops, Large Delay if the traffic density is high and Low Packet delivery ratio.

Proposed Routing Algorithm

Gateway Node-Based Greedy Routing Algorithm (GNGR)

GNGR is a greedy position based reliable routing algorithm and it is designed for sending messages from any node to any other node. In this, the sending of message is from one node to another node (i.e., Unicast) or from one node to all other nodes (i.e., Broadcast/Multicast) in a vehicular ad hoc network. The common design goals of GNGR algorithm are to deliver messages with high reliability and to optimize packet behavior for ad hoc networks with high mobility.

The functional operations of GNGR algorithm is Identification of Neighbor Node (INN), Calculation of Distance (CD) between nodes, Identification of Moving Direction (IMD) of the nodes, Link Stability Calculation (LSC) between nodes, Weighted Score Calculation (WSC) to identify the next hop which is closer to the destination, Gateway Node Selection (GNS).

In the following section, the general assumptions of GNGR algorithm are discussed.

Assumptions

We assume that every vehicle is equipped with special device (ie., OBU), static digital maps, GPS receiver. With help of OBU, vehicles can communicate with one another within each ones radio transmission range and GPS receivers get their accurate geographical location. This geographical location gives the location of destination well in advance, so that the source/forwarder node can send packets to a destination. We also assume each vehicle has the knowledge of its own velocity and direction.

Gateway Node Selection Procedure

The main aim of selection of gateway node is packet forwarding event and this gateway node has shortest distance to the destination. Compared to all other nodes, the gateway node has shortest distance within different stages of transmission range of source/packet forwarding node. To avoid the packet loss in high speed mobility of vehicles, we consider for different stages of transmission range. The overall objective of the algorithm is to increase packet delivery ratio, minimize end to end delay and avoid packet loss. The Highest Transmission Range (HTR) of a vehicle/node is 250m and other different stages of transmission ranges are 200m, 150m, 100m and 50m, which are considerably lower than HTR.

Stage1 transmission Range (i.e. S1TR=250m)

Stage2 transmission Range (i.e. S2TR=200m)

Stage3 transmission Range (i.e. S3TR=150m)

Stage4 transmission Range (i.e. S4TR=100m)

Stage5 transmission Range (i.e. S5TR=50m)

Pseudo Code for GNGR

currentnode : the current node packet

locc: the location for current node

: the speed vector for current node

dest:destination for the packet

locd: the location for destination

nexthop: the node selected as next hop

neighi: the ith neighbor

loci: the location of the ith neighbor

: the speed vector of the ith neighbor

WSi : Weighted Score of node i

: Weighted Score factors

Di: Shortest distance from gateway node i to destination D

Dc: Shortest distance from packet forwarding node c to destination D

: Closeness of nexthop

: Vector for the location of gateway node i to the location of destination node D

:Cosine value of angle made by these vectors

: Link Stability between packet forwarding node c to edge node i

S1TR: Stage1 Transmission Range = 250m

S2TR: Stage2 Transmission Range = 200m

S3TR: Stage3 Transmission Range = 150m

S4TR: Stage4 Transmission Range = 100m

S5TR: Stage5 Transmission Range = 50m

locc getLocation (currentnode)

getSpeed (currentnode)

locd getLocation (destination)

CD ( 1 - )

IMD

Dc = distance(locc, locd)

= locd - locc

WS =

nextHop = currentnode

for all neighbors of currentnode do

loci getLocation (neighi)

getSpeed (neighi)

Di = distance(locd, loci)

Dci = distance(locc, loci)

for all neighbors of currentnode with Dci do

if (Dci S1TR && Dci S2TR)

= locd - loci

WSi = c,i

for neighi with greater WSi do

WS = WSi

nextHop = neighi

end for

else if (Dci S2TR && Dci S3TR)

= locd - loci

WSi = c,i

for neighi with greater WSi do

WS = WSi

nextHop = neighi

end for

else if (Dci S3TR && Dci S4TR)

= locd - loci

WSi = c,i

for neighi with greater WSi do

WS = WSi

nextHop = neighi

end for

else if (Dci S4TR && Dci S5TR)

= locd - loci

WSi =c,i

for neighi with greater WSi do

WS = WSi

nextHop = neighi

end for

else if (Dci S5TR)

= locd - loci

WSi = c,i

for neighi with greater WSi do

WS = WSi

nextHop = neighi

end for

else

carry the packet with currentnode

end if

end for

end for

Simulation Results and Analysis

In this section, we evaluate the performance of routing protocols GPSR, PDGR and GNGR in an open environment.

Dynamic Transition Mobility Model (DTMM)

We use mobility model called Dynamic Transition Mobility Model (DTMM) as shown in Fig. 2. This mobility model can be used to simulate the movement pattern of moving vehicles on streets or roads that are defined by maps from the GPS, equipped in these vehicles. DTMM works on the basic idea that vehicles which move on roads can communicate directly with each other or through multiple hops transmission in the form of VANET. In DTMM, vehicles or nodes are randomly distributed on roads with linear node density and road includes two or more lanes. The vehicles are designed to move in different speeds and directions (Vertical and Horizontal). Vehicle moving in vertical roads indicate that the cars travel in north/south direction and horizontal road shows east/west direction. Security distance is maintained between two subsequent vehicles in a lane. Vehicles can move in desired direction based on the place of destination vehicle at the junction of the roads. Vehicles can transmit the packet in both directions. In this model, deterministic and instantaneous transmission mechanism is followed, because, the transmission of packet is within a certain radius r=250m from the sender. Within this transmission range, vehicles can unicast, multicast and broadcast packets to the neighbouring vehicles. Vehicles are also allowed to overtake the preceding vehicle during packet transmission.

Fig 2: Dynamic Transition Mobility Model

The simulation that was conducted on network simulator is NCTUns5.0 [9]. NCTUns 5.0 is a network simulator and emulator. It includes provision for vehicular traffic simulation. It is open-source and runs on Linux. It directly uses Linux TCP/IP protocol stack. It can use any real -life existing or to be developed UNIX application program as a traffic generator program. It provides a highly-integrated and professional GUI environment as shown in the Fig.3. The GUI supports the desired road network construction and road information is stored in road network specification file.GUI allows to specify different car profile setting and it is stored in car profile file.

Fig 3: A screen shots of the GUI and road network construction in NCTUns5.0

The movement of vehicles was controlled by setting vehicle movement and information related to this is stored in node movement scenario configuration file. Simulations for each of the routing protocols were carried out with varying number of nodes with specific parameters. The IEEE 802.11 Distributed Coordination Function (DCF) was used as the Medium Access Control Protocol. The packet size was fixed to 512 Bytes. The Traffic source was UDP. Initially the nodes were placed at certain specific locations, and then the nodes were moved with varying speeds towards new locations. The parameters related to mobility model and wireless communications are shown in Table 1. We used a 1000mÃ-1000m square street area for simulation, the number of vehicles travelling on the road ranges from 10 to 100 and constant bit rate was 2 (Packets/Second). In this scenario all vehicles are communicate with transmission range of 250 meters.

Table 1: Simulation Parameters

Parameters

Settings

Simulation Area

1000m x 1000m

Number of Vehicles

10 - 100

Mobility of Vehicles

0 - 50 (meter/second)

Number of Packet Senders

30

Transmission Range

250m

Constant Bit Rate (CBR)

2 (packets/second)

Packet Size

512 Bytes

Vehicle beacon interval

0.5 (seconds)

MAC Protocol

802.11 DCF

The following metric is considered to evaluate simulation results:

Packet Delivery Ratio (PDR): the ratio of packets that successfully reach destination to the original sent ones.

Packet Delivery Ratio vs. Mobility

In this division, packet delivery ratio is compared with different speed of vehicles as shown in Fig.4. The packet delivery ratio of GPSR and PDGR decreases due to increase in the speed of vehicles. The high speed of vehicles paves way for the packet loss at the corner of highest transmission range. In GPSR and PDGR, packet loss is high as compared to other VANETs routing protocols. By increasing the speed of vehicles, the packet loss at the corner of highest transmission range is reduced considerably in GNGR and PDR is also improved to 13.8% as compared with PDGR.

Fig 4: Packet Delivery Ratio vs. Mobility

Packet Delivery Ratio vs. Number of Nodes

In this division, packet delivery ratio is compared with number of nodes as shown in Fig.5. In the beginning, the packet delivery ratio is less due to less number of vehicles for GPSR, PDGR and GNGR. The packet delivery increases depending on the increase of nodes in the routing algorithms. GPSR increases delay of packet transmission, when it switches to perimeter mode in case of no node is available.

Fig 5: Packet Delivery Ratio vs. Number of Nodes

The packet delivery ratio is high for PDGR as compared with GPSR. In PDGR, next hop selection for packet forwarding is done through prediction and it is not reliable at all situations. When the vehicles are moving in high speed, the packet forwarded to the corner of transmission range will be lost. To overcome this kind of situation, the GNGR uses weighted score calculation for selection of next hop. By increasing the number of vehicles, the GNGR reduces the packet loss at highest transmission range and also packet delivery ratio increases as compared with PDGR for 12.4%.

Packet Delivery Ratio vs. Transmission Range

In this division, packet delivery ratio is compared with various transmission ranges as shown in Fig.6. In GPSR, packet forward approach is used for one hop neighbor. In the case of PDGR, two hop neighbors are used to forward the packet. However, as number of neighbors starts increasing in the road network, numbers of hops are also increased in GPSR and PDGR. When the number of hops increases, packet delivery ratio decreases. To overcome this kind of situation, the neighbor node selection is based on the various levels of transmission range (ie., 250m, 200m, 150m, 100, 50m) using distance information from GPS. Number of hops is reduced reasonably by using various transmission ranges in GNGR and also the packet delivery ratio improved to 16.2% as compared with PDGR.

Conclusions

In this paper we have analyzed the various routing characteristics of VANETs and identified the properties of VANETs. The contributions and limitations of MANETs, VANETs were presented. The various unique characteristics

Fig 6: Packet Delivery Ratio vs. Transmission Range

of VANET were compared with MANET. We have proposed new mobility model called Dynamic Transition Mobility Model and also position based greedy routing approach GNGR. The GNGR, GPGR and PDGR were compared in terms of packet delivery ratio. The simulation result shows GNGR significantly improves packet delivery ratio. In the future, our approach requires modifications to city environment characteristics and different mobility models with obstacles. The proposed GNGR routing algorithm approach when compared with other existing approach, gives an improvement of packet delivery ratio over other routing approach.

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