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Movement Awareness position-based routing protocol for Intersection of Urban Environment Yunho Jung, 2Keecheon Kim Konkuk University, Seoul, Korea jyh07@konkuk.ac.kr, kckim@konkuk.ac.kr

Abstract The vehicle networks on the urban roads have many important factors that influence the performance, such as street layouts and intersections with traffic signs, or inter-vehicle interactions. Thus it is important to use a realistic mobility model. We use real world road layouts in TIGER and compared the performance of the GPSR (position based routing) and the OLSR (reactive routing) popular routing protocol. In this paper, we suggest a position-based routing scheme designed for communication with control node at intersection area, like light controller, in urban environment.

1. Introduction Position-based routing, as it is used by protocols like Greedy Perimeter Stateless Routing (GPSR) [3], is very well suited for high dynamic environments such as inter-vehicle communication on highways. However, it has been discussed that radio obstacles (like building), as they are found in urban areas, have a significant negative impact on the performance of position based routing. This paper analyzed the problem of efficiently data delivery in vehicular ad hoc networks at Urban environments, specially many intersection deployed areas like grid. And examines the possibility of deploying an adaptive control system which collect vehicle information (position, id, neighbor node) at intersections, and system that can base its Data forwarding decision on information coming from other cars. We assume each vehicle is equipped with a short-range wireless communication device, as is a

1 2

controller node placed in every intersection with Data Forwarding System. The remainder of this paper is organized as follows. In section 2, we present related work in the field of Position-based routing in VANET, relevant to our work. In section 3, we present the simulation framework we have configured, in order to evaluate the routing protocol more realistically. Section 4 describes our Intersection Area Data Forwarding System. And we conclude in section 6.

2. Related work Vehicular ad hoc networks have been envisioned to be useful in road safety and many commercial applications. For example, a vehicular network can be used to alert drivers to potential traffic jams, providing increased convenience and efficiency. It can also be used to propagate emergency warning to drivers behind a vehicle (or incident) to avoid multi-car collisions [1]. The Greedy Perimeter Stateless Routing (GPSR) [3] algorithm belongs to the category of position-based routing, where an intermediate node forwards a packet to an immediate neighbor which is geographically closer to the destination node. This approach is called greedy forwarding. For that matter, each node needs to be aware of its own position, the position of its neighbors as well as the position of the destination node. However, GPSR is very well suited for highly dynamic environments such as inter-vehicle communication on highways. It has been discussed that radio obstacles, as they are found in urban areas, have a significant negative impact on the performance of position based routing

This research was supported by Seoul R&BD Program (Project number: CR070019) Corresponding author


Greedy Perimeter Coordinator Routing algorithm (GPCR) [5] is an enhancement of the GPSR protocol. It is also based on the fact that streets and junctions naturally form a planar graph and thus does not require any planarization algorithm. Moreover, GPCR does not need an urban map. An important point is that, since junctions are the only places where routing decisions are made, packet must always be sent to a node that is at a junction. Forwarding a packet across a junction risks to bring GPCR to a local maximum. At junctions, a greedy decision is also made, and the neighboring node which brings the maximum progress towards the destination is chosen. If a local maximum is reached, the recovery mode is used. And in case GpsrJ+ [6], Unlike GPCR, GpsrJ+ only forwards packets to nodes in road junctions if and only if the forwarding decision changes with respect to the general forwarding direction of the recovery mode. Otherwise, packets are allowed to progress across the intersection with the maximum progress, saving the protocol many hops.

The movements of vehicles are generated manually using the Vehicle Movement Editor.

Fig. 1. Road map

Among all vehicles, 6 of them are randomly chosen to send CBR data packet to other vehicle during the move. To evaluate of performance each routing protocol, we are measured by the throughput of sending packet, data sending delay and data traffic overhead. The simulation results are presented in Figures 3, 4 and 5.

3. Performance evaluation in realistic urban environment The vehicle networks on the urban roads have many important factors that influence, such as street layouts and intersections with traffic signs, or inter-vehicle interactions. But the widely used Random-Way point Model assumes that the nodes moving in an open field ignores such factors and without obstructions. Thus it is important to use a realistic mobility model, so that results from the simulation correctly reflect the real world performance of a VANET In our experiments, we use version 2.32 of the ns-2 simulator with the MOVE (MObility model generator for VEhicular networks) to rapidly generate realistic mobility models for VANET simulations [7]. First, we compared the performance of the GPSR (position based routing) and the OLSR (reactive routing) popular routing protocol. The street layout is derived and normalized from a snapshot of a real street map in the Houston area based on Topologically Integrated Geographic Encoding and Referencing (TIGER) database [8] form U.S. Census Bureau. These map data are transformed into the data format that can be used by ns2, based on techniques presented in [9]. In our simulation, around 39 vehicles are involved in the simulation with more than 3 ’ 000 recorded vehicles position changes in an area of around 2164m x 2195m.(Fig. 1)

Fig. 3. Throughput of sending packet

Fig. 4. Average End to End delay


Figure 3 shows the change of throughput of sending packet. GPSR protocol is throughput of sending packet bigger than OLSR protocol. It is because GPSR periodically update their neighbor and sink node table about geographical information. But Average End to End delay of GPSR protocol is much more reduced than OLSR protocol. (Fig. 4)

Fig. 5. The number of packets generated

Figure 5 shows the generated packet overhead as a function of the data generated event time. As the generated event time increases, the number of packets generated by all protocol also increases. However, the increasing trend is different. The overhead of GPSR routing increases much faster than OLSR protocols due to the redundant packets generated.

4. Routing Design of Intersection area In real urban road environment, there are many road intersections. To communicate with other vehicles, in many case data packet passes intersection area. The located of intersection affect the throughput of sending packet and data traffic overhead. Because at intersection passed many vehicle which change rapidly.

Fig. 6. Intersection Area in Urban Environment

Considering the road situation on Figure 6, Source’s vehicle (S) wants to send a packet to Destination’s vehicle (D). If using GPSR algorithm, sends the packet to Section 1 direction. And packet traversing

the

cycle

is

(S→section

1→section

3→section 4→section 5→D). But it is not a optimal path for packet delivery. So we design routing protocol using control node, like light controller, in urban area which has many intersection. Our routing protocol can have of a combination of both control node at intersection and mobile nodes on the road. The Control nodes tend to have a stabilizing influence on topology and routing by relaying the packets to/from the neighbor nodes and location of last control node for data forwarding to destination's node. On the other hand, mobile nodes add entropy to the system by causing frequent route setups, teardowns, and packet losses. We assume that every mobile node equipped with preloaded digital map and they know its location by through navigation system. And every control node, like light controller, are connected by wired network and they can find their neighboring mobile nodes through beacon messages using short range wireless channel. And control nodes communicated with each other about their neighbor mobile nodes information (node id, geographical location) and its geographical location. We list the major steps of route path finding algorithm at each intersections in the area. First, source node (S) searches near two control node and send routing path request data to control node (D). Second, if each control nodes receives the request, they choose control node which nearest from destination. And they calculate fastest routing path through their neighbor control node. And then, third each control node sent data about their calculated path to source node. Last, source node received reply, it compares reply, which suggests more optimal routing path, and forwards data to control node. Figure 7 shows the pseudo-code for the find optimal intersection algorithm to destination node. This algorithm is used when control node received routing path request from source node.


[3] B. Karp and H. T. Kung, “GPSR: Greedy perimeter stateless routing for wireless networks,” in Proceedings of ACM MOBICOM-00, August 2000. [4] M.Fiore, J.Hἅrri, F.Filali, and C.Bonnet, "Vehicular mobility simulation for vanets", in Proceedings of the 40th IEEE Annual Simulation Symposium(ANSS-40 2007), 2007 [5] C. Lochert, M. Mauve, H.Fusler and H. Hartenstein, “Geographic routing in city scenarios”, ACM Sigmobile mobile computing and communication review, 2005 [6] Kevin C. Lee, Jerome Harri, Uichin Lee, and Mario Gerla, “Enhanced Perimeter Routing for Geographic Forwarding Protocols in Urban Vehicular Scenarios,” In Fig. 7. Find optimal intersection algorithm

Proc. of AutoNet (in conjunction with IEEE Globecom’07), Washington, DC., Nov. 2007

5. Conclusion GPSR is very well suited for highly dynamic environments such as inter-vehicle communication on highways. It has been discussed that radio obstacles, as they are found in urban areas, have a significant negative impact on the performance of position based routing. To express out point, we evaluate the performance of OLSR and GPSR with realistic urban scenarios using MOVE(MObility model generator for VEhicular networks). And we conclude GPSR protocol is throughput of sending packet bigger than OLSR protocol. It is because GPSR periodically update their neighbor and sink node table about geographical information. In addition, we presented a new position-based routing approach which is able to deal with the challenges of urban road environments. In real urban road environment, there are many road intersections. Our routing protocol can have a combination of both control node at intersection, like light controller and mobile nodes on the road.

6. References [1] J. Zhao and G. Cao, "VADD: Vehicle-Assisted Data Delivery in Vehicular Ad Hoc Networks," in InfoCom 2006. [2] V. Gradinescu, C. Gorgorin, R. Diaconescu, V. Cristea, and L. Iftode, “Adaptive traffic lights using car-to-car communication,” in IEEE Vehicular Technology Conference, (Dublin, Ireland), April 2007.

[7] F. Karnadi, Z. Mo, K. Lan “Rapid Generation of Realistic Mobility. Models for VANET,” WCNC’07. [8] “Tiger, tiger/line and tiger-related products,” U.S. Census Bureau. [9] A. K. Saha and D. B. Johnson, “Modeling mobility for vehicular ad hoc networks,” in Poster in VANET04, Philadelphia, PA, October 2004

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Movement Awareness position-based routing protocol for Intersection of Urban Environment - UPWN

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