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ELRT: improved packet delivery ratio using enhanced link residual time in VANET.


Vehicular ad hoc network is the vital research area in the wireless network for next decade. Research in VANET is mainly focused on traffic information, infotainment security and internet services to the vehicles. VANET provides communication between vehicles in two ways. There are Vehicle-to- Vehicle (V2V) and Vehicle-to-Infrastructure (V2I).

DSRC is the communication device used in VANET. The DSRC model has Road Side Unit (RSU) and On Board Unit (OBU). The distance of DSRC is up to the 1000 m and spectrum is from 5.85 to 5.925 GHz and another model for WAVE (Wireless Access for Vehicular Environment) adopts IEEE standard 1609.

Analysis of routing protocol for VANET has demonstrated that VANET's performance is very poor. Indeed, the movement of nodes in the vehicular network has highly dynamic in topology and mobility. The main problem of the VANET routing is their route instability. It clearly indicates that existing routing protocol does not satisfy when it comes to performance in the network.

The aforementioned characteristics of VANET, results in frequently broken routing paths. To minimize the short-live routing path in vehicular communication, We have to calculate the link residual time (LRT) between two nodes in the routing path. The selection of next hop depends on the maximum value of LRT which leads to the long life of the routing path.

The LRT is mostly based on speed, distance and signal strength. The speed is one of the parameters that can be calculated by the mean value theorem. The next parameter is the distance calculated using longitude and latitude data. These two values can be obtained from Global Position System (GPS). The next parameter is signal strength. It generally decreases as the vehicle moves further from the adjacent hop vehicle. When the distance between two vehicles is closer, the signal strength is increased. The signal strength is generally depends on the transmitting and receiving power of the dedicated short range communication device.

Related work:

There is a lot of work being undertaken in the study of routing in an vehicular ad-hoc network. In VANET environment, routing is based on the link residual time (LRT), speed and distance. We already studied and analyzed on LRT. We have analyzed three type of LRT based on their vehicular environment. The three types are

Position based LRT:

Position based routing supports V2V communication. The LRT calculation for this routing depends on position, speed and distance. The position of the vehicle is deployed on the graph plane.

Prediction based LRT:

Prediction based LRT scheme supports, V2I communication. In VANET we use road side units installed at function points. Here the road side unit is also called anchor unit. This LRT method is calculated using speed and distance.

Cross layer based LRT:

Cross layer based LRT supports V2V communication. This LRT is calculated through the 'physical layer information'. The information in transmission power and receiving power will be in the form of SNR. The cross layer design concept is shown in CLWPR, and it is given below.

There are few specialized routing protocols in the vehicular ad hoc network environment. The cross layer, weighted, position based routing (CLWPR) is one which is supported in this environment. This routing protocol supports the concept of link expiry time. It uses the distance on the road as a metric instead of the actual geographic distance. It keeps the MAC and PHY layer information that is used to construct the LRT. The Distance calculation of VANET has been used in various methods. Commonly, the Cartesian co-ordinate based distance calculation method that uses the X & Y coordinates of a vehicle position is used in V2V communication. In V2I communication, the distance in-between the road side unit and the vehicle is calculated using the Pythagorean theorem. In the calculation of distance in both V2V and V2I, the position of the vehicle is obtained from the GPS system. The proposed system implements the ELRT based on LRT.


Routing path in VANET is not stable, because the vehicular movement is highly variable. Due to this reason, the link will be prone to failure and as a result the routing path will also fail frequently. To resolve this problem, the ELRT is to be used to find the next hop node in the route using which we improve the link quality in the routing path. This reduction in Link failure in routing will improve the packet delivery ratio and decrease the End-to-End delay in VANET.

ELRT is used to find the next hop node that retrieved the neighbor nodes of the current active node. In figure, the attribute of nodes depicts the speed, distance and signal strength. The retrieval of nodes is based on parameters of distance and speed. After retrieving the neighbor nodes we calculate the link residual time for all the retrieved nodes using signal strength. The maximum LRT of a node as specified in the next hop node of routing path. The retrieval process is shown in Fig(1).

A. Distance Calculation:

Distance Calculation of moving vehicles uses the Global Position System (GPS). The GPS system provides the location information of vehicles such as latitude and longitude values. Using these values the exact distance is calculated in VANET.

ELRT algorithm retrieves the neighbor nodes based on distance using DSRC. DSRC has the range of 1000 m. During the retrieval of nodes in this scheme if the distance value is small, then the mobility of nodes decreases and link stability increases. The Distance Calculation is specified in Algorithm (1).

Algorithm 1: Distance calculation:

Begin Distance
<precondition> latitude and longitude values of Vehicles.
<post condition> Distance between            Vehicles.
    x=difference of latitude1 and latitude2
    y=difference of longtitude1 and longtitude2
    a=centreangle (x,y,latitude1,latitude2)
     //R is Redius of earth
return d
End Distance.

This algorithm describes a, c and d. The centre-angle function is calculated and assigned a value in variable 'a'. It uses a haversine formula that gives 'greater circle distance' in earth. The 'greater circle distance' measures the distance between two points on earth.

B. Speed Calculation:

In vehicular environment, the vehicles are moving at various speeds. The change in vehicle speed changes the topology of network structure. When the differences of speed levels in vehicles are minimum and the change of network topology is reduced. Vehicle speed is varying every second thereby complicating the calculation of the correct speed of the target vehicle.

Using Mean Value Theorem (MVT), the vehicle speed is to be calculated. Using the parameters of distance and time, the distance travelled by a vehicle is d. The time between vehicle movements is [t.sub.1] and [t.sub.2].

Speed = [d([t.sub.2]) - d([t.sub.1])]/[t.sub.2] - [t.sub.1] (1)

ELRT uses speed as a second parameter to retrieve the neighboring nodes in VANET. In VANET routing, to maintain link stability we choose to calculate the speed range from 10 to 50 KM pH, as VANET is typically used in urban scenarios and Urban scenarios restrict vehicle speeds to between 10-50 KM pH.

C. Link residual Time:

The LRT predicts the duration of communication between two vehicles. The value of LRT is based on distance and signal-to-noise ratio (SNR). The distance is calculated using above algorithm. Here, the LRT is constructed through physical layer information it has in the form of transmission power and received power.



[SNR.sub.0] = received signal and noise ratio,

[] = threshold value of a signal to noise ration

d = distance

[gamma] = path loss

D. ELRT Algorithm:

In this algorithm, we calculate the Link residual time for selected nodes based on distance, speed and signal strength. We filter neighboring nodes using three constraints. The first one is to check if [T.sub.distance] is within Destination distance. If it is true then the packet is directly transferred to destination node. The second constraint is to check the distance for the entire set of neighboring nodes which are in- between [T.sub.distance] and [T.sub.mdistance], then set the flag value as true. After that we check the third constraint, which is to check the speed just like the second constraint. Both constraints are processed then only we can pass through to calculate the LRT value. Here, we don't calculate the LRT for all neighbor nodes but only for subset of the neighbor nodes. This reduces the unwanted processing time, since time is precious in the VANET environment.

Performance Analysis:

We implemented the ELRT protocol under ns3 and compared it with CLWPR and GPSR. We measured several improvements in significant metrics for VANET. For realism, we used the tool SUMO[TM]. This tool is implemented because of its realistic portrayal of vehicular mobility. Our objective is to evaluate ELRT with respect to an increasing dynamic network. The choice of focus is to increase speed. If we increase the speed of the vehicle, then it changes topology very frequently, and we also get effective result from the simulation to enhance the link residual time.

Algorithm 2: ELRT:

Begin ELRT
<post_condition>Link Residual Time
         deliver a packet
                          if(nx1==1 &&nx==1)
                                  Calculate LRT;
end ELRT

Using this link residual time for the next hop nodes we calculate the routing life time in VANET. Routing life time is the summation of relay nodes LRT.

RLT = [n.summation over (i=1)] [LRT.sub.i] (3)

ELRT algorithm focuses on the filtration of nodes in the next hop. This is to reduce the packets to send the next hop.(i.e) It reduces the calculation of LRT for each and every node present in the next hop set. To minimize the next hop set, we filter the nodes with distance, speed and signal strength. For simulating the mobility of vehicles, we used up to 250 vehicles; figure (2) shows the network topology. Nodes are placed over three junction points each with two parallel roads with alternative driving directions. All parameters of ELRT are mentioned in the table (1)

Fig(3) shows the graph ELRT is comparatively better than CLWPR and GPSR. Initially, it seems CLWPR is a better approach but ELRT increased is performance consistently over time. ELRT achieves a Packet delivery ratio similar for smaller radio ranges and speed, while a significant improvement compared with CLWPR and GPSR with larger radio range and high speed as shown in fig(3). ELRT have the awareness of radio range(distance) and speed with long connectivity among the nodes.

Fig (4) depicts a graph in which the End-to-End delay of ELRT is decreased. The performance of ELRT in End-to-End delay is more or less same as CLWPR.


In this work we have enhanced the Link Residual Time in VANET. We proposed a new approach on routing in an urban--environment that takes advantage and improve the performance of the packet delivery ratio. We also simulate ELRT and compare it with GPSR and CLWPR. We found that ELRT achieves better performance and at the same time satisfies us on the End-to-End delay. Similarly, we will pursue the implementation of stable link connection in a vehicular ad hoc network.


Article history:

Received 3 September 2014

Received in revised form 30 October 2014

Accepted 4 November 2014


Su, W., S.J. Lee, M. Gerla, 2001."Mobility prediction and routing in ad hoc wireless network", International journal of Network management, 3-30.

Jing Zhu, St. Louis, M.O. USA, S. Roy, 2003. MAC for dedicated short range communications in intelligent transport system," IEEE Communication Magazine, 41(12): 60-67.

Weidong Xiang, Richardson, P. Jinhua Guo, 2006," Introduction and Preliminary Experimental Results of Wireless Access for Vehicular Environments (WAVE) Systems," in Proc. 3rd Annual International Conference on Mobile and Ubiquitous Systems--Workshops, 1-8.

Vinod Namboodir, Lixin, 2007. "Gao Prediction-Based Routing for Vehicular Ad Hoc Networks", IEEE Transaction on Vehicular Technology, 56(4): 2332-2345.

Nikoletta Sofra, Kin, K. Leung, 2008. "Estimation of Link Quality and Residual Time in Vehicular Ad-Hoc Networks," in Proc. IEEE Wireless Communications and Networking Conference, 2444-2449.

Yuandong Sun, Yongquan Chen, Yangsheng Xu, A GPS enhanced routing protocol for vehicular Ad-hoc network," IEEE International Conference on Robotics and Biomimetics (ROBIO), 2096-2101.

Jung-Hun Kim, SuKyoung Lee, 2011. "Reliable routing protocol for Vehicular Ad Hoc Networks," International Journal of Electronics and Communications, 268-271.

Kaveh Shafiee, Victor, C.M. Leung, 2011."Connecting-aware minimum-delay geographic routing with vehicle tracking in VANETs," Ad Hoc Networks, 131-141.

Nikoletta Sofra, Athanasios Gkelias, kin K. Leung, 2011. "Route Construction for Long Lifetime in VANETs", IEEE Transaction on vehicular Technology, 60: 3450-3461.

Konstantinos Katsaros, Mehrdad Dianati, Rahim Tafazolli, Ralf Kernchen, 2011." CLWPR--A Novel Cross-Layer Optimized Position Based Routing Protocol for VANETs," in Proc. IEEE Vehicular Networking Conference, 200-207.

Sofra, N., A. Gkelias, K.K. Leung, 2011, "Route Construction for Long Lifetime in VANETs," IEEE Transactions on Vehicular Technology, 60(7): 3450-3461.

Nordin N.A.M., Z.A. Zaharudin, M.A. Maasar, N.A. Nordin 2012. "Finding shortest path of the ambulance routing: Interface of A*algorithm using C# programming", IEEE Symposium on Humanities, Science and Engineering Research (SHUSER), 1569-1573.

Hu Lili, Ding Zhizhong, Shi Huijing, 2012. "An Improved GPSR Routing Strategy in VANET," in Proc 8th International Conference on Wireless Communications, Networking and Mobile Computing (WiCOM), 1-4.

Mohammad Reza Jabbarpour, Hossein Malakooti, Masumeh Taheri, Rafidah Md Noor, 2013. "The Comparative Analysis of Velocity and Density in VANET Using Prediction-Based Intelligent Routing Algorithms," Second International Conference on Future Generation Communication Technology (FGCT), 54-58.

Karthikeyan, L., S. Selavakumar, S. Balasubramani, 2014. "Analysis of Link Expire Time in Vehicular AdHoc Networks," National Conference Proceeding.

(1) Karthikeyan L, (2) Selvakumar S and (3) Balasubramani S

(1) Assistant Professor Department of Computer Science, Valliammai Engineering College, Chennai, India-603203

(2) Professor & Head, Department of Computer Science, GKM college of engineering and Technology, Chennai, India-600063

(3) PG Scholar, Department of Computer Science, Valliammai Engineering College, Chennai, India-603203

Corresponding Author: Balasubramani S, PG Scholar, Valliammai Engineering College, Chennai, India-603203

Tel: 09159266313; E-mail:

Table 1: Simulation Parametres.

Parameter               Value

Simulation time      360 seconds
Number of Vehicles       250
Simulation Area      3000 * 2500 m
Range                500 meters
Vehicle Speed        0-50 km/hr
Data packet Size      512 bytes
MAC Protocol          802.11 p
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Title Annotation:Enhanced Link Residual Time and Vehicular Ad hoc Network
Author:Karthikeyan, L.; Selvakumar, S.; Balasubramani, S.
Publication:Advances in Natural and Applied Sciences
Article Type:Report
Date:Sep 15, 2014
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