# Traffic Based Analysis of Efficient & Dynamic Probabilistic Broadcasting Algorithm in MANETs Routing Protocols

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4 To select the value of probability p, the average neighbor number is analyzed. Let X be the area of the adhoc network, N is the total no. of mobile nodes in the network and R is the radio transmission range then, average number of nodes ( n bar) can be computed from the formula n bar = (n-1)(p)(πr 2 )/X. The value of fixed probability that used in AODV-FP is set at p= 0.8. It has been shown that this probability value enable fixed probabilistic flooding to achieve a good performance. The brief outline of the proposed algorithm is shown in figure1. The rebroadcast probability is set high at the hosts in sparser areas where number of neighbor nodes is less and low at the hosts in denser areas where the number of nodes is high. This scheme for density based rebroadcasting implementation analysis requires mobile hosts which periodically exchange HELLO messages between neighbors to and construct a one hop neighbor list in routing table at each host. A high number of neighbours imply that the host is in a dense area and a low number of neighbors imply that the host is situated in a sparser area. On hearing a broadcast packet m at node A Get the broadcast ID from the message: n bar, average number of neighbor(threshold) Get degree n of node X (no. of neighbors of node X) If packet m received for the first time then If n<n bar then Node X has a low degree Set high rebroadcast probability p=p1; Else n>=n bar Node A has a high degree Set low rebroadcast probability p=p2; Endif Endif Generate a random number RN over [0,1] If RN <= p then rebroadcast the received message; else drop the message endif 4. SIMULATION MODEL Figure 1. EDPB algorithm GloMoSim is used as the simulation tool. Performance evaluation of the protocols is done on parameters such as routing overhead and end-to-end delay in different network density, normalized routing load, etc. The simulation, different mobile nodes are moving in different network area. Each node has 250 meter radio transmission range. The node mobility model used is random-waypoint model [21]. In which each node at the beginning of the simulation remains stationary for a pause time, then chooses a random destination and starts moving towards it with a speed selected from a uniform distribution range between 0 to maximum speed. The speed varies from 5 to 20 m/sec. The simulation parameters used, are as per Table 1. Table1. Simulation Parameters Parameter Value Pause time 0,10,20,40,600 sec Number of nodes 25,50,75,100 Traffic load 5,10,15,20,25 connections Mobility model Random waypoint model 111

5 No. of trials 30 Data Traffic CBR Transmission range 250 meters Bandwidth 2Mbps Simulation time 1100 secs Packet size 512 bytes Topology size 800x800 m2 Node speed 5,10,15,20, m/sec 5. PERFORMANCE METRICS AND RESULTS ANALYSIS AODV is already implemented in GloMoSim simulator and analysis is done and the results are obtained for AODV in which traditional blind flooding scheme of route discovery operation is followed and implemented for route rebroadcast request packets. Now, the aim is to reduce the flooding of route request packets during the rout discovery process, and as a result it reduces the broadcast storm problem and net effect is that overall network is improved by the reduced average end-to-end delay and also routing overhead. Traffic Load Analysis: The performance of three protocols in terms of routing overhead vs. offered traffic load is shown below in figure 2. It is found that at a very heavy traffic load, the normalized routing load is reduced to 35% to 40% as compared with AODV blind flooding and AODV fixed probability scheme, at a very heavy traffic load as depicted in below mentioned figure no.3, when used with AODV-EDPB. The delays for different traffic loads is also shown in below mentined figure no.3. The packet transmission load has a direct impact on latency and if the number of packets is high, than the number of collisions is high, and in turn leads to more retransmissions. As a result, packet experiences more latency and because of higher number of duplicate route request packets of rebroadcasts, AODV-EDPB experiences lower latencies than AODV-Blind Flooding and AODV-Fixed Probability. Routing overhead ( RREQ packets) Number of Connections AODV-BF AODV-FP AODV-EDPB Figure 2: Routing Overhead vs. Traffic load (no. of Connections) 112

6 Normalized routing load Number of Connections AODV-BF AODV-FP AODV-EDPB Figure 3. Routing load vs. Traffic load (no. of connections) delay (seconds) Number of Connections AODV-BF AODV-FP AODV-EDPB Figure 4. Delay vs. Traffic load (No. of Connections) Network pause time load analysis: The results in figure 5 show the routing overhead generated by three protocols when the pause time is increased from 10 to 100 sec. Routing overheads ( RREQ packets) Pause time ( seconds) AODV-BF AODV-FP AODV-EDPB Figure 5. Routing overhead vs. pause time. 113

7 The normalized routing load generated by all the three protocols with different network pause times and the number of constant bit rate is set at 30 as shown in figure 6. The results reveal that when the network pause time is increased, the mobility of nodes is decreased which leads to decrease in normalized routing load. It shows that AODV-EDPB has better performance over the other two protocols. The delays incurred by all the three protocols for different network pause time as shown in figure 7. The longer the average pause time is, the less is the node movement within the network, and this means that the nodes look like fixed rather than mobile, so the number of generated RREQ packets will be low at network with high pause time. Normalised routing load Pause time ( seconds) AODV-BF AODV-FP AODV-EDPB Figure 6. Routing load vs. pause time. Delay ( in seconds) Pause time ( seconds) AODV-BF AODV-FP AODV-EDPB Figure 7. Delay vs. pause time. 6. CONCLUSION The simulation results show that new proposed algorithm i.e. EDPB algorithm has definitely superior performance over traditional AODV Blind flooding and AODV fixed probability versions of routing protocols and generates much lower routing overhead and end-to-end delay, as a consequence, the packet collisions and contention in the network is reduced. The proposed 114

9 14. Qi Zhang and Dharma P. Agrawal, Analysis of Leveled probabilistic Routing in Mobile Adhoc Networks, OBR Center for distributed and Mobile Computing, ECECS, Uniy Cincinnati incinnati, OH J. Cartigny, D. Simplot. Border node retransmission based probabilistic broadcast protocols in ad-hoc networks, Telecommunication Systems, vol. 22, no 1 4, pp. 204, Y.-C. Tseng, S.-Y. Ni, E.-Y. Shih, Adaptive approaches to relieving broadcast vol.storm in a wireless multihop mobile Adhoc network,ieee Transactions on Computers, 52, no 5, M.B.yassin, M.Ould Khaoua, L.M.Mackenzie et. al Improving Route Discovery in on demand routing protocol using Local Topology Information in MANETs ACM M. D. Colagrosso, "Intelligent broadcasting in mobile Adhoc networks: Three classes of adaptive protocols," EURASIP Journal on Wireless Communication and Networking, vol. 2007, pp. 16, Perkins C.E. Ad Hoc networking, Adison Wesley, ISBN , C-K. Toh, Ad Hoc Mobile Wireless Networks: Protocols and Systems (Prentice-Hall, New York, 2002). 21. C. Bettstetter, H. Hartenstein and P. Costa: Stochastic properties of the random-waypoint mobility model, Wireless Networks, Vol.10, no. 5, pp (2004). Authors Deepak Dembla is currently serving as Associate Professor & Head of Deptt. of Computer Science at AIET, Jaipur in INDIA. He received the degree of M.Tech.(IT) from Punjabi University, Patiala in the year Presently he is pursuing the degree of Ph.D. from Guru Jambheshwar University of Science & Technology, Hisar,Haryana,India. He has 15 national and international publications to his credit in reputed journals, conferences and seminars. His area of research and specialization is computer networks, mobile adhoc networks and sensor networks. Yogesh Chaba received the B.E. degree in Computer Science and Engineering from Marathwada University, Aurangabad with Distinction in year He obtained his MS degree in Software Systems from BITS Pilani and PhD degree in Computer Science and Engineering from Guru Jambheshwar University of Science and Technology, Hisar respectively. He worked as Chairman,Department of Computer Science and Engineering, Guru Jambheshwar university of Science and Technology, Hisar for three years. He has 14 years of teaching and research experience in field of Computer Science and Engineering. He has 68 national and international publications to his credit in reputed journals and conferences. He is recipient of Young Scientist Award by International Academy of Physical Sciences for research work. He is also Principal Investigator of two projects funded by National agencies in area of Ubiquitous computing and MANET security. His research interests include mobile communication, computer networks and information security. 116

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