2012 International Conference on Information and Computer Networks (ICICN 2012) IPCSIT vol. 27 (2012) (2012) IACSIT Press, Singapore Performance Comparison of MANETs Routing Protocols for Dense and Sparse Topology Mohammed A. Mahdi 1,a + and Tat-Chee Wan 1,2,b 1 School of Computer Sciences, Universiti Sains Malaysia 11800 USM, Penang, Malaysia 2 National Advanced IPV6 (NAV6) Centre, Universiti Sains Malaysia 11800 USM, Penang, Malaysia a wsabi3@yahoo.com, b tcwan@cs.usm.my Abstract. Mobile Ad Hoc Network (MANET) is a group of wireless mobile nodes dynamically forming a network without any pre-existing infrastructure. This paper looks into Mobile Ad-Hoc Networks (MANETs) environment that has varying node densities, called Dense and Sparse node density. It focuses on how the three reactive routing protocols, Cluster Based Routing Protocol (CBRP), Ad hoc On Demand Distance Vector (AODV) and Dynamic Source Routing (DSR) react to MANET environments with varying node densities (Dense and Sparse) topology. Network Simulation (NS2) was used to evaluate the performance of these protocols. The experiment results show that: CBRP protocol outperforms AODV and DSR in term of normalized routing load in both Dense and Sparse topology when traffic sources exceed 20 sources and AODV outperforms CBRP and DSR in term of Delay for all traffic sources. Keywords: MANET, Routing Protocols, Node Density, CBRP, AODV, DSR. 1. Introduction A Mobile Ad hoc Network (MANET) is a collection of wireless mobile nodes (or routers) dynamically forming a temporary network without the use of any existing network infrastructure or centralized administration. MANETs can be used in classrooms, battlefields and disaster recovery. Routing protocols play the main role for any communication in a network where routing protocol is used to correct and efficient route establishment between a pair of nodes in the network so a message can be delivered in a timely manner [1]. MANETs routing protocol can be classified to three different categorized: proactive, on-demand or reactive and hybrid. In proactive protocols, the routes to all the destination (or parts of the network) are determined at the start up, and maintained by using a periodic route update process. In reactive protocols, routes are determined when they are required by the source using a route discovery process. Hybrid protocols combine the basic properties of the first two classes of protocols into one [2]. This paper focuses on CBRP routing protocol and its comparative analysis with two other protocols (AODV and DSR). 2. Overview of MANET Routing Protocols Routing protocols for MANETs are MANETs classified according to the strategies of discovering and maintaining routes into three classes: proactive, reactive, and hybrid [2]. Each routing protocol reacts differently to node mobility and density. This section explains the three routing protocols (CBRP, AODV and DSR) which we used it in our study. 2.1 Dynamic Source Routing (DSR) [3] is an on-demand routing protocol that is based on the concept of source routing. DSR is a simple and efficient routing protocol designed specifically for use in multi-hop wireless ad hoc networks of mobile nodes. DSR is consisted of two mechanisms: Route Discovery and Route Maintenance, that work together to allow nodes to discover and maintain source routes to arbitrary + Corresponding author: E-mail address: (wsabi3@yahoo.com). 37
destinations in the MANETs. DSR computes the routes when necessary and then maintains them. DSR applies on demand schemes for both route discovery and route maintenance. This makes the routing overhead traffic scales to the actual needed size automatically, which is considered as the main advantage of DSR. 2.2 Ad hoc On-demand Distance Vector (AODV) [4] is an on-demand routing protocol that is enabled dynamic, self-starting, multi-hop routing between mobile nodes wishing to establish and maintain a MANETs. AODV is essentially a combination of both DSR and DSDV (Destination Sequenced Distance Vector) protocol. AODV uses the basic on-demand mechanism of Route Discovery and Route Maintenance as in DSR. Also, AODV uses the next hop routing model with sequence numbers and periodic beacons to discover routes and maintain them from DSDV protocol. AODV uses sequence numbers to avoid long-term loops when the ad hoc network topology changes. AODV allows mobile nodes to obtain routes quickly for new destinations, and does not require nodes to maintain routes to destinations that are not in active communication. 2.3 Cluster-Based Routing Protocol (CBRP) [5] is a hierarchical on-demand routing protocol that uses source routing, similar to DSR, to avoid forming loops and route packets. CBRP groups the nodes in a network into several clusters. Each cluster has a cluster head which coordinates data transmission within the cluster and with other clusters. The advantage of CBRP is that only cluster heads exchange routing information, therefore the number of control overhead transmitted through the network is far less than the traditional flooding methods. As a summary, the CBRP has the following features [5]: Fully distributed operation. Less flooding traffic during the dynamic route discovery process. Explicit exploitation of uni-directional links that would otherwise be unused. Broken routes could be repaired locally without rediscovery. Sub-optimal routes could be shortened as they are used. 3. Node Density The optimum density of MANET was studied in [6] which discussed the tradeoffs between network density and node connectivity in the face of increasing node mobility, and proposed a search for an optimal node density value for maintaining connectivity in a stationary network. The relationship of the node density in MANET should to be considered the extent of the nodes transmission range covering the network area. Including the transmission range coverage of the nodes will provide a better estimation for node density and it helps identify how well the network is connected. In this paper network density is defined as Dense when large number nodes are closeness of one another within a specific area and vice versa for Sparse. However, when determining density for a specific network, the connectivity of the network in terms of transmission range that covers the specific area should also to be considered. In [7] Connectivity density was studied and discussed determining the network connectivity that based on the density of the numbers of neighboring nodes. P is the probability of the connectivity. The value n is the number of nodes located in the area. The value µ is represented by Eq. 2 where ρ is the density, π represents the circumference and r is the radius of the transmission: P (k-con) (1-e µ)n (1) µ = ρ π r02 (2) ρ = n/a (3) Based on this one can have the criteria for determining the size of each square in the topology. In this study the value of k is set to 1. This means that in any particular network mentioned as Dense given the probability of the connection of P(k-con) 0.95 where k = 1, there is 1 mutually independent path connecting the nodes in the particular network area. Thus the network is categorized as (almost surely) 1-connected when P (1- con)>= 0.95 based on the definition in [7]. 3.1. Sparse and Dense definition The calculation on the degree of node density of the network areas in this paper will be based on the formula provided for P (1-con) in Eq. 1. Based on P (1-con) two types of node density are identified, Dense and Sparse. These two types of node density are defined as follows: The node density of MANET is considered to be Dense is based on the following conditions: 38
It has at least one mutually exclusive path to other nodes in the same area that is independent of one another. P (1-con) 0.95. The node density of MANET is considered to be Sparse is based on the following conditions: Nodes neighborhood cannot guarantee at least a single connection in the network. P (1-con) < 0.95. The minimal neighbor node degree for Sparse areas could be d min = 1. 4. Simulation Experiments The simulation environment is based on the NS-2 network simulator version 2.34[8], a widely simulator used was used in our experiments. The IEEE 802.11 DCF (Distributed Coordinated Function) MAC was used as the basis for the experiments with a channel capacity of 2Mb/sec. The transmission range of each node was set to 250 m using the Two-Ray Ground Propagation model. 4.1. MANET Simulated Node Density Topology Configuration The node density for simulation is configured based on the degree of node density defined in (Eq. 1). Two types of topologies were studied in this paper, Dense and Sparse topology. Based on the number of nodes which are 50 nodes and transmission range which is 250m and topology (1000x1000), the connection probability of P(1-con) will be greater than 0.95 that means Dense topology. Also, based on the same transimission range and number of nodes and topology (1500x1500), the P(1-con) will be less than 0.95 that means is Sparse topology. 4.2. Mobility Model The mobility model uses the Random Waypoint model. Two field configurations are used: Dense area with topology (1000x1000) m and Sparse area with topology (1000x1000) m, all with 50 nodes. The nodes are moving with 0 pause time and varying speeds (1, 2, 4, 6, 8, 10, 12 and 15) m/s in the two topologies Dense and Sparse. The total simulation time is 500 seconds. 4.3. Traffic Model The traffic pattern which used for all the experiments in this paper was a constant bit rate (CBR) data source running on top of UDP. The data packet size was 128 bytes. The data transmission rate was 4 packets per second. The numbers of traffic sources were set to 10, 30 and. 4.4. Performance Evaluations A routing protocol for MANETs is usually evaluated in terms of performance metrics. These metrics are Packet Delivery Ratio (PDR), Average end-to-end Delay (Delay) and Normalized Routing Load (NRL). We used these metrics to measure the efficiency of CBRP, AODV and DSR protocols. A brief description for these metrics is as follows: Packet Delivery Ratio (PDR): The ratio of number of data packets sent from the source to the number of data packets received at the destination. Average end-to-end Delay (Delay): The average time from the beginning of a packet transmission (including route acquisition delay) at a source node until packet delivery to a destination. Normalized Routing Load (NRL): The ratio of number control packets sent from the source to the number of data packets received at the destination. 5. Analysis Results and Discussions The analysis and discussion for simulation is discussed in this section. The results are shown in form of graphs. Graphs show comparison among the three protocols (CBRP, AODV and DSR) in Dense and Sparse topology with performance metrics and different numbers of traffic sources. 5.1. Dense Topology The figures from 1 to 3 represent the performance metrics (NRL, PDR and Delay) for CBRP, AODV and DSR routing protocols for 50 nodes-dense topology (1000x1000) with (10, 30 and 40) traffic sources. Fig. 1 shows that NRL in CBRP protocol is lower than other protocols (AODV and DSR) with 30 and and it is the highest with. Fig. 2 shows that CBRP has better PDR with 30 and and DSR 39
has the lowest PDR. Fig. 3 shows that AODV has lower Delay with all traffic sources (10, 30 and 40) and DSR has the highest delay with 30 and. Fig. 1: Normalized Routing Load for 50 nodes with various numbers of sources Fig. 2: Packet Delivery Ratio for 50 nodes with various numbers of sources Fig. 3: Average End to End Delay for 50 nodes with various numbers of sources 5.2. Sparse Topology The figures from 4 to 6 show the performance metrics (NRL, PDR and Delay) for CBRP, AODV and DSR protocols for 50 nodes-sparse topology (1500x1500) and traffic sources (10, 30 and 40). Fig. 4 shows that NRL in CBRP protocol is lower than other protocols (AODV and DSR) with 30 and and it is the highest with. Fig. 5 shows that AODV performs well in term of PDR with 30 and, but with slight difference with CBRP. DSR has the least PDR with 30 and. Fig. 6 shows that AODV performs better in term of Delay with all traffic sources (10, 30 and 40) and DSR has the highest delay with 30 and. Fig. 4: Normalized Routing Load for 50 nodes with various numbers of sources 40
Fig. 5: Packet Delivery Ratio for 50 nodes with various numbers of sources Fig. 6: Average End to End Delay for 50 nodes with various numbers of sources 6. Conclusion This paper compared the performance of CBRP, AODV and DSR MANETs routing protocols in both Dense and Sparse topology. The experiment results show that: In Dense topology (1000x1000) m, CBRP protocol outperforms AODV and DSR in terms PDR and NRL when traffic sources exceed 20 sources and AODV outperforms CBRP and DSR in term of Delay, where it has the lowest delay. In Sparse topology (1500x1500) m, CBRP outperforms AODV and DSR in term of NRL when traffic sources exceed 20 sources and AODV protocol outperforms CBRP and DSR in term of PDR with traffic sources (30 and 40). Also, AODV outperforms CBRP and DSR in term of Delay, where it has the lowest delay for all traffic sources (10, 30 and 40). It can conclude that: the performance of these three protocols is decreased when node speed increases in both Dense and Sparse topology. For further research we plan to investigate and study the performance efficient for CBRP, AODV and DSR protocols in MANETs with non-uniform node density. 7. References [1] E. M. Royer and C.-K. Toh. A review of current routing protocols for ad-hoc mobile wireless networks. IEEE Personal Communications, April 1999, pp. 40 45. [2] M. Abolhasan, T. Wysocki, and E. Dutkiewicz, A Review of Routing Protocols for Mobile Ad Hoc Networks, Elsevier Ad Hoc Networks Journal, Vol. 2, No. 1, pp. 1-22, January 2004. [3] Broch J., D. Johnson and D. Maltz, 1999, The Dynamic Source Routing Protocol for Mobile Ad Hoc Networks, http://www.ietf.org/internet-drafts/draft-ietf-manet-dsr-03.txt, IETF Internet draft, Oct., 1999. [4] Perkins, C., E. Belding-Royer and S. Das, 2003. Ad hoc On-Demand Distance Vector (AODV) Routing, RFC 3561. [5] M. Jiang, J. Li and Y.C. Tay, Cluster Based Routing Protocol (CBRP), IETF Internet Draft draft-ietf-manet-cbrpspec-01.txt, August 1999. [6] E. Royer, P. Melliar-Smith, L. Moser. An Analysis of the Optimum Node Density for Ad Hoc Mobile Networks. In Proceedings of the IEEE International Conference on communications, Helsinki, Finland, 2001. [7] C. Bettstetter. On the Connectivity of Wireless Multihop Networks with Homogeneous and Inhomogeneous Range Assignment. In Proceedings. VTC 2002-Fall.2002 IEEE 56th Volume 3, 24-28 Sept. 2002. [8] NS2 Network Simulator 2. http://www.isi.edu/nsnam/ns/. 41