AN OPTIMIZED CLUSTER BASED APPROACH FOR MULTI- SOURCE MULTICAST ROUTING PROTOCOL IN MOBILE AD HOC NETWORKS USING OWCA Ramyashree K.S, Arvind S & Shesharao M. Wanjerkhede GNDEC, Bidar-585403, Karnataka E-mail : rsramyashree@gmail.com, scarvi@rediffmail.com, shesharao@gmail.com Abstract - This paper presents mobile ad-hoc networks which has less redundancy in packet delivery and better forwarding efficiency. For this routing, a new optimized based weighted clustering algorithm (OWCA) is designed to optimize the clusters and cluster heads. To prove the proposed routing protocol performance which has been simulated in NS2, the results proved are the bandwidth, control overhead and forwarding efficiency as the function of increasing multicast sources and destinations. To prove this clustering algorithm and proposed approach, results have been simulated in NS2. Keywords - mobile ad-hoc networks, clustering, routing, NS2. I. INTRODUCTION In traditional wireless networks, a base station or access point facilitates all communications between nodes on the network and communications with destinations outside the network. In contrast, MANETs allow the formation of a network without a fixed infrastructure. These networks only require that nodes having interoperable radio hardware and use the same routing protocol to route traffic over the network. The lessened requirements for such networks, along with the ability to implement them using small, resource-limited devices has made them increasingly popular in all types of application areas. For example, MANET-based sensor networks have been proposed to assist in collecting data on the battle field. Multi-cluster, multi-hop packet radio network architecture for wireless systems should be able to dynamically adapt itself with the changing network configurations. Certain nodes, known as cluster heads, are responsible for the formation of clusters (analogous to cells in a cellular network) and maintenance of the topology of the network. The set of cluster heads is known as a dominant set[1][2]. Any node can become a cluster head if it has the necessary functionality such as processing and transmission power. Nodes register with the nearest cluster head and become member of that cluster. Clusters may change dynamically, reflecting the mobility of the underlying network. The rest of the paper is organized as follows. Chapter II has the literature survey, Chapter III has the drawbacks of the existing system. Chapter IV has the proposed work. Chapter V has the advantages of proposed work. Chapter VI has the semantics of OWCA. Chapter VII deals with the experimental setup and results of our proposed approach and Chapter VIII describes the conclusion & future work. II. RELATED WORK In [3] the author have used the genetic algorithm to optimize the WCA and the optimized results are compared with deterministic WCA to prove the reaffiliations, average number of clusters and load balancing factor(lbf). In [4] the author has optimized the WCA with the use of simulated annealing and results are drawn to prove the re-affiliations of average number of clusters by different maximum displacement and number of nodes. In [5] the author has optimized the WCA with the use of particle swarm optimization and results are drawn for maximum displacement Vs average number of clusters as the function of increasing number of nodes. III. DRAWBACKS OF EXISTING WORK The resource allocation is done by a cluster head to all the nodes belonging to its cluster. Due to the dynamic nature of the mobile nodes, their association and dissociation to and from clusters perturb the stability of the network and hence reconfiguration of cluster heads is unavoidable. Thus, it is desirable to have a minimum number of cluster heads that can serve the network nodes scattered evenly in the area. An optimal selection of the cluster heads is an NP hard problem [6][7]. 41
IV. PROPOSED WORK The OWCA algorithm was designed to select cluster heads dynamically in mobile ad hoc networks. As mentioned above, sensor networks in general have more constraints than traditional networks. It is thus not so appropriate to directly apply the OWCA algorithm to the sensor networks since it does not take the power energy, the transmission rate, among others into consideration. we will modify the weighted clustering algorithm such that it can be used in sensor networks with the specific constraints where sensor networks being considered. Especially, we add one more factor about the characteristic of a sensor node into the evaluation formula, such that the nodes chosen as cluster heads may have a better behavior in heterogeneous sensor networks than those without the additional factor. The cluster heads can then act as application nodes in the sensor networks. After a fixed interval of time, the proposed algorithm is re-run again to find new applications nodes for the purpose of getting a longer system lifetime [8]. V. ADVANTAGES OF PROPOSED WORK The base stations are randomly generated to compute the system from the application nodes which were clustered by OWCA and WCA respectively. The transmission radius of an application node was assumed unlimited for simplifying the computation of system. Every application node would thus choose the nearest base station and computed. The system application nodes determined by the two algorithms along with different numbers of base stations would go up steadily. The OWCA algorithm got better system lifetime than the WCA algorithm. It is because OWCA took the characteristics of a sensor node into consideration, but WCA didn t. There is a small difference in the numbers of application nodes for OWCA and WCA. VI. SEMANTICS OF OWAC Basis for Our Algorithm is To decide how well suited a node is to be a cluster head that we take into account with its degree, transmission power, mobility and battery power. The following features are considered in our Optimized weighted clustering algorithm (OWCA). The cluster head election procedure is not periodic and invoked rarely as possible. This reduces system updates and hence computation and communication costs. Each cluster head can ideally support nodes to ensure efficient MAC functioning. A high throughput of the system can be achieved by limiting or optimizing the number of nodes in each cluster. The battery power can be efficiently used within certain transmission range. Consumption of the battery power is more if a node acts as a cluster head rather than an ordinary node. Mobility is an important factor in deciding the cluster heads. Reaffiliation occurs when one of the ordinary nodes moves out of a cluster and joins another existing cluster. In this case, the amount of information exchange between the node and the corresponding cluster head is local and relatively small. The information update in the event of a change in the dominant set is much more than a reaffiliation. A cluster head is able to communicate better to its neighbors if they are closer and within the transmission range to the cluster head. This is due to signal attenuation with increasing distance [2]. In this proposed work, the OWCA is used for identifying Cluster heads in mobile ad-hoc networks[2]. The optimized weighted clustering algorithm (OWCA): Input: A set of sensor nodes, each with the same transmission radius Rv, its individual cumulative time Tv, mobility speed Mv, transmission rate rv, the initial energy Ev, the constant of amplification c, the predefined ideal node number M in a cluster, and the five coefficients w1 to w5. Output: A set of application nodes with its neighbors. Step 1: To find the neighbors N(v) of each node v, where a neighbor is a node with its distance v within the transmission radius Rv. That is: N(v)={v' distance(v,v' ) Rv}. STEP 2: To compute the degree difference v as dv - M for each node v. STEP 3: To compute the sum Dv of the distances between node v with all its neighbors I.e STEP 4: To Compute the mobility speed of every node v by the following formula: 42
where (Xt, Yt) and (Xt-1, Yt-1) are the coordinate positions of node v at time t and t-1. STEP 5: To find the cumulative time Tv in which node v has acted as a cluster head. A larger Tv value with node v implies that it has spent more resources (such as energy). STEP 6: To compute the characteristic Cv for every node v as below: Cv=c * rv/ev, where rv is the transmission rate, Ev is the initial energy and c is a constant for amplification. Interactive tools for iterative exploration, design and problem solving. NS2 Editor Provides standard editing and debugging features. In this proposed work an Optimized clustering algorithm based on the weighted clustering algorithm with additional constraints is used for selection of cluster heads in mobile ad-hoc networks. The characteristics of mobile nodes including the energy and the transmission rate are considered in the proposed algorithm. The cluster heads chosen can act as the application nodes in a network and may change in different time intervals. After a fixed interval of time, the proposed algorithm is re-run again to find new applications nodes such that the system lifetime can be expected to last longer. STEP 7: To Calculate the combined weight Wv=w1 v+w2dv+w3mv+w4tv + w5cv for every node v. STEP 8: To Choose the node with a minimum Wv as the cluster head. STEP 9: To eliminate the chosen cluster head and its neighbors from the set of original sensor nodes. STEP 10: Repeat step from 1 to 9 for the remaining nodes until each node is assigned to a cluster. Note that in Step 6, the factor for the characteristic of a node is added to evaluate the goodness of a node as a cluster head. As an alternative to evaluate the goodness of a node, the factor of the cumulative time can be removed and the initial energy in the characteristic factor of a node can be changed as the remaining energy. This is because the remaining energy of a node partially depends on its cumulative time as a cluster head. VII. EXPERIMENTAL RESULT AND ANALYSIS In this work, a high-level technical computing language NS2 is used since it provides an interactive environment for algorithm development, data visualization, data analysis, and numerical computation. The Key features are as follows: High-level language for technical computing. Develop environment for managing code, files and data. Fig.1 shows the time intervals along X-axis and No. of packets sent along Y-axis. In the above fig. cluster formation using overall result of existing and proposed work are compared 43
Fig.2 shows the time intervals along X-axis and bandwidth consumption with respect to nodes along Y- axis. In the above fig, bandwidth goes on increasing with increase in time interval. Fig.3 shows the time intervals along X-axis and No. of Total packet sent, received, dropped and overhead along Y-axis. Fig.4 shows the cluster routing formation in mobile adhoc networks. VIII. CONCLUSION In this project, a cluster based multi-source multicast routing protocol is used to provide efficient multicasting and robustness in mobile ad hoc networks. For this routing, a new optimized based weighted clustering algorithm (OWCA) we designed to optimize the clusters and hence cluster heads. we have proposed an optimized clustering algorithm based on the weighted clustering algorithm with additional constraints for selection of cluster heads in mobile networks. To prove this clustering algorithm and proposed approach results have been simulated in NS2.The simulation results are conducted with both single active multicast cluster and multi active multicast cluster. The routing protocol achieves high delivery ratio without causing large control overhead in multicast environment. Hence the proposed cluster based multicast routing protocol is efficient and robust for multicasting. In ad hoc networks, power consumption is an important factor for network. As future work, we can carry out more simulations to study the performance of the proposed algorithm in different environments and planning to compare our algorithm with more existing multicast routing protocols. Also the proposed work can be carried out using other effective clustering approaches as an attempt to extend this approach for solving more complicated problems in ad-hoc networks. 44
IX. REFERENCES [1] Wei-dong Yang and Guang-zhao Zhang, A Weight-Based Clustering Algorithm for mobile Ad Hoc network, IEEE Proc. ICWMC'07 0-7695-2796-5/07, 2007. [2] M Chatterjee, S K Das and D Turgut, WCA: A Weighted Clustering Algorithm for Mobile Ad Hoc Networks, IEEE Journal of Clustering Computing, 5(2), pp.193-204, 2002. [3] Damla Turgut, Sajal K. Das, Ramez Elmasri and Begumhan Turgut, Optimizing Clustering Algorithm in Mobile Ad hoc Networks Using Genetic Algorithmic Approach, IEEE, pp.62-66, 2002. [4] Damla Turgut, Begumhan Turgut, Ramez Elmasri and Than V. Le, Optimizing Clustering Algorithm in Mobile Ad hoc Networks Using Simulated Annealing, IEEE pp.1492-1497, 2003. [5] Chunlin Ji, Yangyang Zhang and Shing Gao, Particle Swarm Optimization for Mobile Ad Hoc Networks Clustering, IEEE, pp.372-375, 2004. [6] S. Basagni, 1. Chlamtac, and A. Farago, "A Generalized Clustering Algorithm for Peer-to- Peer NeworW, Proceedings ofworkshop on Algarithmic Aspects of Communication (satellite workshop of ICALP), Bologna, Italy, July 1997. [7] B. Bollohas, Random Graphs, Academic Press, 1985. [8] Geetha Jayakumar, and G. Gopinath, Ad Hoc Mobile Wireless Networks Routing Protocols A Review, Journal of Computer Science,vol.3(8), pp.574-582, 2007. 45