International Journal of Computer Engineering and Applications, Volume VIII, Issue II, Part I, November 14

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1 A NEW LOAD BALANCING CLUSTERING ALGORITHM FOR SENSOR NETWORK Sunil Kumar Singh 1, Rajesh Duvvuru 2, Gudikhandula Narasimha Rao 3 1 Department of Computer Science & Engineering, DIT University, Dehradun, Uttarakhand, INDIA. 2,3 Department of Geo-Engineering, Andhra University College of Engineering, Visakhapatnam, India ABSTRACT: The basic idea behind load balancing is to distribute load on all the computers or nodes which are idle or less utilized. Clustering is a technique to distribute all sensor nodes in an efficient way for utilizing their energy in an optimal way. Different heuristics employ different policies to elect Cluster Heads. Many these policies are biased in favor of some nodes. As a result, these nodes having greater responsibility which may drain their energy faster due higher number of communication made, causing them to drop out of the network. Therefore, there is a need for load- balancing among Cluster Heads to allow all nodes the opportunity to serve as a Cluster Head. In this project we proposed a few enhancements to existing algorithms to remove the unbalanced distribution of nodes under the Cluster Heads and increase the active life of a node in a network. This has been performed using simulation under OMNET++ and the comparison graphs have been shown to depict the performance enhancement. Keywords: Sensor networks, Cluster Heads, Load balancing, Highest Degree (HD), Lowest ID (LID), OMNET++ [1] INTRODUCTION Wireless sensor networks, and the arrangement of these small, electronic devices into radio networks, have introduced the ability of distantly monitoring physical surroundings for a wide variety of parameters. In effect, there is additionally the ability to interface the physical world, monitoring whatever parameter is of interest, with the internet. A scientist may, for example, be able to view conditions in a rain forest on another continent, from his office in Delhi. Sensor networks do not use a fixed architecture for communication. In order to provide efficient communication there need to be wireless backbone. The backbone must change to reflect the changes in the network topology as the nodes move. The algorithm to choose the backbone should be very fast and efficient as they involve mobile nodes powered by battery. Clustering is a solution to the above problem. Even clustering suffers overhead from cluster formation and maintenance. Since the nodes are powered by limited supply of power, the objective is to enhance the existing clustering algorithms to make them a bit more efficient. A general Sensor Network will have the following features [1] 145

2 A New Load Balancing Clustering Algorithm For Sensor Network Mobility: The nodes can change the position themselves in matter of seconds, making the mobility pattern of the nodes non deterministic. This mobility pattern had a major effect on the formation of clusters within the network. This mobility and wireless nature is one of the major features of the sensor networks and helps it to be deployed in any kind of terrain. Multi hop Network: Since the nodes work as group,a multi hop network is created so that even if a node is not in direct contact with the cluster head it can still get the information via the intermediate nodes by forwarding the same data. This multi hop networks is generated by the conversion of a normal node to a router or gateway. Multiple roles for a node: The sensor networks should be able to organize itself by generating routers, gateways etc. to maintain communication with all the other nodes. This is done by converting a normal node to a router or a gateway. Energy Constraints: In a sensor network the nodes are mobile and communicate over a wireless channel. Being mobile the power is used from a battery and the size needs to be kept at a minimum. Hence there is a need to manage the battery power consumption, so that they do not drop out of the network prematurely due to low power. Out of these the greatest challenge for a sensor network is resource management and that too particularly the battery life which, later in the paper is referred to as network lifetime. The lifetime of a sensor network is the time when the network starts working till one of the node go down out of the network because the battery was exhausted. When clusters are shaped in the network, one of the nodes becomes the cluster head, which bears the responsibility of sustained communication with all the other nodes. This accountability of maintaining the communication links depletes the battery very fast as this node has to make more number of communications as compared to a normal node and the node drops out of the network. This reduces the network lifetime. In this paper we discuss two existing clustering algorithms and propose an enhancement for efficient distribution of nodes under one cluster head merging with these two algorithms. [2] RELATED WORKS Many clustering with load balancing algorithms [2] [3] [6] [7] are proposed till date. But the basic idea is retrieved from our previous works regarding wireless networks and its transmission issues [11][12] [13] [14] [15] [16]. In this paper, we first present the study of two popular clustering algorithms HD [4] and LID [5]. Highest-Degree (HD) algorithm, originally proposed by M. Gerla, J.T.C. Tsai [5], exclusively uses location information for cluster formation the highest degree node in a neighborhood, i.e. the node with the largest number of neighbors is elected as CH. Since it uses the degree of connected nodes as a parameter, a few nodes are over loaded as compared to the other nodes. This increases the battery consumption as this node has to shoulder a lot of responsibility. Thus, the probability of this node dropping out of the network is high. The main asset of LID method is its implementation simplicity. It is also a quick clustering method, as it only takes two HPs to decide upon cluster structure and also provides a 146

3 more stable cluster formation than HD. In contrast, HD needs three HPs to establish a clustered architecture. However, the main drawback of LID heuristic is its bias towards nodes with smaller IDs: these nodes are highly likely to serve as CHs for long periods which may lead to their rapid battery drainage. In addition, neither LID nor HD algorithm take into account of mobility metrics, i.e. highly mobile nodes are equally likely to be elected as CHs, Although their movement away from their attached cluster members may soon lead to a current clustering effect. [2.1] PARAMETER IDENTIFICATION For discussing the load balancing clustering Algorithms, we must first define a few major parameters using which these algorithms have been implemented [8]. A combined metric clustering algorithm [9], [10] uses parameters like running average, degree of connectivity, transmission power and available battery power of a radio to find its suitability as a head. Work Budget: The amount of work that can be done using a fully charged battery by a single node. This can also be referred as the total energy stored in a node. Node Range: The maximum communication range of a single node. Here we have assumed it to be 25 units. Node ID: The node IDs. Work Done: The amount of energy/battery depleted by making one communication with one node. Mobility Pattern: Non Deterministic i.e. they are independent to move. Sample Area: The total network area. Here we have assumed it 200x200 sq units. While implementing all these algorithms we have used a set of common algorithms for calculating the distance between the nodes, random work deduction, and mobility of the nodes. [3] PROPOSED ENHANCEMENT Using the above two algorithms [4] [5] and incorporating the concept of load transfer in the determination of the Cluster Head we propose an enhancement to the whole process of clustering. [3.1] SELECTION OF THE CLUSTER HEAD Selection of the Cluster Head, we are using Fuzzy decision made by the nodes that will be coming under different Cluster Head. Work budget left and numbers of nodes under the cluster head are the main parameters for this fuzzy selection. Based on these two values the node will decide its Cluster Head. In the above algorithm first calculate the average work budget left for all the Cluster Head and the average number of nodes under a Cluster Head. We then retrieve the Cluster Head with the maximum array will give us the desired Cluster Head for the current node. We multiply because if the WB is high but 147

4 A New Load Balancing Clustering Algorithm For Sensor Network difference between NN and AVG (NN) is low, we get a lower value as compared to the rest of the combinations. The Cluster Head, having the lowest value will have a relatively higher work budget left and a lower number of nodes under it are selected. [3.2] LOAD TRANSFER This part of the enhancements is done to reduce the effect of increased load when a new node gets admitted into the cluster. The above algorithm works well, but if the node tries to join at a later stage, the Cluster Heads tends to transfer this node to another Cluster Head which is relatively under loaded. Parameters: SOC: set of all the cluster heads CHN: Cluster Head Node NT: Total number of nodes in the sample area CVR: The nodes covered under a cluster head NR: Node Range (25 Units) Ni: Node under consideration NC: Set of respective ClusterHead that can take the Current node under its own cluster NUN: Number of Nodes under the ClusterHead WB: Work Budget left Implementation: Begin TransferCHK for(i=0; i<=nc; i++) if(ni belong to NC) T1= ( WBi from SC) T2 = ( NUNi from SC) M1 = MOD(AVG(T1-Ti)) M2 = MOD(AVG(T2-Ti)) M3= M1*M2 if(min(m3) Transfer Ni to CHNi end if end if end for end TransferCHK When a new request comes to a ClusterHead, the ClusterHead looks for all the ClusterHeads which can take this node under its own cluster. The ClusterHead compares its existing work budget and number of nodes under it. So the ClusterHead with relatively lower load will accept this incoming node. [4]SIMULATIONS AND RESULTS The test bench that we have used: Mobility pattern is non deterministic. 148

5 Movement not more than ½ the range of a node per unit time. 200 x 200 Sq. Unit Areas. Size of the cluster is not known. Total number of nodes = {25, 50, 75,100}. Sample time = 100ms. Node Range = 25 Units (fixed). Work Budget = 5000 Units. Random Work deducted with a max work done of10. The OMNet++ simulator is a software tool designed in C++. The simulated sensor network is composed of slowly changing network topology. The topology is a square area. The network nodes are randomly distributed. Initial energy (Work Budget) of all the nodes in the network is The range of each node is 25 units which are taken as input to the program. The movement speed of a node can vary between 0 and ½ the communication range of a node. The mobile nodes move according to the random waypoint" model. Each mobile node begins the simulation by remaining stationary for pause time seconds. It then selects a random destination in the defined topology area and moves to that destination at a random speed. The random speed is distributed uniformly between zero and maximum speed of not more than ½ the communication range of a node. Upon reaching the destination, the mobile node pauses again for pause time seconds, selects another destination, and proceeds there as previously described. This movement pattern is repeated for the duration of the simulation. Figure 1: Formation of efficient clusters [4.1] COMPARISON OF HD, LID AND PROPOSED ALGORITHMS One of the major parameter where we can easily asses the quality of the algorithm is the average Cluster Head Time. This gives us a fair idea of the network lifetime. Hence we compare our proposed algorithm with the existing algorithm. 149

6 A New Load Balancing Clustering Algorithm For Sensor Network Figure 2: Comparison of HD, LID and proposed algorithms From [Figure-2] we can conclude that our proposed algorithm performed well on Higest ID and Lowest ID algorithms. Average cluster head duration (sec) of proposed algorithm is high compare to HD and LID algorithms in less number of nodes. [4.2] ANALYSIS OF AVERAGE CLUSTER NUMBER FORMATION [Figure-3] shows the alteration of the average number of clusters with respect to the number of nodes where the transmission range=150 and Max_disp =10. Our algorithm acquires fewer clusters than the others. Also, the average number of clusters is decreased; when the transmission range increases. The possible reason for this kind of behavior is that a clusterhead with a large transmission range will cover a larger area. Figure 3: Number of nodes Vs. Average No. of clusters, Max_disp=10, Tx_Range= 150 [5]CONCLUSION HD is efficient in building the clusters and deciding upon which nodes will be elected as the cluster head. But the HD algorithm suffers a major flaw related to the way in which it makes these clusters. The LID algorithm has a better way of making clusters. By choosing the uncovered node having the lowest physical ID as the cluster head, it forms the cluster taking the nodes in direct communication range. But this type of election is to out disadvantage since this single node reduces the average cluster head time. The proposed enhancements helps the existing algorithm LID to maintain a better network as compared to LID alone by distributing load over to a relatively under loaded Cluster Head and helps in the selection of Cluster Head by monitoring two major parameters of the sensor network i.e. the work budget left and the number of nodes serviced by the cluster head. This improvement in the network lifetime is due to the change in the election and selection algorithm, and also due to the transfer of node when there is a new admission to the system after it starts running. As the number of nodes in the sample area increases the network lifetime decreases. But this fall in the network lifetime is stabilized as the nodes increase. 150

7 REFERENCES [1] S.Kumar C.Chong, "Sensor Networks: Evolution, Opportunities," in Proc. IEEE, Aug. 2003, pp ,vol. 91, no. 8. [2] J. Yu, P. Chong, A Survey οf Clustering Schemes for Mobile Ad Hoc Networks, IEEE Communications Surveys, 7(1), pp , March2005. [3] Chor Ping Low and Can Fang, Load-Balanced Clustering Algorithms for Wireless Sensor Networks, Technical Report July [4] M. Gerla, J.T.C. Tsai, Multicluster, mobile, multimedia radio network, Wireless Networks 1(3), pp , [5] Li C. R., Gerla Mario, Adaptive Clustering for Mobile Wireless Networks, IEEE Journal of Selected Areas in Communications, 15(7), pp , September [6] Jabbar N., Batt P., Threshold based load balancing protocol for energy efficient routing in Wireless Sensor Network, International Conference on Advance Communication and Technology (ICACT), pp , February [7] Boyinbode, O.; Le, H.; Takizawa, M. A survey on clustering algorithms for wireless sensor networks. Int. Space-Based Stituated Comput. 2011, 1, [8] Peng Guo, Tao Jiang, Kui Zhang, et al. Clustering algorithm in initialization of multi-hop wireless sensor networks, IEEE Transactions on Wireless Communications, 8(12): , [9] S.Das, M.Chatterjee, D.Turgut, Wca: A weighted clustering algorithm for mobile ad hoc networks, Journal of Cluster computing (special issue on mobile ad hoc networks), vol. 5, no. 2, 4, pp , [10] S. Basagni, Distributed clustering for ad hoc networks, Proceedings of International Symposium on Parallel Architectures, Algorithms and Networks, pp , June [11] Duvvuru, Rajesh, P. Jagadeeswara Rao, and Sunil Kumar Singh. "Improvizing Security levels in WLAN via Novel BSPS." Emerging Trends in Communication, Control, Signal Processing & Computing Applications (C2SPCA), 2013 International Conference on. IEEE, [12] Singh, Sunil Kumar, Rajesh Duvvuru, and Jyoti Prakash Singh. "Performance Impact of TCP and UDP on the Mobility Models and Routing Protocols in MANET." Intelligent Computing, Networking, and Informatics. Springer India, [13] Duvvuru, Rajesh, et al. "Enhanced Security Levels of BSPS in WLAN." International Journal of Computer Applications 84.2 (2013): [14] Singh, Sunil Kumar, Rajesh Duvvuru, and Amit Bhattcharjee. "Performance Evaluation Of Proactive, Reactive And Hybrid Routing Protocols With Mobility Model In MANETS.",IJRET, Volume: 02 Issue: 08, pp , Aug-2013 [15] Duvvuru, Rajesh, et al. "Scheme for Assigning Security Automatically for Real-Time Wireless Nodes via ARSA." Quality, Reliability, Security and Robustness in Heterogeneous Networks. Springer Berlin Heidelberg, [16] Singh, Sunil Kumar, Rajesh Duvvuru, and Saurabh Singh Thakur. "Congestion Control Technique Using Intelligent Traffic and Vanet." arxiv preprint arxiv: (2013). 151