A KMeans-Genetic Based Clustering Architecture to Optimize WSN Communication

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1 A KMeans-Genetic Based Clustering Architecture to Optimize WSN Communication Pooja 1, Chinar Chahar 2 1,2 Student, M.Tech. (ECE), CBS Group of Institutions, Fatahpuri, Jhajjar, Haryana ABSTRACT Sensing network is the restricted real time network with smaller coverage range and energy node specification. In this architecture, complete network is divided in smaller dynamic or static regions called clusters. In this paper, the clustering method is improved in real time scenario by including two major techniques. At first level, the cluster selection procedure is improved by setting up the extra parameters as well as the decision criteria. In this stage, the degree and density analysis greedy method is applied to generate the spanning tree over the network. The selection of cluster head as the subtree head is also done in this stage. Once the cluster head and cluster members are identified. The route formulation is done using genetic approach. The implementation is done in matlab environment. The simulation results shows that the method has improved the communication and network life. Keywords: KMeans, Genetics, Clustering Architecture, I INTRODUCTION The basic objective on any routing[1][2][3][4] protocol is to make the network useful and efficient. In a cluster[2][5][6][7][8] based routing protocol group s sensor nodes, each group of nodes has a CH or a gateway. Sensed data is sent to the CH rather than send it to the BS; CH performs some aggregation function on data it receives then sends it to the BS where these data is needed. A clustered sensor network could also be classified as Single hop model Multi hop model A) Single hop model A single hop network is one in which sensor nodes use single hopping to reach the cluster head. LEACH is an example of homogenous Single Hop model. Both LEACH and LEACH C use one level clustering (The Cluster heads of each cluster directly connected with the sink node or base station.). B) Multi hop model In a multi-hop[9][10][11] network nodes use multi-hopping to reach the cluster head. The extension of LEACH called M LEACH or multi hop LEACH is a good example of multi hop model. The data captured by the sensor nodes are often converted into an aggregate form requested by the applications (e.g., average temperature reading). Primarily designed for monitory purposes, many sensor applications require continuous aggregation energy consumption because each sensor node has to report every reading to the base station. In wireless sensor networks, communication is a dominant source of energy consumption to save energy; data semantics can be relaxed to allow approximate data aggregation with precision guarantees. II. ADVANTAGES OF CLUSTERING Clustering[12][13][14] provides the effective network architecture to utilize the limited resources in more effective way. The advantages of the existing clustering method is given in this section A) Resource Utilization The work has presented the clustered architecture to perform the communication in sensor network under the sensor node restrictions. These restrictions are identified in terms of energy, smaller sensing range, limited memory etc. In this work, complete network is divided in smaller sub networks. These sub networks allow the small range 28

2 communication. The aggregative communication is here defined to reduce the multipath communication over the network. The adaptive routing and communication is able to reduce the energy consumption over the network. B) Balanced Network The work has provided an improved clustered architecture in which the energy adaptive balanced clustering is performed. The work has avoided the chances the overweight and underweight clusters. The node criticality is also considered while forming the clusters so that the reliability of the clustering process is improved. C) Multihop aggregative communication The multihop aggregative communication is performed between the cluster heads. This communication approach has reduced the overall communication over the network. The energy adaptive path has reduced the number of communication, confliction over the network so that the efficiency and reliability is improved. III RESEARCH METHODOLOGY In this present work, an effective dual featured clustering model is provided to achieve the better network life and communication in sensor network. The model is defined to utilize the sensor network features adaptively and strengthen these features while performing the communication. The complete communication model is here divided in two main stages. This two phase model is shown here in figure1. In first stage, the clustering is applied on restricted resource network. In this phase, the node level analysis is performed under multiple associated features. These features are extracted for individual nodes, surrounding featured nodes etc. These node level features include node energy, coverage range and the probability. The group adaptive features includes the degree of nodes, load and density analysis. Based on these parameters, the clustering method is applied to identify the clusters. Once the cluster heads are identified, the cluster members are identified based on the range and density map. Based on these criteria aspects, the spanning tree is generated for the network. Figure 1: Flow of Work 29

3 Once clustering is formed in the network, the next work is to perform the hierarchical communication in the network. The first level communication is here performed between the nodes and the cluster. This communication is performed directly for internal nodes and for external nodes; the genetic adaptive path is generated. Once the information is delivered to the cluster heads, the aggregative communication path is generated to deliver the information finally to base station. At this stage, the genetic approach is defined to perform the communication to the base station. The algorithmic model of work and genetic description is also defined in later sections. IV RESULTS In this paper, an effective clustering model is defined for sensor network. The proposed two phase model first improved the cluster election and later on improved the routing over the network. The work is implemented in matlab environment. The simulation parameters for the network construction and communication are listed here in table 1. Parameters Sensor Network Area Table 1 : Simulation Parameters Values 150x150 Number of Nodes 100 Initial Energy Transmission Loss Receiving Loss Sensing Range 0 to 1 J 5nJ 5nJ 40 Mtr Load Threshold 10 Number of Communication Rounds 100 The network is defined with random position of nodes. The analysis of the work is done in terms of packet communication and energy consumption parameters. The simulation results obtained from work are shown in figure 2 and figure 3. Figure 2: Dead Node Analysis 30

4 Here figure 2 is showing the dead node analysis for existing and proposed approach. The green line in figure showing the dead node for proposed approach and red line is showing the dead nodes for existing approach. The figure shows that the method has improved the network life effectively. Figure 3: Alive Node Analysis Here figure 3 is showing the alive node analysis for existing and proposed approach. The green line in figure showing the alive node for proposed approach and red line is showing the alive nodes for existing approach. The figure shows that the method has improved the network life effectively. Figure 4: Packet Communication Analysis Here figure 4 is showing the packet communication analysis for existing and proposed approach. The green line in figure showing the packet communication for proposed approach and red line is showing the packet communication for existing approach. The figure shows that the method has improved the packet communication for sensor network. 31

5 CONCLUSION In this paper, an improved clustering architecture for sensor network. In this two phase architecture, the cluster head selection is done using KMeans adaptive multi featured method. Once the cluster selection is done, the genetic method is applied to generate route over the cluster heads. The comparative simulation results shows that the method has improved the packet communication and network life. REFERENCES [1]. H. Li, "LEACH-HPR: An energy efficient routing algorithm for Heterogeneous WSN," Intelligent Computing and Intelligent Systems (ICIS), 2010 IEEE International Conference on, Xiamen, 2010, pp [2]. X. Li, W. Gang, L. Zongqi and Z. Yanyan, "An energy-efficient routing protocol based on particle swarm clustering algorithm and inter-cluster routing algorithm for WSN," th Chinese Control and Decision Conference (CCDC), Guiyang, 2013, pp [3]. S. P. Nayak, S. C. Rai and S. K. Pradhan, "MERA: A Multi-clustered Energy Efficient Routing Algorithm in WSN," 2015 International Conference on Information Technology (ICIT), Bhubaneswar, 2015, pp [4]. D. Gopal and Suriyakala C. D, "Performance evaluation of hybrid energy efficient cluster based routing scheme for mobile sink based WSN," 2015 International Conference on Control, Instrumentation, Communication and Computational Technologies (ICCICCT), Kumaracoil, 2015, pp [5]. N. A. Khan, K. Saghar, R. Ahmad and A. K. Kiani, "RAEED-EA: A formally analysed energy efficient WSN routing protocol," th International Bhurban Conference on Applied Sciences and Technology (IBCAST), Islamabad, 2016, pp [6]. R. Banerjee and C. K. Bhattacharyya, "Energy efficient routing and bypassing energy-hole through mobile sink in WSN," Computer Communication and Informatics (ICCCI), 2014 International Conference on, Coimbatore, 2014, pp [7]. L. Mengyao, Y. Zhang and X. Li, "Ring-based security energy-efficient routing protocol for WSN," The 26th Chinese Control and Decision Conference (2014 CCDC), Changsha, 2014, pp [8]. D. Singh, S. Sharma, V. Jain and J. Gajrani, "Energy efficient source based tree routing with time stamp in WSN," Signal Propagation and Computer Technology (ICSPCT), 2014 International Conference on, Ajmer, 2014, pp [9]. D. Wang, H. Tian and S. Wang, "Energy-Efficient Routing Research for WSN," 2007 International Conference on Wireless Communications, Networking and Mobile Computing, Shanghai, 2007, pp [10]. R. Manjula and R. Datta, "An energy-efficient routing technique for privacy preservation of assets monitored with WSN," Students' Technology Symposium (TechSym), 2014 IEEE, Kharagpur, 2014, pp [11]. J. Xu, F. Ning and D. Jiang, "The analysis and research of WSN's energy efficient routing protocols," Automatic Control and Artificial Intelligence (ACAI 2012), International Conference on, Xiamen, 2012, pp [12]. G. Lakshmi Phani, K. Venkat Sayeesh, K. Vinod Kumar and G. Rama Murthy, "ERFLA: Energy efficient combined routing, fusion, localization algorithm in cognitive WSN," 2010 Seventh International Conference on Wireless and Optical Communications Networks - (WOCN), Colombo, 2010, pp [13]. T. Agrawal, R. S. Kushwah and R. S. Tomar, "An Energy Efficient Routing with Range Switching in WSN," Computational Intelligence and Communication Networks (CICN), 2014 International Conference on, Bhopal, 2014, pp [14]. R. K. Kodali and N. Sarma, "Energy efficient routing protocols for WSN's," Computer Communication and Informatics (ICCCI), 2013 International Conference on, Coimbatore, 2013, pp