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Hierarchical clustering algo for wsn


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Research in Wireless Sensor Network Using Algorithm Implementation.

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Hierarchical clustering algo for wsn

  1. 1. Hierarchical Clustering Algorithm for Wireless Sensor Network Guided by Mrs. Himangi Pande Presented by Samruddhi P. Wagh 12418 1
  3. 3. INTRODUCTION • Sensor networks are highly distributed networks of small, lightweight wireless nodes, deployed in large numbers to monitor the environment or system by the measurement of physical parameters such as temperature, pressure, or relative humidity. • Sensors are made up of micro-electro mechanical systems (MEMS) technology. • Each node of sensor network consists of 3 subsystems:  Sensor Subsystem  Processing Subsystem  Communication Subsystem 3
  4. 4. Architecture of wsn Layered Architecture Clustered Architecture 4
  5. 5. Protocol stack for wsn 5
  6. 6. Routing Protocols for WSN • Routing Protocols for WSNs generally fall into 3 groups:     Data-Centric(also known as Data Aggregation) Hierarchical Location-Based QOS Oriented 6
  7. 7. Application and QOS of WSN Application QOS • • • • • • Delay, Jitter and Loss Reliability and Scalability Responsiveness Power Efficiency Mobility Bandwidth 7
  8. 8. Types of Clustering Intra-Cluster(within cluster) • In a cluster, one node act as a cluster head(CH) and rest of the node act as a cluster member(CM). • CH is selected using Election algorithm i.e. based on energy consumption. • If energy level of CH<Threshold, then the new CH selection. 8
  9. 9. Inter-Cluster(between cluster head and every cluster head and the sink)Types of Clustering Contd.. 9
  10. 10. Evolution of Hierarchical Clustering • Low-Energy Adaptive Clustering Hierarchy (LEACH) • Energy Efficient Hierarchical Clustering (EEHC) • High Energy Efficient Distributed (HEED) Low-Energy Adaptive Clustering Hierarchy (LEACH) 10
  11. 11. Hierarchical Clustering Algorithm • “ How to dynamically organize the sensor nodes into WSN and route sensed information from field sensor to remote base station?” • Hierarchical Clustering Algorithm is divided into two parts: • Multilevel hierarchical approach in Dynamic Clustering Election Algorithm(for efficient Cluster Head (CH) selection) • Dynamic Energy Efficient Hierarchical Routing Algorithm(for energy efficient routing) 11
  12. 12. Introduction of Dynamic and multilevel Hierarchical clustering The cluster formation is restructured based on the set of nodes without losing its transmission power. In Dynamic cluster algorithm, ID of node is set according to its distance to the data sink. 12
  13. 13. Multilevel hierarchy approach 13
  14. 14. Dynamic Clustering Election Algorithm • Step 1: Let the value of Degree of Isolation σ, such as σ = 0.001 Set j = 1; • Step 2: While (E ( current CH) < certain threshold) { The current CH broadcasts a message to poll the residual energy level of all its children; When a sensor receives this message it will report the current residual energy to its CH; • Step 3: The current CH selects the child with the maximum residual energy as the new CH ; the new Ch changes the radio range to 2R and broadcasts probing the new delivery node message to all neighbors. 14
  15. 15. Dynamic Clustering Election Algorithm contd…. • Step 4 : If (sink node == original cluster && hopCount < hopCount of original cluster) { Report its current battery residual energy and its path cost to original sink ; After the new CH receives the reply information; } • Step 5 : If (path_costold + costij * EREy < minPathCost) { minPathCost = path_costold + costij * EREy } 15
  16. 16. Dynamic Clustering Election Algorithm contd… • Step 6 : Change the primary path to corresponding information • If ( path_costnew > η* path_costold ) { Initiate path-switching by sending a new probing message to probe the path to another node; } • Step 7 : The new CH broadcast has selected the new delivery node in its primary path, it will broadcast the new information to all children in the old cluster. • Step 8 : All of children in old cluster change its previous hop to the NEW CH in its primary path. } 16
  17. 17. Energy Efficiency • Er the energy consumed in receiving the signal. • The total amount of energy needed to be consumed in order to send a packet over the one-hop distance is: Ei = Eij + Er where Ei is the energy of node i after sending data to node j; • Eij is the path loss, which is simply the difference between the transmission power used by i and the signal power received by j. 17
  18. 18. Dynamic Energy Efficient Hierarchical Routing Algorithm • Step 1: Split the number of regions based on the distance d. • Step 2: Compute the node distance d & energy level Eelec (in Joules). • Step 3: Select the CH based on the distance between the BS & the other CH. • Step 4: Set the cluster ID for all the clusters. • Step 5: The entire cluster ID is maintained in the Base Station. • Step 6: During topology discovery phase, a source node sends out a route request packet, which is flooded to the BS. Each node along a path also embeds its transmitting power and the cost of the path from the source into packet sent to its next hop. 18
  19. 19. Dynamic Energy Efficient Hierarchical Routing Algorithm contd.. • Step 7: Upon receiving the multiple copies of the route reply message, the source finds out a few routes to reach the BS based on distance. • Step 8: If none of the candidates meet the battery requirement, then the BS is informed to lower the value of Bref (t)(reference value w.r.t time) and the procedure repeats. • Step 9: Once the route is established, the source start to send the data to BS. 1. By using the reference, the selected routes are more evenly distributed over the entire network so that the network lifetime can be prolonged. 2. The Bs does not choose the final route because it does not know the battery status of the node. 19
  20. 20. Dynamic Energy Efficient Hierarchical Routing Algorithm contd.. 3. The value of Bref (t) can be chosen by the BS in terms of the estimation of the average power consumption per node at the current time, which can be computed based on the observed total energy consumption of the network. • The Routing Table which is maintained in CH is shown in Table. Node ID Cluster ID Distance between CH Energy level of each node 25 2.1 200 m 198 J 33 2.2.1 100 m 256 J 38 3.1 170 m 300 J 45 3.2.3 120 m 357 J 50 3.3.3 125 m 370 J 70 4.1 150 m 400 J 75 4.2.1 175 m 420 J 20
  21. 21. Dynamic Energy Efficient Hierarchical Routing Algorithm contd.. 21
  22. 22. Simulation method Total energy spent vs. number of levels in the clustering hierarchy22
  23. 23. Simulation method contd… Total Energy Spent vs. number of levels in the clustering hierarchy23
  24. 24. Conclusion • Hierarchical clustering and routing algorithms will work efficiently and reduces the energy consumption of sensor nodes. • As the CH selection is important problem in sensor network, for this cluster-based routing has been shown to be more energy efficient and increase the network lifetime through data aggregation. • The goal of selecting the CH is to minimize the transmission cost and energy usage. 24
  25. 25. Bibliography • Kazem Sohraby, Daniel Minoli, Taieb Znati,”WIRELESS SENSOR NETWORKS Technology, Protocols and Applications,” John Wiley, New York, 2007. • S.V. Manisekaran, Dr.R.Venkatesan, “Energy Efficient Hierarchical Clustering for Sensor Networks,” 2010 IEEE Second International Conference on Computing, Communication and Network Technologies. • S. Bandyopadhyay and E.J.Coyle, “An Energy Efficient Hierarchical clustering Algorithm for Wireless Sensor Networks,” 2003 IEEE Electrical and Computer Engineering, Purdue University, West Lafayette, IN, USA. • C. Siva Ram Murthy, B. S. Manoj, “AD HOC WIRELESS NETWORKS Architecture and Protocols,” PEARSON. • Heinzelman W, Chandrakasan A, Balakrishnan H, “An Application-Specific Protocol Architecture for Wireless Microsensor Networks IEEE Transactions on wireless communication, P. 660-670, 2002. • F.L.LEWIS, D.J.Cook and S.K.Das, “Wireless Sensor Networks Smart Environments: Technologies, protocols, and Applications John Wiley, New 25 York, 2004.
  26. 26. Thank you !!! 26