Saturday, 13 April 2013
WSN Partitioning Strategies
call 0345-7069321
Wireless Sensor Network is
partitioned into collection of nodes which are called clusters. Clustering
technique is used in order to enhance performance of WSNs. There are many
clustering techniques whose goals are longevity, convergence in more area,
nodes reliability and route stability. CH may be pre-selected by network
designer or elected by sensors in clusters after deployment. CHs are mainly
responsible for all activities in the clusters like it keeps member nodes in
low power sleep mode to increase the life time of individual node by reducing
the energy consumption.
Different energy efficient routing protocols have been
suggested which perform single and multi hop communication to send data from
source to cluster head and cluster head
to sink(base station) like
1. CACC: Clustering Algorithm based on Cell Combination
2. VAP-E: Energy-Efficient Clustering -Virtual Area
Partition
3. CFL: Clustering for
Localization
4. FoVs: Overlapped Field of View
5. KOCA: K-Hop Overlapping Clustering Algorithm
6. PEZCA: Power-Efficient Zoning Clustering Algorithm
7. VoGC: Voting-on-Grid clustering
8. BARC: Battery Aware
Reliable Clustering
9. Hausdroff
Clustering
10. HSA: Harmony Search Algorithms
11. PEGASIS: Power-Efficient Gathering in Sensor
Information System
12. Max-Min D-Cluster Algorithm
13. PDCH: Pegasis
Algorithm Improving Based on Double Cluster Head
14. GROUP
15. EECS: Energy Efficient Clustering Schemes
16. EEUC: Energy Efficient Unequal Clustering
17. LCA: Linked Cluster
Algorithms and LCA2
18. Highest-connectivity cluster algorithm
19. Particle swarm optimizers for cluster formation
in wireless sensor networks (PSO- Clustering )
20. PSO-C: Centralized-PSO
21. MST-PSO: Minimum Spanning Tree-PSO
22. LEACH and Its Descendant
Students will simulate suggested clustering protocols and
compare the result with the respect to energy consumption and life time of
nodes in WSN.
Simulations can be implemented in
MATLAB or any network simulation like NS2 etc.
NOTE: Students will implement and
compare result of any two algorithms specified by the Supervisor.
