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  1. Energy adaptive cluster-head selection for wireless sensor networks using center of energy mass

Energy adaptive cluster-head selection for wireless sensor networks using center of energy mass

Akhtarkavan, E ; Sharif University of Technology | 2008

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  1. Type of Document: Article
  2. DOI: 10.1007/978-3-540-89985-3_16
  3. Publisher: 2008
  4. Abstract:
  5. A set of small battery-operated sensors with low-power transceivers that can automatically form a network and collect some desired physical characteristics of the environment is called a wireless sensor network. The communications must be designed to conserve the limited energy resources of the sensors [14].By clustering sensors we can save energy. In this paper, we introduce a new concept called "Center of Energy Mass" which is a combination of both energy level and location of the nodes which is used to form the new factor of "distance of the nodes to the CEM ".Distance of the nodes to the CEM is used together with Probability Density Function of the normal distribution in optimizing LEACH's cluster head selection algorithm. We optimized LEACH's random Cluster-Heads selection algorithm by means of finding the CEM, to ensure balanced energy depletion over the whole network thus prolonging the network lifetime. Simulation results show that our algorithm improves First Node Dies by 23.5% and Half Nodes Die by 5.6%. © 2008 Springer-Verlag
  6. Keywords:
  7. CEMLeach ; Center of energy mass ; Energy adaptive ; LEACH ; Wireless sensor ; Clustering algorithms ; Computational electromagnetics ; Computer science ; Dies ; Energy resources ; Leaching ; Normal distribution ; Optimization ; Probability density function ; Sensor networks ; Wireless sensor networks
  8. Source: 13th International Computer Society of Iran Computer Conference on Advances in Computer Science and Engineering, CSICC 2008, Kish Island, 9 March 2008 through 11 March 2008 ; Volume 6 CCIS , 2008 , Pages 130-137 ; 18650929 (ISSN); 3540899847 (ISBN); 9783540899846 (ISBN)
  9. URL: https://link.springer.com/chapter/10.1007/978-3-540-89985-3_16