The week's pick
Reseach Article
A Constant Threshold based Clustering using Fuzzy Inference in Wireless Sensor Network
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 178 - Number 34 |
| Year of Publication: 2019 |
| Authors: Yogender Kumar Sharma, Rajveer Singh |
10.5120/ijca2019919196
|
Yogender Kumar Sharma, Rajveer Singh . A Constant Threshold based Clustering using Fuzzy Inference in Wireless Sensor Network. International Journal of Computer Applications. 178, 34 ( Jul 2019), 10-16. DOI=10.5120/ijca2019919196
Abstract
Wireless Sensor Network (WSN)is a network containing insignificant sensors node(SN) through low electrical transducers used as a data collecting tool in various environments based on the network setup. Designed message or communication passing procedure designed to protect the incomplete dynamism properties of sensors aimed at data processing is the main task of WSN. To accomplish this task, we are introducing a new concept for saving energy and enhancing the Network's network length. Clustering is innumerable in areas where excluding clustering Each compartment uses a different algorithm to utilize multi-hip routing, using an appropriate nod code to conduct information from the base station, from every position to every round. The residual energy, no. of nodes, distance within every node is estimated such as the Fuzzy criterion for selecting cluster head FMCR-CT, network lifetime, dead rounds in every round, first node decease, half node dies as well as the previous node dies. A sensor network can be made scalable by forming clusters. A sensor network can be made scalable by forming clusters. A sensor network can be made scalable by forming clusters. A sensor network can be made scalable by forming clusters. A sensor network can be made scalable by forming clusters. A sensor network can be made scalable by forming clusters
References
- Q. Gao, D. Holding, Y. Peng, and K. Blow, “Energy efficiency design challenge in sensor networks,” in London Communications Symposium, 2002.
- S. Ali, A. Ashraf, S. B. Qaisar, M. K. Afridi, H. Saeed, S. Rashid, E. A. Felemban, and A. A. Sheikh, “Simplimote: A wireless sensor network monitoring platform for oil and gas pipelines,” IEEE Systems Journal, vol. 12, no. 1, pp. 778– 789, 2018.
- F. Engmann, F. A. Katsriku, J.-D. Abdulai, K. S. Adu-Manu, and F. K. Banaszek, “Prolonging the lifetime of wireless sensor networks: A review of current techniques,” Wireless Communications and Mobile Computing, vol. 2018, 2018
- J. Lee, B. Shah, G. Pau, J. Prieto, and K.-I. Kim, “Real-time communication in wireless sensor networks,” Wireless Communications and Mobile Computing, vol. 2018, 2018.
- K. Kapalta, R. Singh, and A. Gautam, “Energy-efficient techniques of wireless sensor networks: A review,” International Journal of Advanced Research in Computer Engineering & Technology (IJARCET) Volume 6, Issue 2, February 2017, ISSN: 2278 – 1323,
- D. Pubill, J. Serra, and C. Verikoukis, “Harvesting artificial light indoors to power perpetually a wireless sensor network node,” in 2018 IEEE 23rd International Workshop on Computer Aided Modeling and Design of Communication Links and Networks (CAMAD). IEEE, 2018, pp.
- S. D. Attri and A. Tyagi, “Climate profile of India,” Environment Monitoring and Research Centre, India Meteorological Department, Lodi Road, New Delhi- 110003 (India), 2010
- C. Schurgers and M. B. Srivastava, “Energy efficient routing in wireless sensor networks,” in Military communications conference, 2001. MILCOM 2001. Communications for network-centric operations: Creating the information force. IEEE, vol. 1. IEEE, 2001, pp. 357–361.
- M. Mansouri, A. Sardouk, L. Merghem-Boulahia, D. Gaiti, H. Snoussi, R. Rahim-Amoud, and C. Richard, “Factors that may influence the performance of wireless sensor networks,” in Smart Wireless Sensor Networks. InTech, 2010.
Index Terms
Keywords