Open AccessAdaptive energy efficient fuzzy: An adaptive and energy efficient fuzzy clustering algorithm for wireless sensor network‐based landslide detection system
Author(s) -
Ahmed Suhaib,
Gupta Swastik,
Suri Ashish,
Sharma Sparsh
Publication year - 2021
Publication title -
iet networks
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.466
H-Index - 21
eISSN - 2047-4962
pISSN - 2047-4954
DOI - 10.1049/ntw2.12004
Subject(s) - cluster analysis , wireless sensor network , computer science , energy consumption , efficient energy use , fuzzy logic , fuzzy clustering , node (physics) , energy (signal processing) , data mining , algorithm , distributed computing , real time computing , computer network , artificial intelligence , engineering , mathematics , statistics , structural engineering , electrical engineering
It is a well‐known research outcome that clustering helps in increasing the network lifetime and the routing performance. This research thus aims to optimize the energy consumption of wide scale wireless sensor networks (WSNs) by proposing a novel and an adaptive energy efficient fuzzy (AEEF) clustering for a WSN. It is an improvement and modification on the traditional clustering of the cells of the network for Landslide Detection systems. It incorporates the concept of fuzziness and state machine in selecting the cluster heads, unlike previously clustering algorithms such as low‐energy adaptive clustering hierarchy and so on. The proposed AEEF approach is validated by carrying out simulations and the results show that the average energy consumption per node under no‐clustering is 0.5144892 mJ, whereas it reduces drastically to 0.084482 mJ using the proposed AEEF clustering algorithm. Hence, the proposed algorithm is approximately 83.5% more energy efficient and thus increases the lifetimes of the nodes deployed for sensing a landslide along with being adaptive to any changes in the ambient conditions.