Adaptive energy efficient fuzzy: An adaptive and energy efficient fuzzy clustering algorithm for wireless sensor network‐based landslide detection system

Abstract

Abstract 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.