Centralized two tier clustering method for wireless sensor networks based on a coupled cascaded fuzzy system

Current applications in the Internet of Things generally rely on wireless sensor network deployments that measure and control a restricted area. Most of those applications use sensor nodes powered with batteries, so efficient energy management is required to maximize the lifetime of the network. To tackle this issue, clustering becomes a suitable solution to prolong energy sources, being the selection of the cluster heads crucial for its optimal operation. The application of soft computing techniques (e.g. fuzzy logic) to clustering has improved the wireless network performance significantly. Therefore, this approach proposes a centralized, two-tier clustering method in which there are cluster heads and super cluster heads. This hierarchy is defined based on a sustainability filter and a two-stage cascaded fuzzy system. Initially, the sustainability filter removes unsuitable nodes in the process of selecting the cluster heads. The decision is based on the node residual energy, eliminating those with low battery levels. The remainder nodes use a first fuzzy system where some of them are promoted as cluster heads. Then, for each CH, a second stage is run, which takes as input the output of the first fuzzy system and three other variables to allow the selection of super cluster heads. The findings of the simulation of this approach have demonstrated that cascaded fuzzy systems have the capacity to circumvent issues such as rapid depletion of energy at the node in close proximity to the base station. Additionally, the simulation results of the proposed method demonstrated a substantial enhancement in the lifetime across the various scenarios applied. Furthermore, they have been shown to exhibit a substantial degree of adaptability to varying base station locations.