Linear optimization and fuzzy-based clustering for WSNs assisted internet of things

Wireless data transmission on the Internet of Things (IoT) needs data-aware communication protocols. Clustering is one of the effective network management approaches that enhance the lifetime of IoT. The primary challenge in the data transmission across IoT is designing an energy-efficient clustering mechanism. Existing protocols struggle with the non-optimal selection of CHs and frequent re-clustering on IoT, which leads to significant energy consumption. If the cluster head (CH) lifetime of the devices (nodes) is known prior, then re-clustering can be avoided to a reasonable extent. Therefore, in this paper, we estimate the lifetime of devices as CHs by solving a linear optimization problem to extend the first node death as much as possible and also, stalls the frequent re-clustering process to minimize the energy consumption. We also apply the uniform distribution of CHs to ensure balanced energy consumption on IoT devices. The proposed clustering technique named ECFEL (Efficient Clustering using Fuzzy logic based on Estimated Lifetime) for IoT outperforms the existing protocols, namely Low Energy Adaptive Clustering Hierarchy (LEACH), MODified LEACH (MOD-LEACH), Dynamic k-LEACH (DkLEACH), Novel-PSO-LEACH, FM-SCHEL, and M-IWOCA techniques in terms of first node death (FND), half node death (HND), last node death (LND). Our simulation results showcase that ECFEL is having a better lifetime in terms of FND, HND, and LND, respectively. Furthermore, the experiments also confirm that ECFEL consumes less energy while maintaining a packet delivery ratio for a more extended period.