A Fuzzy-based Clustering and Data Collection for Internet of Things based Wireless Sensor Networks

The Internet of Things (IoT) has become an integral part of our daily lives and this is possible due to the billions of connected devices. Thanks to the small and cheap sensors with the ubiquity of wireless networks, which form any kind and size of IoT. A huge number of sensors that can exchange various data among themselves make it a basic building block of IoT. Sensor nodes play a major role in IoT but the major issue is resource constraints. As sensor nodes are resource constraints devices, energy conservation is the main objective in most of the networks. This paper proposes an energy-efficient data collection mechanism using dynamic clustering routing. The soft computing technique (Fuzzy Inference System) is used for dynamic clustering with three important network parameters: residual energy, node density, and packet generated. A Wi-Fi enabled mobile coordinator router (MCR) is used to collect data from the cluster head (CH) directly. After gathering data from any CH, it transmits that received data to the gateway using the IEEE803.11 protocol instantly for further processing and applications. The extensive simulations for the proposed scheme show a better performance on different parameters mainly with reference to network life, average data delivery, energy depletion, and average delay.