Scalable and sustainable wireless sensor networks for agricultural application of Internet of things using fuzzy c-means algorithm

Abstract

The integration of sensing, communication and Internet is innovatively merging into a new technology called Internet of things (IoT). Wireless sensor networks (WSNs) are the main physical monitoring infrastructure of IoT. Resource constrained sensor nodes have to be utilized in energy efficient manner so as to maximize the monitoring network's lifetime. Thus, for large scale monitoring applications of agriculture, forest and environment, it is required to have sustainable WSNs, where maximum number of sensors is alive over a large period of time. In the new era of IoT, WSN is popularly preferred and used in precision agriculture for farmland monitoring. In this proposed work, an attempt is made to design a cost effective clustering algorithm to obtain energy efficient sustainable WSN while maximizing node density and coverage area. The first objective of the proposed algorithm is to optimize energy efficiency by reducing data transmission distance of sensor nodes using fuzzy c-means (FCM) clustering algorithm. The second objective is to select a suitable cluster head node (CHN) based on perceived probability to attain network scalability. The results obtained shows that proposed algorithm is more energy efficient than other similar approaches. The comparative result statistics prove that proposed algorithm outperforms in terms of half of the nodes dead (HND) and last node dead (LND) for scalable scenarios. Thus, it can be effectually used in farm monitoring IoT systems.