Improving Energy Estimation based Clustering with Energy Threshold for Wireless Sensor Networks

Abstract

In this paper an energy usage estimation technique (LCEFCM) has been proposed which employs the Fuzzy C-Means clustering for creating clusters in the Wireless Sensor Networks. LCEFCM reduces the energy consumption considerably compared to other clustering methods like simulated annealing and K-Means clustering. It applies the dynamic clustering mechanism combined with balanced clustering method. LCEFCM outperforms LEACHC, LEACHC Estimate(LCE) and LCEKMeans for various performance measuring factors like network lifetime, data received, alive nodes etc.

An IoT Framework for Real-Time Event Detection and Acquisition Using Mobile Sink

“…So the sensors with limited battery power are bottlenecked to prolong the life of the network due to the data acquisition and processing. Thus majority of current research work is targeted towards finding different solutions to provide a better idea for maximization of network life [18,19]. As more energy being consumed during communication process the author in this article has followed multi-hop clustering approach and by using the concept of sleep based scheduling the sink can collect data from each node which results in network life enhancement.…”

“…The quadcopter takes an alternate path and flies by changing the motor speed when an obstacle is detected in the medium. The purpose of this section is to calculate the relationships between the thrust forces created by each propeller and the quadcopter's different movements [7,19]. The spatial orientation can be described as the combination of three angles: roll, pitch and yaw.…”