Design Of Self Sustained Smart Ultra Low Power Circuits For Iot Application

Abstract

The lifetime and power management of Internet-of-Things (IoT) devices are a concern newlineas IoT usage for diverse applications grows in popularity. In this thesis, a model newlinepredictive method for assessing wireless sensor network lifetime and cluster head newlineselection is presented. The dynamic parameters of a wireless sensor network are newlinecollected using a simulation tool (Smart Mesh IP Power and performance calculator). newlineIn order to integrate clustering and the best routing protocol, this study takes into newlineaccount machine learning. The hop depth, advertising, number of Motes, backbone, newlinerouting, reporting interval, payload size, downstream frame size, supply voltage, and newlinepath stability are the predictors, and the current consumption, data latency, and build newlinetime are the response variables to establish the models for estimating the power and newlineperformance of the network. The residual energy in each node, distance from the base newlinestation, and data transmission rate are the predictors, and the priority of the cluster head newlineis the response variable to establish models for achieving the best routing path in a newlineWireless Sensor Network (WSN). The standard tree, Support Vector Machine (SVM), newlineEnsemble, and Gaussian Process Regression (GPR) models for lifetime estimation are newlineanalyzed in comparison with the Smart Mesh IP tool, and the models for cluster head newlineselection outperforms the existing models in literature in terms of average energy of newlinenodes and number of dead nodes. This novel approach concentrates on the effect of newlinevarious dynamic parameters on network lifetime prediction. newlineResearchers globally are working on Indoor high-performance photovoltaics, which newlinecan power IoT devices. Here arises the need for experimentation with Indoor newlinePhotovoltaics (IPV) under varying interior lighting conditions that includes the newlineintegration of artificial light and daylight. This research focuses on experimental newlineverification of the IPV characterization under indoor lighting conditions, adopting the newlinerecent human-centric lighting challenges. A crucial requireme