Optimize power consumption in sensor node for wsn

Abstract

quotBackground: Energy consumption in wireless Sensor Node(SN) is the main concern for any senor node when it is deployed with the minimum power source and limited resources. Limited strategies consider the stochastic behavior of the system, especially the Sensor node and condition under which it functions. The techniques in literature studies indicate application of parameter tuning to improvise the application metrics but the process overlook the effect of new configurations on performance and other metrics for system as a whole. The new setting for reconfiguration are derived through computationally cumbersome techniques that involve derivatives, equations or models. Moreover, the reconfiguration is performed over-the-air with huge command files that incur communication cost. newline newlineAim: The aim of the research undertaken is to devise an efficient technique to improve the lifetime of a Sensor Node. The method must be lightweight and take into consideration parameters of all modules along with the effect of reconfiguration on system performance. newline newlineMaterials and Methods: For Hypothesis, an efficient low power consuming SN design is considered for Quantitative research to derive a technique for algorithmic implementations and simulations. Reconfiguration of the node, over the air by minimal command and self reconfiguration based on the conditions, are simulated through theoretical calculations using NI LabVIEW Software. Differential Evolution optimization is used for making SN self adapting to the surrounding circumstances for RF settings as well as simple search methods and statistical analysis is used for acquisition and MCU parameter optimization. newline newlineResults and Discussion: The proposed dynamic reconfigurations prove much efficient over the static settings in the SN. Dynamic Reconfiguration Algorithm using Differential Evolution for optimization helps conserving energy up to about 10.73% and added 15.47%(MCU) + 9.62%(ADC) conservation is achieved by applying Acquisition and MCU Parameter Optimization (AMPO). Hybrid Algor