Context-Aware Energy Management System for Data Acquisition in Wireless Sensor Networks

Abstract

Energy management for wireless sensor network (WSN) has become a critical research interest mainly due to its energy constraint. A typical WSN node has three major power consuming sub-units namely; data acquisition, computation and communication. The amount of energy consumed by these sub-units can be minimized through the use of energy management techniques and algorithms. However, this research is aimed at designing a Context-aware energy management system for WSN with focus on the data acquisition sub-unit. A Context-aware and Energy-efficient Data Acquisition Reconfiguration Algorithm (CAEEDARA) was developed with context limited to the node available battery energy and sampled inputs obtained. The context information along with the obtained input characteristics forms the basis for node reconfiguration behavior decision which includes varying sampling frequency and computing sampling interval. The WSN node architecture include several low-power components such as gas sensors for monitoring carbon dioxide CO2, methane CH4 and nitrogen dioxide NO2 (Gascard NG and Graphene-based) interfaced with a microcontroller unit (MSP430F2272) for processing acquired data samples, a ZigBee module (ZE51-2.4) remote data communication and transmission. The developed CAEEDARA algorithm was domicile at the base station (BS) due to its access to unlimited energy. The operation of the developed CAEEDARA algorithm was simulated using MatLab 2018b running on a Dell Intel Core i3 processor, and the performance evaluation metric was based on its duty cycle and energy consumed. Simulation results showed that the CAEEDARA algorithm saves about 80% of the node battery energy thus resulting in a prolong node life.