A Simple but Accurate Estimation of Residual Energy for Reliable WSN Applications

Abstract

A number of studies have been actively conducted to address limited energy resources in sensor systems over wireless sensor networks. Most of these studies are based on energy-aware schemes, which take advantage of the residual energy from the sensor system's own or neighboring nodes. However, existing sensor systems estimate residual energy based solely on voltage and current consumption, leading to inaccurate estimations because the residual energy in real batteries is affected by temperature and load. This misinformation makes a complete nonsense of existing energy-aware research, which is not allowed in reliable WSN applications. In this study, therefore, an efficient residual-energy estimation scheme is proposed in consideration of not only the voltage but also the temperature and load characteristics of batteries. The performance of the proposed scheme was verified through an experiment and simulations in the actual environment, and its effect gets more notable when the scale of the WSN goes larger.

An analytical model to estimate the state of charge and lifetime for batteries with energy harvesting capabilities

“…In such a condition, it is noticeable that the output voltage is mainly affected by current intensity ( I ) but it may also be influenced by temperature variations . From Figure , note that although the experimental data induced battery stress by draining (+ I ) and injecting (− I ) high‐intensity currents (−4.5 A ≤ I ≤ +4.5 A), the proposed model can track such changes satisfactorily.…”

QoS-Aware Energy Management and Node Scheduling Schemes for Sensor Network-Based Surveillance Applications

“…The software-based measurement schemes consider different factors such as reduction in sensing range and packet throughput. In a software-based scheme proposed by Kim et al [35], temperature and load characteristics are also taken into account for measuring the overall energy consumption. The transmission and reception quality of sensor nodes (in terms of total number of packets transmitted and received) is considered as a metric to measure energy consumption by Khriji et al [37].…”

A Channel-Aware Routing Protocol With Nearest Neighbor Regression For Underwater Sensor Networks

“…With ACK packets and the corresponding received SNR values, nodes in initialization process construct the list of SNR vectors described in Section III-A. Besides the list of SNR vectors, we utilize residual energy which can be obtained in a variety of ways [29] [30], historical SNR means, and variances as auxiliary parameters for best forwarder selection. After the initialization process, each underwater node has a list of neighbor nodes information, including received SNR vectors, latest residual energy and historical SNR means and variances, then changes to the listening process.…”