Open AccessPredictive model for battery life in IoT networks
Author(s) -
Reddy Maddikunta Praveen Kumar,
Srivastava Gautam,
Reddy Gadekallu Thippa,
Deepa Natarajan,
Boopathy Prabadevi
Publication year - 2020
Publication title -
iet intelligent transport systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.579
H-Index - 45
eISSN - 1751-9578
pISSN - 1751-956X
DOI - 10.1049/iet-its.2020.0009
Subject(s) - battery (electricity) , internet of things , computer science , random forest , set (abstract data type) , curse of dimensionality , machine learning , artificial intelligence , real time computing , embedded system , power (physics) , physics , quantum mechanics , programming language
The internet of things (IoT) is prominently used in the present world. Although it has vast potential in several applications, it has several challenges in the real‐world. One of the most important challenges is conservation of battery life in devices used throughout IoT networks. Since many IoT devices are not rechargeable, several steps to conserve the battery life of an IoT network can be taken using the early prediction of battery life. In this study, a machine learning based model implementing a random forest regression algorithm is used to predict the battery life of IoT devices. The proposed model is experimented on ‘Beach Water Quality – Automated Sensors’ data set generated from sensors in an IoT network from the city of Chicago, USA. Several pre‐processing techniques like normalisation, transformation and dimensionality reduction are used in this model. The proposed model achieved a 97% predictive accuracy. The results obtained proved that the proposed model performs better than other state‐of‐art regression algorithms in preserving the battery life of IoT devices.