A Framework for Prediction and Storage of Battery Life in IoT Devices using DNN and Blockchain

TL;DR

This paper presents a framework combining deep neural networks and blockchain technology to predict and securely store battery life data for IoT devices, enhancing automation and trust in real-time sensor networks.

Contribution

It introduces a novel integrated approach using DNNs for battery life prediction and blockchain for secure data storage in IoT applications.

Findings

Accurate battery life prediction model for IoT sensors.

Blockchain ensures tamper-proof storage of predictive data.

Facilitates proactive maintenance planning for IoT deployments.

Abstract

As digitization increases, the need to automate various entities becomes crucial for development. The data generated by the IoT devices need to be processed accurately and in a secure manner. The basis for the success of such a scenario requires blockchain as a means of unalterable data storage to improve the overall security and trust in the system. By providing trust in an automated system, with real-time data updates to all stakeholders, an improved form of implementation takes the stage and can help reduce the stress of adaptability to complete automated systems. This research focuses on a use case with respect to the real time Internet of Things (IoT) network which is deployed at the beach of Chicago Park District. This real time data which is collected from various sensors is then used to design a predictive model using Deep Neural Networks for estimating the battery life of IoT sensors that is deployed at the beach. This proposed model could help the government to plan for placing orders of replaceable batteries before time so that there can be an uninterrupted service. Since this data is sensitive and requires to be secured, the predicted battery life value is stored in blockchain which would be a tamper-proof record of the data.