IoT Solutions with Multi-Sensor Fusion and Signal-Image Encoding for Secure Data Transfer and Decision Making

TL;DR

This paper presents a multi-sensor fusion and signal-to-image encoding approach for IoT data, enabling secure transfer and improved decision-making, especially in military applications involving wearable devices and gesture recognition.

Contribution

It introduces a novel signal-to-image encoding method for IoT data fusion and demonstrates its effectiveness with deep learning models for anomaly detection and gesture recognition.

Findings

Successful data fusion from heterogeneous sensors

Effective signal-to-image encoding for visualization

Deep learning models support gesture and anomaly detection

Abstract

Deployment of Internet of Things (IoT) devices and Data Fusion techniques have gained popularity in public and government domains. This usually requires capturing and consolidating data from multiple sources. As datasets do not necessarily originate from identical sensors, fused data typically results in a complex data problem. Because military is investigating how heterogeneous IoT devices can aid processes and tasks, we investigate a multi-sensor approach. Moreover, we propose a signal to image encoding approach to transform information (signal) to integrate (fuse) data from IoT wearable devices to an image which is invertible and easier to visualize supporting decision making. Furthermore, we investigate the challenge of enabling an intelligent identification and detection operation and demonstrate the feasibility of the proposed Deep Learning and Anomaly Detection models that can support future application that utilizes hand gesture data from wearable devices.