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

arXiv: https://arxiv.org/abs/2106.01497 · HTML: https://mlsanity.com/paper/2106.01497/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 43 references that are in the map.
- First posted on arXiv 2 Jun 2021.

## Builds on (most cited first, 3 of 3)

- [Transforming Sensor Data to the Image Domain for Deep Learning - an Application to Footstep Detection](https://mlsanity.com/paper/1701.01077/) (2017)
- [How do you Command an Army of Intelligent Things?](https://mlsanity.com/paper/1712.08976/) (2017)
- [Blockchain of Things (BCoT): The Fusion of Blockchain and IoT Technologies](https://mlsanity.com/paper/1910.06898/) (2019)

## Built on by (most cited first, 3 of 3)

- [Survey of Recent Multi-Agent Reinforcement Learning Algorithms Utilizing Centralized Training](https://mlsanity.com/paper/2107.14316/) (2021)
- [Strategic Maneuver and Disruption with Reinforcement Learning Approaches for Multi-Agent Coordination](https://mlsanity.com/paper/2203.09565/) (2022)
- [Emergent Behaviors in Multi-Agent Target Acquisition](https://mlsanity.com/paper/2212.07891/) (2022)

## Abstract (arXiv)

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.