# Analog Signal Compression and Multiplexing Techniques for Healthcare Internet of Things

## TL;DR

This paper introduces an analog domain signal compression and multiplexing method for healthcare IoT that reduces power consumption and device count by combining signals and using a novel modulation technique, with promising simulation results.

## Contribution

It proposes a fully analog multi-signal compression and encoding method based on AJSCC and a new FPMM multiplexing technique for healthcare IoT applications.

## Key findings

- Reduces power consumption by avoiding ADCs
- Enhances security and interference robustness with FPMM
- Achieves low error rates in simulations

## Abstract

Scalability is a major issue for Internet of Things (IoT) as the total amount of traffic data collected and/or the number of sensors deployed grow. In some IoT applications such as healthcare, power consumption is also a key design factor for the IoT devices. In this paper, a multi-signal compression and encoding method based on Analog Joint Source Channel Coding (AJSCC) is proposed that works fully in the analog domain without the need for power-hungry Analog-to-Digital Converters (ADCs). Compression is achieved by quantizing all the input signals but one. While saving power, this method can also reduce the number of devices by combining one or more sensing functionalities into a single device (called 'AJSCC device'). Apart from analog encoding, AJSCC devices communicate to an aggregator node (FPMM receiver) using a novel Frequency Position Modulation and Multiplexing (FPMM) technique. Such joint modulation and multiplexing technique presents three mayor advantages---it is robust to interference at particular frequency bands, it protects against eavesdropping, and it consumes low power due to a very low Signal-to-Noise Ratio (SNR) operating region at the receiver. Performance of the proposed multi-signal compression method and FPMM technique is evaluated via simulations in terms of Mean Square Error (MSE) and Miss Detection Rate (MDR), respectively.