Analog Signal Compression and Multiplexing Techniques for Healthcare Internet of Things

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.

Embedding ML Algorithms onto LPWAN Sensors for Compressed Communications

“…Data are also usually quantized to round the measures to significant approximations requiring fewer bits for coding. They are often aggregated ( [8] or [9]).…”

Simple Measurement System for Biological Signal Using a Smartphone

“…And comparing the stationary type measurement system the number of channels is only one in this situation. However, this disadvantage will be able to overcome the signal multiplexing techniques, which were proposed in the past [12], [13].…”

Signal Recovery Performance Analysis in Wireless Sensing with Rectangular-Type Analog Joint Source-Channel Coding

“…The original analytical derivations of rectangular-type AJSCC are done in this work for for two SNR conditions, high SNR and medium/low SNR. The high SNR derivations was presented in our work [9], and in this journal revision the comprehensive analysis and derivations on medium/low SNR are presented based on the complete listings of all the signal variation cases on the three-dimensional mapping curve of the rectangular-type AJSCC. To address the second research challenge, MSE evaluation results are provided with varying number of parallel lines in the rectangular-type AJSCC for both analog sensing and low-resolution digital sensing.…”