# Towards Smart Wireless Body-Centric Networks

## TL;DR

This paper demonstrates that wireless body-centric channels exhibit long-memory properties, such as power-law autocorrelation decay and high Hurst exponents, which can enhance predictive analysis in human-centered wireless networks.

## Contribution

It provides the first experimental evidence of long-range dependence in body-centric channels using real-world data from multiple BANs.

## Key findings

- Channels show power-law autocorrelation decay
- Channels have Hurst exponent > 0.5 on average
- LRD properties can enable autonomous sensing and decision-making

## Abstract

We investigate the existence of 'long-memory' or long-range dependence (LRD) of the wireless body-centric channels, e.g., on-body, body-to-body (B2B), with real-life experimental dataset collected from 10 co-located wireless body area networks or BANs (people fitted with wearable sensors). We examine two different factors on that purpose such as: the pattern of the decaying autocorrelation function (ACF) and the Hurst exponent. From the experimental outcome, we show that, the ACF decay of the body-centric channels follows a power-like decay and the channels have a Hurst exponent much greater than 0.5 on average. These results indicate that the body-centric channels can possess long-memory or LRD characteristic which can be used for predictive analysis and intelligent decision making to build futuristic wireless human-centered networks that can sense and act autonomously. We also clarify whether the presence of the LRD property is sufficient for reliable prediction of the body-centric channels.