People Counting using Radio Irregularity in Wireless Sensor Networks -- An Experimental Study

TL;DR

This paper explores using radio irregularity-induced signal fluctuations in wireless sensor networks to develop a low-cost, effective method for indoor people counting, leveraging existing wireless signals instead of dedicated sensors.

Contribution

It introduces a novel approach that exploits radio irregularity phenomena for people counting, providing an alternative to traditional sensor-based systems.

Findings

Radio irregularity can be used to detect human presence.

Discriminant analysis improves counting accuracy.

The method offers a low-cost solution for indoor people counting.

Abstract

The Internet has grown into a large cyber-physical system centered that connects not just computer systems but a plethora of systems, devices, and objects, collectively referred to as "Things", giving rise to the term "Internet of Things" (IoT). It encompasses technologies for identification and tracking, sensing and actuation, both wired and wireless communications, and also, intelligence and cognition. Wireless communications, which is an integral part of IoT, suffers from radio irregularity -- a phenomenon referring to radio waves being selectively absorbed, reflected or scattered by objects in their paths, e.g., human bodies that comprises liquid, bone and flesh. Radio irregularity is often regarded as a problem in wireless communications but, with the envisioned pervasiveness of IoT, we aim to exploit radio irregularity as a means to detect and estimate the number of people. We demonstrate how radio signal fluctuations arising from radio irregularity, combined with discriminant analysis, can be used to provide a simple low-cost alternative to dedicated sensing systems for indoor people counting.