Patterns in the RSSI traces from an indoor urban environment

Abstract

Urban environments are notorious for their high spectrum usage, particularly in their unlicensed radio bands. Wireless sensor network (WSN) nodes incorporate modern transceivers that can measure the background noise/interference and change channels. These combined capabilities suggest the need to better understand urban environments so that nodes can better avoid competing devices. In this paper, we explore the noise and interference patterns found on 256 frequencies in an indoor urban environment's 900 MHz ISM and non-ISM bands. We begin the process by using off-the-shelf WSN hardware to sample the environment at 5 kHz from 16 locations simultaneously. From these samples, we identify five prevalent patterns and then hand-classify the 4096 traces of noise and interference. Finally, we extract a variety of statistics from the traces and use them in a Bayesian network classifier.