Research On ZigBee Nodes Using Environmental Characteristics And WiFi Radio Frequency Interference Fingerprinting

Accurate indoor localization has long been a hot research area in mobile computing.Traditional indoor localization technologies only use the Radio Signal Strength(RSS)information and only can localize the nodes with the same communication protocol.But in real scenario,since RSS is easily affected by the indoor environments,plus there are many types of nodes with different protocols,the application range is limited.To solve the above problems,our paper puts forward two approaches to localize the target object accurately.The first one is able to leverage various other resources plus RSS in localization.Besides the traditional RSS information,the environmental physical features,e.g.the light,temperature and humidity information,are all utilized for localization.After building a comprehensive fingerprint map for the above information,K Nearest Neighbors and Bayesian algorithm are utilized in localization.Experimental results show that the successful localization accuracy can dramatically outperform traditional pure RSS-based indoor localization method.The localization accuracy can be improved by 45%.The other approach is to localize different wireless nodes with different protocols based on the same communication frequency band.Due to the wide spread of WiFi,WiFi shares the same unlicensed 2.4GHz ISM spectrum with ZigBee.Our first try is to localize the ZigBee node using the WiFi signals interfered by ZigBee signals.The interfered WiFi signals are described by the change of Channel State Information(CSI).Our approach utilizes Principal Component Analysis(PCA),Dynamic Time Warping(DTW)and K Nearest Neighbor algorithms to localize the target.We performed our experiments in different indoor scenarios.The experimental results show that the system that can localize different wireless nodes with different communicating protocols.The localization accuracy can achieve meter level.