Comparison of the Mechanisms of the Zigbee's Indoor Localization Algorithm

Localization has been an important issue along with the development of context-aware systems applicable to dynamic mobile environment. ZigBee is very cheap and less power consuming wireless techniques comparing to other types such as RFID, infrared and ultrasound. Thus this paper compares conventional techniques for predicting mobile objectpsilas location by combining estimates drawn from received signal strength (RSS) using ZigBee modules. We classify these mechanisms into two categories; map-based localization techniques, referring to database of predefined locations and their RSSI data; and distance-based localization using Markov chain inference. Our results show the relationship between RSSI and distance in indoor ZigBee environment and compare localization accuracy of those two techniques. We conclude that map-based localization is not suitable for flexible changes in indoors because of its predefined condition setup and lower accuracy comparing to distance-based Markov chain inference localization system.