Zero-Configuration Indoor Positioning System Based On RF Signal Strength

Wireless sensor networks (WSNs) have become a research highlight inthe field of information technology. Thanks to the comprehensive sensortechnique and embedded computing technology, distributed networks atlarge scales which are capable of self-organization and self-configurationthrough wireless communication can be efficiently constructed. Only whenthe data is associated with the location information throughout thedata-centric sensor networks will the inverse mapping, i.e. interaction be-tween information field and entity world be achieved. As a key supportingtechnology in WSNs, positioning exerts enormous influences on coveragedeployment, topology control, routing communication, target tracking and soon.Considering the application-specific characteristic of WSNs, extensiveresearch into indoor positioning is not only essential but also pressing. In-door environment differs from outdoor situation in more complicated influ-encing factors and more abstruse underlying wireless-communication-relatedcharacteristics. Therefore indoor positioning is undoubtedly important anddifficult as well. In this paper, with the insight of complex indoor signalcharacteristics on mind, after racking through plenty of literature closely re-lated to wireless communication, we assess both pros and cons of diversesystem respectively. Eventually we propose this zero-configuration indoorpositioning system based on the difference of WSNs signal strength.Utilizing the comparison between numerous indoor and outdoor experi-ment results, we extract a number of indoor signal characteristics, and pro-pose a signal-strength-discrepancy-based zero-building distributed adaptivepositioning algorithm, i.e. iNemo, iNemo's work flow can be partitioned into3 steps: firstly makes a matching-based comparison between target signaland beacon signal; secondly calculates the signal strength difference whosenegative exponential function is used as weighted coefficients; thirdly esti-mates target location by weighted centroid algorithm.Designing for heterogeneous, hierarchical and network-based experi-mental system are accomplished. Bottom tier of the system is mostly realizedwhich serves to achieve optimal management mechanism and provide basestation GUI and data processing work. Other affiliated issues are includedand taken into full considerations, such as the coordination among the nodes based on certain management strategy, configuration and maintenance forthe infrastructure as well as the mechanism built upon it, and the occurrencepossibility of abnormalities.Sensor nodes loaded with this algorithm are deployed under differentindoor circumstances in a 100-m~2 region with 4 beacons. The results showthe system can achieve a precision of 2m error-in-mean, while 90% of thepositioning results lie in the error lower than 3m, which exhibits certainadaptability of circumstance. Moreover, the system is easy to deploy withouta manually-configured signal map, and is particularly cutting out for applica-tions such as medical care.