Extreme RSS Based Indoor Localization for LoRaWAN With Boundary Autocorrelation

The received signal strength (RSS) fingerprint-based approaches are widely used for indoor location-based services. The emerging Long Range Wide Area Network (LoRaWAN) is a cost-effective solution for indoor latency-tolerant location-based services attributed to its long-range property. In general, there are serious RSS fluctuations due to fadings along the communication path, thus significantly jeopardizing the localization accuracy. To overcome the challenge, we propose the extreme RSS to stabilize the fingerprint database and formulate boundary autocorrelation to downsize tremendously the searching complexity and thus proliferating localization accuracy. In essence, the RSS fluctuations are modeled as a Bernoulli random process so that the RSS stability can be estimated by a newly defined fluctuation analytic function. To mitigate the impact of the perturbative fluctuation, the extreme RSS is further defined to cultivate a highly stable and robust fingerprint database which withstand environmental dynamics. In addition, boundary autocorrelation is developed to measure and compare the similarity between the measured RSS values versus the prestored fingerprint database. RSS values with low autocorrelation coefficients are eradicated from the typically lengthy searching. The downsized complexity significantly improves the localization accuracy. Experiments were carried out and the results revealed that the proposed method achieved sub-10-meter localization accuracy in indoor environments. Such accuracy is encouraging and superior in contemporary LoRaWAN measurements.