An environmental-adaptive Wi-Fi localization approach with low start-up cost for the exhibition industry

With the prevalence of smartphones, many new business opportunities are being spawned by exploiting the locations or routes of the users. We propose a probabilistic Wi-Fi localization approach to meet the special needs of the exhibition industry: tight schedules, large venues and dynamic environment. Unlike the popular fingerprinting approach which requires extensive training time and the need for retraining, our approach has a much lower start-up cost which means that it doesn't need the labor-intensive training phase while having comparable accuracy for localization. This is achieved by employing a real-time calibration of signal degradation model, which also makes our localization model be adaptive to the dynamic environment. We also propose practical optimization techniques which can dynamically control the computational cost during device localization and hence more devices can be tracked. Experiments with our testbed show that our proposed approach can achieve acceptable and stable localization results in different environment based on real-time calibration. From other tests of our system in real-world exhibition event held in a venue having of 4,000 square meters, over half of the localization errors are within 4.5 meters.