InferLoc: Calibration Free Based Location Inference for Temporal and Spatial Fine-Granularity Magnitude

Abstract

Location is the most important information in the field of context-aware computing. Normally, one location represented as absolute physical coordinate is less understandable than semantically meaningful place like "home", "office", etc. This paper proposes a novel calibration free based algorithm called InferLoc to infer user's daily significant locations using Wi-Fi signals obtained from mobile phone. InferLoc contains three main steps: 1) Stop point detection based on trajectory segmentation through similarity calculation between neighbor sampling windows; 2) Location discovery through density based clustering and 3) Semantically significant location inference through matching clustered locations and recorded places in personal diary. Furthermore, we implement and validate InferLoc algorithm on realistic data collected from real-world wireless environment. Experimental results show that InferLoc can recognize visiting locations both in temporal and spatial fine-granularity magnitude under short response delay.

Real-Time and Accurate Indoor Localization with Fusion Model of Wi-Fi Fingerprint and Motion Particle Filter

“…With each smartphone, we scan the Wi-Fi for 10 times (Test. [1][2][3][4][5][6][7][8][9][10]. We can conclude that every time we collect the Wi-Fi signals, it will take more than 1 second on average.…”

“…As the basic support of Indoor LBS application, a timely localization with high-accuracy and low power consumption is very important. At present, one of the most popular techniques is Wi-Fi fingerprint based location [3][4][5][6][7][8]. References [3][4][5][6][7][8] take use of the existing wireless network infrastructures to avoid extra deployment costs.…”

“…It becomes easy to offer indoor location services without any other infrastructure investment. A common idea of Wi-Fi based indoor location is fingerprint method [3][4][5][6][7][8]. It is a classical classification problem where different supervised machine learning techniques have been used.…”

Voronoi-clustering for plane data

“…Clustering [23], [22], [27], [18], [1] have been actively explored for pattern recognition, such as speech recognition, character recognition, etc., and the machine learning techniques have been employed in various applications, including image retrieval [20], [33], [17], multimedia [15], [25], [30], [16], [24], [32], [11] and mobile applications [6], [3], [4], [8], [34], [7], [31], [5]. Specifically, the clustering algorithm of machine learning is mainly applied for image segmentation, computer vision and multimedia applications [14], [13].…”