Indoor device-free passive localization with DCNN for location-based services
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Indoor device‑free passive localization with DCNN for location‑based services Lingjun Zhao1 · Chunhua Su1,5 · Zeyang Dai1 · Huakun Huang1 · Shuxue Ding2,3 · Xinyi Huang4 · Zhaoyang Han1
© Springer Science+Business Media, LLC, part of Springer Nature 2019
Abstract With the increasing demand of indoor location-based services, such as tracking targets in a smart building, device-free localization technique has attracted great attentions because it can locate the targets without employing any attached devices. Due to the limited space and complexity of the indoor environment, there still exist challenges in terms of high localization accuracy and high efficiency for indoor localization. In this paper, for addressing such issues, we first convert the received signal strength (RSS) signals into image pixels. The localization problem is then formulated as an image classification problem. To well handle the variant RSS images, a deep convolutional neural network is then structured for classification. Finally, for validating the proposed scheme, two real testbeds are built in the indoor environments, including a living room and a corridor of an apartment. Experimental results show that the proposed scheme achieves good localization performance. For example, the localization accuracy can reach up to 100% in the scenario of living room and 97.6% in the corridor. Moreover, the proposed approach outperforms the methods of the K-nearest-neighbor and the support vector machines in both the noiseless and noisy environments. Keywords Device-free localization · Internet of things · Image · Classification · Convolutional neural network · Location information service · Indoor
* Lingjun Zhao [email protected] * Chunhua Su chsu@u‑aizu.ac.jp * Shuxue Ding [email protected] Extended author information available on the last page of the article
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1 Introduction Location-based services (LBS) are becoming more and more important in the era of Internet of things (IoT) for modern life [29]. With this background, wireless localization techniques have drawn a great deal of attentions [5]. Specially, for some occasions, such as intruder detection in security safeguards, aging monitoring at a smart home, and patient tracking in a hospital, people may not expect to be equipped with the extra tracking devices. Thus, many current localization techniques, e.g., global positioning system (GPS) [27] or radio frequency identification (RFID) [18], may not be capable because they are device-based techniques [30]. To overcome this challenge, a passive wireless localization technology, devicefree localization (DFL), is proposed [2]. DFL has attracted extensive interest recent years because it is based on wireless sensor networks to locate target who does not equip with any attached devices [4, 11]. As shown in Fig. 1, in the IoT-based DFL system [14, 30], wireless sensors, termed as anchor points (AP), are used to collect location data by transmitting and receiving signals collaboratively. These transmitting-rec
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