Abstract
Indoor localization has been gradually improving over the past decade, utilizing emerging and proliferating wireless technologies. Since its inception, WiFi technology has been increasingly penetrating indoor environments, residential, and nonresidential. Becoming ubiquitous, WiFi was considered a fertile subject for use in indoor localization utilizing the wireless access points that predominantly provide network coverage in indoor environments. As the Internet of Things (IoT) technology is speeding towards pervasiveness, IoT devices can offer substantial aid to improve accuracy, which is at the heart of the indoor localization process. In this paper, we provide a preliminary study of the feasibility of using WiFi-enabled devices, including IoT devices, to improve location signature in indoor localization besides the traditionally used wireless access points. Through field experiments, we provide insights into the coverage and penetration of such devices in different domains characterized by the activities exercised in them. Our conclusions highlight the challenges of incorporating such devices into the fingerprint-based indoor localization. Additionally, we focus on the use of machine learning approaches that recently gained momentum in indoor localization research.
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