A Survey of Crowd Sensing Opportunistic Signals for Indoor Localization <s> SmartSLAM <s> The proliferation of Wi-Fi infrastructures has facilitated numerous indoor localization techniques using Wi-Fi location fingerprints. They make it possible to identify a room or a place in urban environment, which is especially important in enabling many interesting location-based services. As there are too many rooms and places such as cafes and restaurants to be recognized in urban environment, the crowdsourcing approach has been proposed to collect Wi-Fi location fingerprints based on user participation. However, its actual deployment in a large-scale urban environment presents numerous design and implementation challenges due to urban characteristics such as a large crowd, dense region, and device diversity. This paper presents Elekspot system, whose design goal is to support system scalability, device heterogeneity, robustness against lack of contributions, and localization accuracy. Through several experiments and implementation of actual applications targeting urban places we confirmed that the architecture and methods of Elekspot can effectively meet the design goals. <s> BIB001 </s> A Survey of Crowd Sensing Opportunistic Signals for Indoor Localization <s> SmartSLAM <s> Many indoor localization techniques that rely on RF signals from wireless Access Points have been proposed in the last decade. In recent years, research on crowdsourced (also known as “Organic”) Wi-Fi fingerprint positioning systems has been attracting much attention. This participatory approach introduces new challenges that no previously proposed techniques have taken into account. This paper proposes “FreeLoc”, an efficient localization method addressing three major technical issues posed in crowdsourcing based systems. Our novel solution facilitates 1) extracting accurate fingerprint values from short RSS measurement times 2) calibration-free positioning across different devices and 3) maintaining a single fingerprint for each location in a radio map, irrespective of any number of uploaded data sets for a given location. Through experiments using four different smartphones, we evaluate our new indoor positioning method. The experimental results confirm that the proposed scheme provides consistent localization accuracy in an environment where the device heterogeneity and the multiple surveyor problems exist. <s> BIB002
8.10. FreeLoc BIB002 . The main goal of FreeLoc is to investigate how to achieve efficient WiFi-based localization in an environment where device heterogeneity and multiple surveyors exist. To address these issues, the authors devised a novel KeyValue fingerprint data structure with a parameter , where Key denotes a specific BSSID and Value is a vector containing BSSIDs of which RSS is weaker than the Key. This relative representation of RSS from APs along with not only makes the system immune to device diversity but also increase similarity between fingerprints collected at slightly different place, which enable merging Value factors for the same Key under multisurveyor circumstances. Wi-Fi fingerprint data was gathered at about 70 different locations in a building with 4 different devices. The result shows that cross device error is less than 2 m for hallway 4 m for laboratory. BIB001 . Elekspot is a platform that enables urban indoor environment localization via crowdsourcing. The system is designed to support several major issues (inevitable problems) in crowdsourcing framework: system scalability, device heterogeneity, and robustness of lack of contribution. A different method is proposed to deal with each of these design goals, respectively. Specifically, a method named SSBIn which makes inverted index for only BSSIDs with top n strongest RSS strength instead of all BSSIDs in fingerprint is introduced to reduce time in retrieving too many fingerprint and thus enable scalability. To support device diversity, the authors propose to obtain linear relations between fingerprints from different devices automatically based on contributions in the same location and keep updating them. Finally, they suggest using confidence value to denote reliability instead of position error distance.