A Survey of Crowd Sensing Opportunistic Signals for Indoor Localization <s> The State-of-the-Art Solutions <s> Redpin is a fingerprint-based indoor localization system designed and built to run on mobile phones. The basic principles of our system are based on known systems like Place Lab or Radar. However, with Redpin it is possible to consider the signal-strength of GSM, Bluetooth, and WiFi access points on a mobile phone. Moreover, we devised methods to omit the time-consuming training phase and instead incorporate a folksonomy-like approach where the users train the system while using it. Finally, this approach also enables the system to expeditiously adapt to changes in the environment, caused for example by replaced access points. <s> BIB001 </s> A Survey of Crowd Sensing Opportunistic Signals for Indoor Localization <s> The State-of-the-Art Solutions <s> Most current methods for 802.11-based indoor localization depend on surveys conducted by experts or skilled technicians. Some recent systems have incorporated surveying by users. Structuring localization systems "organically," however, introduces its own set of challenges: conveying uncertainty, determining when user input is actually required, and discounting erroneous and stale data. Through deployment of an organic location system in our nine-story building, which contains nearly 1,400 distinct spaces, we evaluate new algorithms for addressing these challenges. We describe the use of Voronoi regions for conveying uncertainty and reasoning about gaps in coverage, and a clustering method for identifying potentially erroneous user data. Our algorithms facilitate rapid coverage while maintaining positioning accuracy comparable to that achievable with survey-driven indoor deployments. <s> BIB002
8.1. Redpin BIB001 . Redpin is one of the earliest signal based indoor localization solutions, which proposes to incorporate user participation to build fingerprints rather than depending on designated and time-consuming training process. Redpin developed an adaptive indoor localization system involving GSM, Wi-Fi, and Bluetooth signals. Users could contribute without much effort while at the same time guarantee roomlevel accuracy. The Redpin system consists of two components: the Sniffing component is designed to gather various wireless signals in range to build fingerprints and the Locator component contains algorithm to locate a user using distance in signal domain. User interacts with Redpin in the following way: after sniffing process, if a user could be located by the system with the signal measurement he/she uploaded, the user will be informed of his/her current location; otherwise the user will be prompted to name his/her current location. The performance of the system was evaluated by conducting localization experiment with 10 rooms and 9 of the rooms were recognized correctly in result, which means an accuracy of about 90%. BIB002 . OIL targeted at organic room-level localization to achieve which users need to integrate with OIL system to make binds for rooms and corresponding Wi-Fi fingerprints.