DecaFriend: Serverless indoor localization system on mobile phone platform

Abstract

Mobile phone is now becoming a necessary gadget in our daily life. Mobile applications have been proposed to advance the function of mobile phone. Location service application is a kind of mobile application that provides location information such as current location of the mobile phone. GPS is the popular technology for location service on mobile phone because many models of mobile phone are embedded with GPS sensor. Although GPS can provide high accuracy over outdoor scenario, GPS cannot provide good accuracy in indoor environment because the GPS signal from GPS satellites is blocked by the structure of building. The indoor localization systems have been proposed to address the above-mentioned problem. Many indoor localization systems require costly back-end server to process location. Moreover, the devices need to connect to the back-end servers to request the service. In this paper, we propose a serverless indoor localization system for mobile phone platform. The proposed system does not need any support from back-end server. The localization is processed on mobile phone. The system is developed as an Android application, utilizing Wi-Fi signal to determine the current location of mobile phone and uses mobile ad hoc network to disseminate the current location to other users in the system. The proposed system achieves accuracy up to 83.43% when the system is deployed in the twofloors building, which is the experimental site.

FloorLoc-SL: Floor Localization System with Fingerprint Self-Learning Mechanism

“…This objective of this module is to provide a current activity state of mobile phone user for floor counting module. Due to the fact that FloorLoc-SL is a localization system on mobile phone platform without any support from back-end server, the activity classification hit ← 0 (6) for each access point from top ap list do (7) if access point is in ap list of floor fingerprint then (8) hit ← hit + 1 (9) end if (10) end for (11) if ℎ MAX = hit do (12) current floor level ← UNKNOWN (13) else if ℎ MAX < hit do (14) ℎ (2) floor fingerprint ← fingerprint of floor in fingerprint database (3) else do (4) floor level ← floor (5) ap list ← null (6) floor fingerprint ← floor level, ap list (7) end if (8) for each access point from top ap list do (9) if access point is not in ap list of floor then (10) ap list.add(access point) (11) end if (12) end for Algorithm 2: Fingerprint algorithm.…”

“…After initializing this parameter, the algorithm waits until a difference of atmospheric pressure can reach to the thresholds for one-floor (6) previous pressure ← current pressure (7) activity state ← DOWN (8) else if Δ ≤ up then (9) previous pressure ← current pressure (10) activity state ← UP (11) else then (12) activity state ← UNCHANGED (13) end if (14) ← + window size (15) end for Algorithm 4: Activity classification using atmospheric pressure approach. …”

“…Many researchers have proposed indoor localization systems applying several technologies such as Wi-Fi signal [1, 3, 4, 7, 9, 10, 12], acoustic background [2], inertial sensor on mobile phone [15], and magnetic distortion [11]. The indoor localization system can be classified into two types: model-based approach and fingerprint-based approach.…”

“…Many indoor localization systems have been proposed to locate a mobile device in a building [1][2][3][4][5][6][7][8][9][10][11][12]. The previously proposed systems apply fingerprint-based approach [1-4, 6, 7, 9-12].…”

“…Many indoor localization systems have been proposed to locate a mobile device in a building [1–12]. The previously proposed systems apply fingerprint-based approach [1–4, 6, 7, 9–12]. The concept is that the different location in the building has a different signal characteristic.…”