An efficient wireless access point selection algorithm for location determination based on RSSI interval overlap degree determination

Optimally choosing wireless Access Points (APs) as urban areas become more densely packed with them becomes increasingly challenging. In WiFi-based Indoor Positioning Systems (IPS), Selecting Wireless Access Point (AP), namely, WiFi routers, is significant as the more APs that are selected, the higher the computation, energy and time cost. This is unsuitable for networking low-resource devices as part of an Internet of Things. In addition, selecting the optimum number of APs not only reduces redundant information but also improves the positioning accuracy. In this paper, we present a novel AP selection method that uses the RSSI Interval Overlap Degree (IOD) to discriminate between known location Reference Points. We validated our algorithm in an office-like indoor space at a Queen Mary computer science lab. The results show that our algorithm has an improved performance, which is 13.6%, 18.2%, and 7.6% better than IG (information gain), MI (mutual information), SD (standard deviation) used as baseline algorithms, respectively.

[1]  Hao Jiang,et al.  A mutual information based online access point selection strategy for WiFi indoor localization , 2015, 2015 IEEE International Conference on Automation Science and Engineering (CASE).

[2]  Arun Venkataramani,et al.  Energy consumption in mobile phones: a measurement study and implications for network applications , 2009, IMC '09.

[3]  J. W. S. Liu,et al.  Building/Environment Data/Information Enabled Location Specificity and Indoor Positioning , 2017, IEEE Internet of Things Journal.

[4]  Andreas Zell,et al.  Indoor Positioning via Three Different RF Technologies , 2008 .

[5]  Jochen Schiller,et al.  Location Based Services , 2004 .

[6]  Yeong-Sheng Chen,et al.  Efficient localization scheme based on coverage overlapping in wireless sensor networks , 2010, 2010 5th International ICST Conference on Communications and Networking in China.

[7]  Kegen Yu,et al.  Improved Wi-Fi RSSI Measurement for Indoor Localization , 2017, IEEE Sensors Journal.

[8]  Yiqiang Chen,et al.  Power-efficient access-point selection for indoor location estimation , 2006, IEEE Transactions on Knowledge and Data Engineering.

[9]  Wei Zhang,et al.  Radius based domain clustering for WiFi indoor positioning , 2017 .

[10]  Moustafa Youssef,et al.  WLAN location determination via clustering and probability distributions , 2003, Proceedings of the First IEEE International Conference on Pervasive Computing and Communications, 2003. (PerCom 2003)..

[11]  Dejian Meng,et al.  A reflective context-aware system for spatial routing applications , 2008, MPAC '08.

[12]  I. Oppermann,et al.  Performance of UWB position estimation based on time-of-arrival measurements , 2004, 2004 International Workshop on Ultra Wideband Systems Joint with Conference on Ultra Wideband Systems and Technologies. Joint UWBST & IWUWBS 2004 (IEEE Cat. No.04EX812).

[13]  Stefan Poslad,et al.  ERSP: An Energy-Efficient Real-Time Smartphone Pedometer , 2013, 2013 IEEE International Conference on Systems, Man, and Cybernetics.

[14]  Chiu-Ching Tuan,et al.  An AP Selection with RSS Standard Deviation for Indoor Positioning in Wi-Fi , 2015, 2015 9th International Conference on Innovative Mobile and Internet Services in Ubiquitous Computing.

[15]  Chi Guo,et al.  A Lane-Level LBS System for Vehicle Network with High-Precision BDS/GPS Positioning , 2015, Comput. Intell. Neurosci..

[16]  Stefan Poslad,et al.  Using a Smart City IoT to Incentivise and Target Shifts in Mobility Behaviour—Is It a Piece of Pie? , 2015, Sensors.

[17]  R. Bharat Rao,et al.  Evolution of mobile location-based services , 2003, CACM.

[18]  P. Cochat,et al.  Et al , 2008, Archives de pediatrie : organe officiel de la Societe francaise de pediatrie.

[20]  Shih-Hau Fang,et al.  A Group-Discrimination-Based Access Point Selection for WLAN Fingerprinting Localization , 2014, IEEE Transactions on Vehicular Technology.

[21]  Stefan Poslad,et al.  Indoor Location and Orientation Determination for Wireless Personal Area Networks , 2009, MELT.