The continuous both indoor and outdoor location of subjects is an essential capability within AAL systems. It enables adaptive and context-aware behavior within the services implemented. In AAL systems, location information has been managed in a simplistic way until now: i.e. it refers either to specific rooms or concrete coordinates within the elder’s home. Understanding it as specific rooms is the most usual approach. In this paper we argue that managing both information granularity levels simultaneously is also interesting within AAL. And we restrict the discussion to 802.11 fingerprinting based indoor location technologies. We present here how to systematically build and evaluate 802.11 fingerprinting based indoor location systems for both approaches and how to integrate them within a unique service. In regard to fingerprinting methods, managing both information levels simultaneously allows reducing deployment and maintenance cost for such technology.
O.Mohamed Badawy and M.A.Bani Hasan, Decision tree approach to estimate user location in wlan based on location fingerprinting, in: National Radio Science Conference, NRSC 2007, IEEE, 2007, pp. 1–10.
2.
P.Bahl and V.N.Padmanabhan, Radar: An in-building rf-based user location and tracking system, in: Proc. of the Nineteenth Annual Joint Conference of the IEEE Computer and Communications Societies, INFOCOM 2000, Vol. 2, IEEE, 2000, pp. 775–784.
3.
R.Battiti, A.Villani and T.LeNhat, Neural network models for intelligent networks: Deriving the location from signal patterns.
4.
C.M.Bishop, Neural Networks for Pattern Recognition, Clarendon Press, Oxford, 1995.
5.
L.Breiman, Bagging predictors, Technical report, Department of Statistics, University of California, Berkeley, California 94720, September 1994.
6.
L.Breiman, Random forests, Machine Learning45(1) (2001), 5–32.
7.
M.Brunato and R.Battiti, Statistical learning theory for location fingerprinting in wireless lans, Computer Networks47(6) (2005), 825–845.
8.
P.Castro, P.Chiu, T.Kremenek and R.Muntz, A probabilistic room location service for wireless networked environments, in: Ubicomp 2001: Ubiquitous Computing, Springer, 2001, pp. 18–34.
9.
Y.Chen, Q.Yang, J.Yin and X.Chai, Power-efficient access-point selection for indoor location estimation, IEEE Transactions on Knowledge and Data Engineering18(7) (2006), 877–888.
10.
G.F.Cooper and E.Herskovits, A Bayesian method for constructing Bayesian belief networks from databases, in: Proc. of the Seventh Conference on Uncertainty in Artificial Intelligence, Morgan Kaufmann Publishers Inc., 1991, pp. 86–94.
11.
G.F.Cooper and E.Herskovits, A Bayesian method for the induction of probabilistic networks from data, Machine Learning9(4) (1992), 309–347.
12.
C.Cortes and V.Vapnik, Support-vector networks, Machine Learning20(3) (1995), 273–297.
13.
K.Derr and M.Manic, Wireless based object tracking based on neural networks, in: 3rd IEEE Conference on Industrial Electronics and Applications, ICIEA 2008, IEEE, 2008, pp. 308–313.
14.
A.K.Dey, Providing architectural support for building context-aware applications, PhD thesis, College of Computing, Georgia Institute of Technology, December 2000.
15.
E.Elnahrawy, X.Li and R.P.Martin, The limits of localization using signal strength: A comparative study, in: 2004 First Annual IEEE Communications Society Conference on Sensor and Ad Hoc Communications and Networks, IEEE SECON 2004, IEEE, 2004, pp. 406–414.
16.
D.Fahed and R.Liu, Wi-fi-based localization in dynamic indoor environment using a dynamic neural network, International Journal of Machine Learning and Computing3(1) (2013).
17.
S.-H.Fang and T.-N.Lin, Indoor location system based on discriminant-adaptive neural network in ieee 802.11 environments, IEEE Transactions on Neural Networks19(11) (2008), 1973–1978.
18.
A.Farshad, J.Li, M.K.Marina and F.J.Garcia, A microscopic look at wifi fingerprinting for indoor mobile phone localization in diverse environments, in: International Conference on Indoor Positioning and Indoor Navigation, Vol. 28, 2013, p. 31.
19.
Y.Freund and R.E.Schapire, A short introduction to boosting, Journal of Japanese Society for Artificial Intelligence14(5) (September 1999), 771–780.
20.
Y.Freund, R.E.Schapireet al., Experiments with a new boosting algorithm, in: ICML, Vol. 96, 1996, pp. 148–156.
21.
T.Garcia-Valverde, A.Garcia-Sola, A.Gomez-Skarmeta, J.A.Botia, H.Hagras, J.Dooley and V.Callaghan, An adaptive learning fuzzy logic system for indoor localisation using wi-fi in ambient intelligent environments, in: 2012 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), IEEE, 2012, pp. 1–8.
22.
Y.Gwon, R.Jain and T.Kawahara, Robust indoor location estimation of stationary and mobile users, in: Twenty-Third Annual Joint Conference of the IEEE Computer and Communications Societies, INFOCOM 2004, Vol. 2, IEEE, 2004, pp. 1032–1043.
23.
V.Honkavirta, T.Perala, S.Ali-Loytty and R.Piché, A comparative survey of wlan location fingerprinting methods, in: 6th Workshop on Positioning, Navigation and Communication, WPNC 2009, IEEE, 2009, pp. 243–251.
24.
G.Jekabsons, V.Kairish and V.Zuravlyov, An analysis of wi-fi based indoor positioning accuracy, Scientific Journal of Riga Technical University44(1) (2011), 131–137, Computer Sciences.
25.
A.LaMarca and E.DeLara, Location systems: An introduction to the technology behind location awareness, Synthesis Lectures on Mobile and Pervasive Computing3(1) (2008), 1–122.
26.
C.Laoudias, C.G.Panayiotou and P.Kemppi, On the rbf-based positioning using wlan signal strength fingerprints, in: 2010 7th Workshop on Positioning Navigation and Communication (WPNC), IEEE, 2010, pp. 93–98.
27.
T.-N.Lin and P.-C.Lin, Performance comparison of indoor positioning techniques based on location fingerprinting in wireless networks, in: 2005 International Conference on Wireless Networks, Communications and Mobile Computing, Vol. 2, IEEE, 2005, pp. 1569–1574.
28.
T.Mitchell, Machine Learning (Mcgraw-Hill International Edit), McGraw Hill Higher Education, 1997.
29.
E.Mok and B.K.S.Cheung, An improved neural network training algorithm for wi-fi fingerprinting positioning, ISPRS International Journal of Geo-Information2(3) (2013), 854–868.
30.
J.R.Quinlan, C4.5: Programs for Machine Learning, The Morgan Kaufmann Series in Machine Learning, Morgan-Kauffman, San Mateo, California, 1993.
31.
T.Roos, P.Myllymäki, H.Tirri, P.Misikangas and J.Sievänen, A probabilistic approach to wlan user location estimation, International Journal of Wireless Information Networks9(3) (2002), 155–164.
32.
S.Saha, K.Chaudhuri, D.Sanghi and P.Bhagwat, Location determination of a mobile device using ieee 802.11 b access point signals, in: 2003 IEEE Wireless Communications and Networking, WCNC 2003, Vol. 3, IEEE, 2003, pp. 1987–1992.
33.
R.Setiya and A.Gaur, Fingerprinting based localization of mobile terminals using ieee802. 11, World Journal of Science and Technology2(3) (2012).
34.
J.A.Silva, M.J.Nicolau and A.Costa, Wifi localization as a network service, 2011.
35.
D.Skalak, Prototype selection for composite nearest neighbor classifiers, PhD thesis, University of Massachusetts Amherst, 1997.
36.
A.Smailagic, J.Small and D.P.Siewiorek, Determining user location for context aware computing through the use of a wireless lan infrastructure, Institute for Complex Engineered Systems Carnegie Mellon University, Pittsburgh, PA, 15213, 2000.
37.
K.Trawiński, J.M.Alonso and N.Hernández, A multiclassifier approach for topology-based wifi indoor localization, Soft Computing, 1–15.
38.
C.-L.Wu, L.-C.Fu and F.-L.Lian, Wlan location determination in e-home via support vector classification, in: 2004 IEEE International Conference on Networking, Sensing and Control, Vol. 2, IEEE, 2004, pp. 1026–1031.
39.
Y.Xu and Y.Sun, Neural network-based accuracy enhancement method for wlan indoor positioning, in: 2012 IEEE Vehicular Technology Conference (VTC Fall), IEEE, 2012, pp. 1–5.
40.
J.Yim, Introducing a decision tree-based indoor positioning technique, Expert Systems with Applications34(2) (2008), 1296–1302.
41.
X.Yubin, Z.Mu and M.Lin, Hybrid fcm/ann indoor location method in wlan environment, in: IEEE Youth Conference on Information, Computing and Telecommunication, YC-ICT’09, IEEE, 2009, pp. 475–478.
42.
M.Zhou, Y.Xu and L.Tang, Multilayer ann indoor location system with area division in wlan environment, Journal of Systems Engineering and Electronics21(5) (2010), 914–926.