Localization in Wireless Networks: The Potential of Triangulation Techniques

User localization is one of the key service-enablers in broadband mobile communications. Moreover, from a different point of view, next steps towards automatic network optimization also depend upon the capability of the system to perform real-time user localization, in order to obtain the traffic distribution. The aim of this paper is to get deeper into the feasibility and accuracy of different localization mechanisms ranging from triangulation to database correlation. Call tracing data extracted from a real operating mobile network have been used to assess these algorithms after the execution of an extensive measurements campaign. Results show that enhanced triangulation offers the best performance even outperforming other more sophisticated mechanisms like fingerprinting, without introducing any change in the network and without requiring any special characteristic of the user equipment. Indeed, the lack of precision of channel estimates, which for the same position could differ up to 10 dB, introduces a large uncertainty that harms localization mechanisms based on database correlation. Finally, this paper identifies the areas for improvement in triangulation to reach its maximum potential, provides details for its implementation and analyzes the performance of the different proposed enhancements. © 2012 Springer Science+Business Media, LLC.The authors would like to thank the funding received from the Ministerio de Industria, Turismo y Comercio within the Project number TSI-020100-2010-183 and from the Generalitat Valenciana IMIDTA/2010/800 funds.Osa Ginés, V.; Matamales Casañ, J.; Monserrat Del Río, JF.; López Bayo, J. (2013). Localization in Wireless Networks: The Potential of Triangulation Techniques. Wireless Personal Communications. 68(4):1525-1538. https://doi.org/10.1007/s11277-012-0537-2S15251538684Laiho J., Wacker A., Novosad T. (2006) Radio Network Planning and Optimisation for UMTS 2nd Edn. Wiley, AmsterdamOsa V., Matamales J., Monserrat J. et al (2010) Expert systems for the automatic optimisation of 3G networks. WAVES 2: 97–105Gustafsson F., Gunnarsson F. (2005) Mobile positioning using wireless networks: Possibilites and fundamental limitations based on available wireless network measurements. IEEE Signal Processing Magazine 22(4): 41–53. doi: 10.1109/MSP.2005.1458284Gezici S. (2008) A survey on wireless position estimation. Springer Wireless Personal Communications 44(3): 263–282. doi: 10.1007/s11277-007-9375-zBahillo, A., Mazuelas, S., & Lorenzo, R.M., et al. (2010). Accurate and integrated localization system for indoor environments based on IEEE 802.11 round-trip time measurements.EURASIP Journal on Wireless Communications and Networking, 2010, Article ID 102095, p. 13. doi: 10.1155/2010/102095 .Yang Z., Liu Y. (2010) Quality of trilateration: Confidence-based iterative localization. IEEE Transactions on Parallel and Distributed Systems 21(5): 631–640. doi: 10.1109/TPDS.2009.90Zimmermann, D., et al. (2004). Database correlation for positioning of mobile terminals in cellular networks using wave propagation models. In IEEE 60th Vehicular Technology Conference (Vol. 7, pp. 4682–4686) doi: 10.1109/VETECF.2004.1404980 .Zhao Y. (2002) Standardization of mobile phone positioning for 3G systems. IEEE Communications Magazine 40(7): 108–116. doi: 10.1109/MCOM.2002.1018015Caffery J.J., Stuber G.L. (1998) Overview of radiolocation in CDMA cellular systems. IEEE Communications Magazine 36(4): 38–45. doi: 10.1109/35.667411Kaaranen H., Ahtiainen A., Laitinen L., Naghian S., Niemi V. (2005) UMTS networks: Architecture, mobility and services. Wiley, Amsterdam3GPP. (2010). TS 25.215 Physical layer; Measurements (FDD). http://www.3gpp.org/ftp/Specs/archive/25_series/25.215/25215-920.zip .3GPP. (2010). TS 25.133 Requirements for support of radio resource management. http://www.3gpp.org/ftp/Specs/archive/25_series/25.133/25133-950.zip .3GPP. (2009). TS 45.010 Radio subsystem synchronization. http://www.3gpp.org/ftp/Specs/archive/45_series/45.010/45010-900.zip .Kos, T., Grgic, M., & Sisul, G. (2006). Mobile user positioning in GSM/UMTS cellular networks. In 48th International Symposium ELMAR-2006 focused on multimedia signal processing and communications (pp. 185–188). doi: 10.1109/ELMAR.2006.329545 .Kirkpatrick S., Gelatt C. D. Jr., Vecchi M. P. (1983) Optimization by simulated annealing. Science 220(4598): 671–680. doi: 10.1126/science.220.4598.671Hepsaydir, E. (1999). Analysis of mobile positioning measurements in CDMA cellular networks. In Radio and Wireless Conference, RAWCON 99 (pp. 73–76). doi: 10.1109/RAWCON.1999.810933 .Villebrun, E., Ben Hadj Alaya, A., Boursier, Y., & Noisette, N. (2006). Indoor Outdoor user discrimination in mobile wireless networks. In Vehicular Technology Conference 2006 Fall (pp. 1–5, 25–28). doi: 10.1109/VTCF.2006.500 .Farr, T.G., et al. (2007). The shuttle radar topography mission. Reviews of geophysics, Vol. 45, RG2004, 33 pp. doi: 10.1029/2005RG000183

Recurrent Neural Networks For Accurate RSSI Indoor Localization

This paper proposes recurrent neuron networks (RNNs) for a fingerprinting indoor localization using WiFi. Instead of locating user's position one at a time as in the cases of conventional algorithms, our RNN solution aims at trajectory positioning and takes into account the relation among the received signal strength indicator (RSSI) measurements in a trajectory. Furthermore, a weighted average filter is proposed for both input RSSI data and sequential output locations to enhance the accuracy among the temporal fluctuations of RSSI. The results using different types of RNN including vanilla RNN, long short-term memory (LSTM), gated recurrent unit (GRU) and bidirectional LSTM (BiLSTM) are presented. On-site experiments demonstrate that the proposed structure achieves an average localization error of $0.75$ m with $80\%$ of the errors under $1$ m, which outperforms the conventional KNN algorithms and probabilistic algorithms by approximately $30\%$ under the same test environment.Comment: Received signal strength indicator (RSSI), WiFi indoor localization, recurrent neuron network (RNN), long shortterm memory (LSTM), fingerprint-based localizatio

Maximum convergence algorithm for WiFi based indoor positioning system

WiFi-based indoor positioning is widely exploited thanks to the existing WiFi infrastructure in buildings and built-in sensors in smartphones. The techniques for indoor positioning require the high-density training data to archive high accuracy with high computation complexity. In this paper, the approach for indoor positioning systems which is called the maximum convergence algorithm is proposed to find the accurate location by the strongest receiver signal in the small cluster and K nearest neighbours (KNN) of other clusters. Also, the K-mean clustering is deployed for each access point to reduce the computation complexity of the offline databases. Moreover, the pedestrian dead reckoning (PDR) method and Kalman filter with the information from the received signal strength (RSS) and inertial sensors are applied to the WiFi fingerprinting to increase the efficiency of the mobile object's position. The different experiments are performed to compare the proposed algorithm with the others using KNN and PDR. The recommended framework demonstrates significant proceed based on the results. The average precision of this system can be lower than 1.02 meters when testing in the laboratory environment with an area of 7x7 m using three access points

RSS-based wireless LAN indoor localization and tracking using deep architectures

Wireless Local Area Network (WLAN) positioning is a challenging task indoors due to environmental constraints and the unpredictable behavior of signal propagation, even at a fixed location. The aim of this work is to develop deep learning-based approaches for indoor localization and tracking by utilizing Received Signal Strength (RSS). The study proposes Multi-Layer Perceptron (MLP), One and Two Dimensional Convolutional Neural Networks (1D CNN and 2D CNN), and Long Short Term Memory (LSTM) deep networks architectures for WLAN indoor positioning based on the data obtained by actual RSS measurements from an existing WLAN infrastructure in a mobile user scenario. The results, using different types of deep architectures including MLP, CNNs, and LSTMs with existing WLAN algorithms, are presented. The Root Mean Square Error (RMSE) is used as the assessment criterion. The proposed LSTM Model 2 achieved a dynamic positioning RMSE error of 1.73 m, which outperforms probabilistic WLAN algorithms such as Memoryless Positioning (RMSE: 10.35 m) and Nonparametric Information (NI) filter with variable acceleration (RMSE: 5.2 m) under the same experiment environment.ECSEL Joint Undertaking ; European Union's H2020 Framework Programme (H2020/2014-2020) Grant ; National Authority TUBITA

A Soft Range Limited K-Nearest Neighbours Algorithm for Indoor Localization Enhancement

This paper proposes a soft range limited K nearest neighbours (SRL-KNN) localization fingerprinting algorithm. The conventional KNN determines the neighbours of a user by calculating and ranking the fingerprint distance measured at the unknown user location and the reference locations in the database. Different from that method, SRL-KNN scales the fingerprint distance by a range factor related to the physical distance between the user's previous position and the reference location in the database to reduce the spatial ambiguity in localization. Although utilizing the prior locations, SRL-KNN does not require knowledge of the exact moving speed and direction of the user. Moreover, to take into account of the temporal fluctuations of the received signal strength indicator (RSSI), RSSI histogram is incorporated into the distance calculation. Actual on-site experiments demonstrate that the new algorithm achieves an average localization error of $0.66$ m with $80\%$ of the errors under $0.89$ m, which outperforms conventional KNN algorithms by $45\%$ under the same test environment.Comment: Received signal strength indicator (RSSI), WiFi indoor localization, K-nearest neighbor (KNN), fingerprint-based localizatio

Evaluating indoor positioning systems in a shopping mall : the lessons learned from the IPIN 2018 competition

The Indoor Positioning and Indoor Navigation (IPIN) conference holds an annual competition in which indoor localization systems from different research groups worldwide are evaluated empirically. The objective of this competition is to establish a systematic evaluation methodology with rigorous metrics both for real-time (on-site) and post-processing (off-site) situations, in a realistic environment unfamiliar to the prototype developers. For the IPIN 2018 conference, this competition was held on September 22nd, 2018, in Atlantis, a large shopping mall in Nantes (France). Four competition tracks (two on-site and two off-site) were designed. They consisted of several 1 km routes traversing several floors of the mall. Along these paths, 180 points were topographically surveyed with a 10 cm accuracy, to serve as ground truth landmarks, combining theodolite measurements, differential global navigation satellite system (GNSS) and 3D scanner systems. 34 teams effectively competed. The accuracy score corresponds to the third quartile (75th percentile) of an error metric that combines the horizontal positioning error and the floor detection. The best results for the on-site tracks showed an accuracy score of 11.70 m (Track 1) and 5.50 m (Track 2), while the best results for the off-site tracks showed an accuracy score of 0.90 m (Track 3) and 1.30 m (Track 4). These results showed that it is possible to obtain high accuracy indoor positioning solutions in large, realistic environments using wearable light-weight sensors without deploying any beacon. This paper describes the organization work of the tracks, analyzes the methodology used to quantify the results, reviews the lessons learned from the competition and discusses its future