In some cases, the limited memory in mobile devices may restrict the types of algorithms that can be deployed. Even though over time mobile devices such as smartphones are becoming more powerful (with the size of integrated memory also increasing steadily), the sophistication of localization algorithms has also increased over time, driven by the need for greater levels of accuracy. Thus, limits on processing capabilities and memory with mobile devices cannot be overlooked.
Portability. Techniques for indoor localization that require mobile devices to be carried by the moving subject must ensure that such devices are not heavy, too large, or cumbersome to carry. For instance, requiring subjects to wear foot‐mounted/strapped sensors is inconvenient and unlikely to result in a solution that is accepted by a large population of users. Similarly, using proprietary wireless signals that require custom (and possibly bulky) hardware to be attached to smartphones for indoor localization may not be ideal for many people who carry smartphones in their pockets. Thus, portability concerns cannot be ignored, as they can make the crucial difference between indoor localization solutions being widely accepted or largely ignored.
Device heterogeneity. Indoor localization techniques must be able to cope with the heterogeneity of devices on which they may eventually be deployed. Such heterogeneity can be a function of differences in models/vendors of Wi‐Fi, IMU, or other wireless/sensor interfaces used across devices. These differences can cause significant accuracy variations when deploying indoor localization techniques across devices. For example, analysis in [171] shows a localization error of as much as 8× due to mobile device heterogeneity. Approaches such as the SHERPA framework [172], which proposes heterogeneity‐resilient fingerprint pattern matching, are needed to enable heterogeneity resilience.
Initialization and deployment costs. Many indoor localization techniques require an initialization phase, for example, training machine learning algorithms, war‐driving (i.e. site surveying involving searching for Wi‐Fi wireless networks with a moving vehicle or person to create a map of Wi‐Fi APs in an area), or calibrating sensors. Other techniques may require infrastructure enhancements, for example, deploying custom radio beacons across an indoor environment, before the technique can be used for localization. All such initialization is time consuming and usually has costs associated with it. Care needs to be taken to ensure that the initialization phases of localization techniques are short and manageable; and that deployment costs for any custom components are not too high. Novel techniques, such as crowdsourcing with multiple users and heterogeneous devices to create radio maps for fingerprinting‐based indoor localization, and variants of the SLAM techniques discussed earlier, can significantly reduce initialization time and costs. Such approaches can overcome limitations that arise due to changing infrastructure, for example, adding or removing of Wi‐Fi APs in indoor environments over time.
Application‐domain specific requirements. The requirements from indoor localization solutions vary quite significantly across application domains. A few studies have quantified acceptable values for localization performance metrics (Section 37.3) across application domains. In [173], the requirements for indoor localization for the mass market were discussed, emphasizing the use of standard devices (e.g. smartphones) and existing infrastructure (e.g. Wi‐Fi APs) without significant supplementary sensors, beacons, or additional wearable components. In [174], indoor localization requirements for underground construction sites are discussed, with an emphasis on high accuracy (~centimeter level). In [175], indoor localization requirements for enforcement officers, firefighters, and military personnel are presented, with an emphasis on encrypted communication, uncertainty estimation, fast real‐time response, and robustness of devices.
References
1 1 R. Mautz, “Indoor Positioning Technologies,” 2012.
2 2 A. Molisch, “Ultrawideband propagation channels,” Proceedings of IEEE, Special Issue on UWB, Vol. 97, pp. 353–371, 2009.
3 3 A. Bensky Wireless Positioning Technologies and Applications, Artech House Publishers, 2007, 311 p.
4 4 Z. Farid, R. Nordin, and M. Ismail, “Recent advances in wireless indoor localization techniques and system,” Journal of Computer Networks and Communications, 2013.
5 5 https://en.wikipedia.org/wiki/Global_Positioning_System,2016.
6 6 F. Seco, A. R. Jim’enez, C. Prieto, J. Roa, and K. Koutsou, “A survey of mathematical methods for indoor localization,” in Proceedings of the 6th IEEE International Symposium on Intelligent Signal Processing (WISP ‘09), August 2009, pp. 9–14.
7 7 O.A. Aider, P. Hoppenot, and E. Colle, “A model‐based method for indoor mobile robot localization using monocular vision and straight‐line correspondences,” Robotics and Autonomous Systems, Vol. 52, No. 2‐3, pp. 229–246, August 31, 2005.
8 8 V.A. Petrushin, Gang Wei, and A.V. Gershman, “Multiple‐camera people localization in an indoor environment,” Knowledge and Information Systems, Vol. 10, No. 2, pp. 229–241, August 2006.
9 9 IEC, “Safety of laser products – part 1: Equipment classification and requirements,” Technical Information Report 2nd edition, International Electrotechnical Commission, 2007.
10 10 C. Ciavarella and F. Paterno, “The design of a handheld, location‐aware guide for indoor environments,” Personal and Ubiquitous Computing, Vol. 8, No. 2, pp. 82–91, 2004.
11 11 J. Xiao, Z. Liu, Y. Yang, D. Liu, and H. Xu, “Comparison and analysis of indoor wireless positioning techniques,” in Proceedings of the International Conference on Computer Science and Service System (CSSS ‘11), June 2011, pp. 293–296.
12 12 J. Torres‐Solis, T.H. Falk, and T. Chau, “A review of indoor localization technologies: towards navigational assistance for topographical disorientation,” in Ambient Intelligence (ed. F.J.V. Molina), INTECH Open Access Publisher, 2010.
13 13 P. Vorst, J. Sommer, C. Hoene et al., “Indoor positioning via three different RF technologies,” in Proceedings of the 4th European Workshop on RFID Systems and Technologies (RFID SysTech ‘08), June 2008, pp. 1–10.
14 14 X. Zhao, Z. Xiao, A. Markham, N. Trigoni, and Y. Ren, “Does BTLE measure up against WiFi? A comparison of indoor location performance,” in European Wireless 2014; 20th European Wireless Conference; Proceedings of, VDE, May 2014, pp. 1–6.
15 15 M. Philipose, J.R. Smith, B. Jiang, A. Mamishev, S. Roy, and K. Sundara‐Rajan, “Battery‐free wireless identification and sensing,” IEEE Pervasive Computing, Vol. 4, No. 1, pp. 37–45, January/March 2005.
16 16 M. Vossiek, L. Wiebking, M. Gl¨anzer, D. Mastela, and M. Christmann, “Wireless local positioning—concepts, solutions, applications,” in Proceedings of the IEEE Radio and Wireless Conference (RAWCON ‘03), August 2003, pp. 219–224.
17 17 M. Rabinowitz and J.J. Spilker Jr., “A new positioning system using television synchronization signals,” IEEE Transactions on Broadcasting, Vol. 51, No. 1, pp. 51–61, March, 2005.
18 18 H. Sheng, L. Jian‐jun, X. Ying, G. Jun‐wei, and B. Hae‐Young, “Hybrid location determination technology within urban and indoor environment based on cell‐id and path loss,” Journal of Chongqing University of Posts and Telecommunication (Natural Science Edition), Vol. 16, No. 5, 46–49, October 2004.
19 19 A. Popleteev, V. Osmani, and O. Mayora, “Investigation of indoor localization with ambient FM radio stations,” in Proceedings of the IEEE International Conference on Pervasive Computing and Communications (PerCom ‘12),
