Seamless Outdoors-Indoors Localization Solutions on Smartphones: Implementation and Challenges

© ACM, 2016. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in http://doi.org/10.1145/2871166[EN] The demand for more sophisticated Location-Based Services (LBS) in terms of applications variety and accuracy is tripling every year since the emergence of the smartphone a few years ago. Equally, smartphone manufacturers are mounting several wireless communication and localization technologies, inertial sensors as well as powerful processing capability, to cater to such LBS applications. A hybrid of wireless technologies is needed to provide seamless localization solutions and to improve accuracy, to reduce time to fix, and to reduce power consumption. The review of localization techniques/technologies of this emerging field is therefore important. This article reviews the recent research-oriented and commercial localization solutions on smartphones. The focus of this article is on the implementation challenges associated with utilizing these positioning solutions on Android-based smartphones. Furthermore, the taxonomy of smartphone-location techniques is highlighted with a special focus on the detail of each technique and its hybridization. The article compares the indoor localization techniques based on accuracy, utilized wireless technology, overhead, and localization technique used. The pursuit of achieving ubiquitous localization outdoors and indoors for critical LBS applications such as security and safety shall dominate future research efforts.This research was sponsored by Koya University, Kurdistan Region-Iraq. The authors also would like to thank Dr. Ali Al-Sherbaz (from the University of Northampton-UK) and Dr. Naseer Al-Jawad (from the University of Buckingham-UK) for providing and improving the quality of this article in terms of academic and technical writing.Maghdid, HS.; Lami, IA.; Ghafoor, KZ.; Lloret, J. (2016). Seamless Outdoors-Indoors Localization Solutions on Smartphones: Implementation and Challenges. ACM Computing Surveys. 48(4):1-34. https://doi.org/10.1145/2871166S134484I. Adusei, K. Kyamakya, and K. Jobmann. 2002. Mobile positioning technologies in cellular networks: An evaluation of their performance metrics. Proceedings of MILCOM 2002. 2, 1239--1244.Faiz Anuar and Ulrike Gretzel. 2011. Privacy concerns in the context of location-based services for tourism. In ENTER 2011 Conference, Innsbruck, Austria.A. Bensky. 2008. Wireless Positioning Technologies and Applications. Artech House, Inc. Norwood, MA.Azzedine Boukerche, Horacio A. B. F. Oliveira, Eduardo F. Nakamura, and Antonio A. F. Loureiro. 2007. Localization systems for wireless sensor networks. IEEE Wireless Communications 14, 6, 6--12.Azzedine Boukerche, Horacio A. B. F. Oliveira, Eduardo F. Nakamura, and Antonio A. F. Loureiro. 2008. Secure localization algorithms for wireless sensor networks. IEEE Communications Magazine 46, 4, 96--101.Azzedine Boukerche, Horacio A. B. F. Oliveira, Eduardo F. Nakamura, and Antonio A. F. Loureiro. 2008. Vehicular ad hoc networks: A new challenge for localization-based systems. Computer Communications 31, 12, 2838--2849.M. Butler. 2011. Android: Changing the Mobile Landscape. PERVASIVE Computing 10, 1, 4--7.J. Caffery and G. Stuber. 1998. Overview of radiolocation in CDMA cellular systems. IEEE Communications Magazine 36, 4, 38--45.Suma S. Cherian and Ashok N. Rudrapatna. 2013. LTE location technologies and delivery solutions. Bell Labs Technical Journal 18, 2, 175--194.M. Ciurana, D. Lopez, and F. Barcelo-Arroyo. 2009. SofTOA: Software ranging for TOA-based positioning of WLAN terminals. Location and Context Awareness 207--221.Paul Craven, Ronald Wong, Neal Fedora, and Paul Crampton. 2013. Studying the Effects of Interference on GNSS Signals. International Technical Meeting. San Diego, California: The Institute of Navigation, 893--186.D. Dardari, P. Closas, and P. M. Djuric. 2015. Indoor tracking: Theory, methods, and technologies. IEEE Transactions on Vehicular Technology 64, 4, 1263--1278.Guido De Angelis, Giuseppe Baruffa, and Saverio Cacopardi. 2012. GNSS/Cellular hybrid positioning system for mobile users in urban scenarios. IEEE Transactions on Intelligent Transportation Systems 14, 1, 313--321.Horacio Antonio Braga Fernandes De Oliveira, Azzedine Boukerche, Eduardo Freire Nakamura, and Antonio Alfredo Ferreira Loureiro. 2009. An efficient directed localization recursion protocol for wireless sensor networks. IEEE Transactions on Computers 58, 5, 677--691.Francescantonio Della Rosa, Mauro Pelosi, and Jari Nurmi. 2012. Human-induced effects on RSS ranging measurements for cooperative positioning. International Journal of Navigation and Observation 13.Zhongliang Deng, Yanpei Yu, Xie Yuan, Neng Wan, and Lei Yang. 2013. Situation and development tendency of indoor positioning. China Communications 10, 3, 42--55.Mohammed Elbes, Ala Al-Fuqaha, and Muhammad Anan. 2013. A precise indoor localization approach based on particle filter and dynamic exclusion techniques. Network Protocols and Algorithms 5, 2, 50--71.R. Faragher and R. Harle. 2013. SmartSLAM--an efficient smartphone indoor positioning system exploiting machine learning and opportunistic sensing. In ION GNSS.Zahid Farid, Rosdiadee Nordin, and Mahamod Ismail. 2013. Recent advances in wireless indoor localization techniques and system. Journal of Computer Networks and Communications 12.S. A. Fayaz. 2013. Location service for wireless network using improved RSS-based cellular localisation. International Journal of Electronics 1--16.C. Feng, W. Au, S. Valaee, and Z. Tan. 2010. Compressive sensing based positioning using RSS of WLAN access points. In 2010 Proceedings of IEEE INFOCOM, 1--9.Ruijun Fu, Yunxing Ye, and K. Pahlavan. 2012. Heterogeneous cooperative localization for social networks with mobile devices. In IEEE 23rd International Symposium on Personal Indoor and Mobile Radio Communications (PIMRC’12).T. Gallagher, B. Li, A. Kealy, and A. Dempster. 2009. Trials of commercial Wi-Fi positioning systems for indoor and urban canyons. In IGNSS 2009 Symposium on GPS/GNSS.T. Gallagher, E. Wise, B. Li, A. Dempster, C. Rizos, and E. Ramsey-Stewart. 2012. Indoor positioning system based on sensor fusion for the blind and visually impaired. In 2012 International Conference on Indoor Positioning and Indoor Navigation (IPIN’12), 1--9.Miguel Garcia, Diana Bri, Jesus Tomas, and Jaime Lloret. 2013. A cooperative decision making algorithm for wireless location systems using interlinking data. In 10th International Conference on Cooperative Design, Visualization and Engineering (CDVE’13). Mallorca, Spain.Miguel Garcia, Fernando Boronat, Jesus Tomás, and Jaime Lloret. 2009. The development of two systems for indoor wireless sensors self-location. Ad Hoc & Sensor Wireless Networks 8, 3--4, 235--258.A. Günther and C. Hoene. 2005. Measuring round trip times to determine the distance between wlan nodes. In Proceedings of Networking 2005. Springer, 768--779.R. Hansen, R. Wind, C. Jensen, and B. Thomsen. 2009. Seamless indoor/outdoor positioning handover for location-based services in streamspin. In 10th International Conference on Mobile Data Management: Systems, Services and Middleware (MDM’09), 267--272.R. Harle. 2013. A survey of indoor inertial positioning systems for pedestrians. In IEEE Communications Surveys Tutorials 15, 3, 1281--1293.A. Hassan and S. Khairulmizam. 2009. Integration of global positioning system and inertial navigation system with different sampling rate using adaptive neuro fuzzy inference system. Science Journal 7, 98--106.J. Hightower and G. Borriello. 2001. Location systems for ubiquitous computing. Computer 34, 8, 57--66.C. Hoene and J. Willmann. 2008. Four-way TOA and software-based trilateration of IEEE 802.11 devices. In IEEE 19th International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC’08), 1--6.J. Huang, D. Millman, M. Quigley, D. Stavens, S. Thrun, and A. Aggarwal. 2011. Efficient, generalized indoor WiFi GRAPHSLAM. In 2011 IEEE International Conference on Robotics and Automation (ICRA’11), 1038--1043.L. Hui, Y. Lei, and W. Yuanfei. 2010. UWB, Multi-sensors and WiFi-mesh based precision positioning for urban rail traffic. In Ubiquitous Positioning Indoor Navigation and Location Based Service (UPINLBS’10), 1--8.Ihsan Alshahib Lami, S. Halgurd Maghdid, and Torben Kuseler. 2014. SILS: A smart indoors localization scheme based on on-the-go cooperative smartphones networks using onboard bluetooth, WiFi and GNSS. In Proceedings of the 27th International Technical Meeting of The Satellite Division of the Institute of Navigation (ION GNSS+’14). Tampa, FL.T. Iwase and R. Shibasaki. 2013. Infra-free indoor positioning using only smartphone sensors. In 2013 International Conference on Indoor Positioning and Indoor Navigation (IPIN’13).S. Jin. 2012. Global Navigation Satellite Systems: Signal, Theory and Applications. In Tech. 438 pages.K. Kalliola. 2008. Bringing navigation indoors. The Way We Live Next. Nokia.J. Kim, J. Lee, and C. Park. 2008. A mitigation of line-of-sight by TDOA error modeling in wireless communication system. In International Conference on Control, Automation and Systems (ICCAS’08), 1601--1605.S. Koenig, M. Schmidt, and C. Hoene. 2011. Multipath mitigation for indoor localization based on IEEE 802.11 time-of-flight measurements. In 2011 IEEE International Symposium on World of Wireless, Mobile and Multimedia Networks (WoWMoM’11), 1--8.N. Kohtake, S. Morimoto, S. Kogure, and D. Manandhar. 2011. Indoor and outdoor seamless positioning using indoor messaging system and GPS. In International Conference on Indoor Positioning and Indoor Navigation (IPIN’11).A. LaMarca, Y. Chawathe, S. Consolvo, J. Hightower, I. Smith, J. Scott, et al. 2005. Place lab: Device positioning using radio beacons in the wild. Pervasive Computing 301--306.J. Lee, Z. Lin, P. Chin, and K. Yar. 2010. One way ranging time drift compensation for both synchronized and non-synchronized clocks. In 2010 International Conference on System Science and Engineering (ICSSE’10), 327--331.Jae-Eun Lee and Sanguk Lee. 2010. Indoor initial positioning using single clock pseudolite system. In 2010 International Conference on Information and Communication Technology Convergence (ICTC’10), 575--578.B. Li, A. G. Dempster, and C. Rizos. 2010. Positioning in environments where GPS fails. FIG Congress, Sydney, Australia, 1--18.D. Lim, S. Lee, and D. Cho. 2007. Design of an assisted GPS receiver and its performance analysis. In IEEE International Symposium on Circuits and Systems (ISCAS’0), 1742--1745.H. Liu, H. Darabi, P. Banerjee, and J. Liu. 2007. Survey of wireless indoor positioning techniques and systems. IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews 37, 6, 1067--1080.Kaikai Liu, Qiuyuan Huang, Wang Jiecong, Li Xiaolin, and D. O. Wu. 2013. Improving GPS service via social collaboration. In 2013 IEEE 10th International Conference on Mobile Ad-Hoc and Sensor Systems (MASS’13).X. Liu, S. Zhang, J. Quan, and X. Lin. 2010. The experimental analysis of outdoor positioning system based on fingerprint approach. In 2010 12th IEEE International Conference on Communication Technology (ICCT’13), 369--372.Jaime Lloret, Jesus Tomas, Alejandro Canovas, and Irene Bellver. 2011. A geopositioning system based on WiFi networks. In The 7th International Conference on Networking and Services (ICNS’11). Venice, Italy.Jaime Lloret, Jesus Tomás, Miguel Garcia, and Alejandro Cánovas. 2009. A hybrid stochastic approach for self-location of wireless sensors in indoor environments. Sensors 9, 5, 3695--3712.Diego Lopez-de-Ipina, Bernhard Klein, Christian Guggenmos, Jorge Perez, and Guillermo Gil. 2011. User-Aware semantic location models for service provision. International Symposium on Ubiquitous Computing and Ambient Intelligence, Riviera Maya, Mexico.Dimitrios Lymberopoulos, Jie Liu, Xue Yang, Romit Roy Choudhury, Vlado Handziski, and Souvik Sen. 2015. A realistic evaluation and comparison of indoor location technologies: Experiences and lessons learned. In Proceedings of the 14th International Conference on Information Processing in Sensor Networks. ACM, New York, NY.N. Mahiddin, N. Safie, E. Nadia, S. Safei, and E. Fadzli. 2012. Indoor position detection using WiFi and trilateration technique. The International Conference on Informatics and Applications (ICIA’12), 362--366.T. Manodham, L. Loyola, and T. Miki. 2008. A novel wireless positioning system for seamless internet connectivity based on the WLAN infrastructure. Wireless Personal Communications 44, 3, 295--309.Alex Mariakakis, Souvik Sen, Jeongkeun Lee, and Kyu-Han Kim. 2014. Single access point based indoor localization. In Proceedings of ACM MobiSys.R. Mautz. 2009. The challenges of indoor environments and specification on some alternative positioning systems. In 6th Workshop on Positioning, Navigation and Communication (WPNC’09), 29--36.M. Mock, R. Frings, E. Nett, and S. Trikaliotis. 2000. Clock synchronization for wireless local area networks. 12th Euromicro Conference on Real-Time Systems (Euromicro RTS’00), 183--189.E. Mok. 2010. Using outdoor public WiFi and GPS integrated method for position updating of knowledge-based logistics system in dense high rise urban environments. 8th International Conference on Supply Chain Management and Information Systems (SCMIS’10), 1--4.D. Niculescu and B. Nath. 2004. VOR base stations for indoor 802. 11 positioning. In Proceedings of the 10th Annual International Conference on Mobile Computing and Networking 26, 58--69.T. Oshin, S. Poslad, and A. Ma. 2012. Improving the energy-efficiency of GPS based location sensing smartphone applications. In IEEE 11th International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom’12), 1698--1705.R. Padilla. 2013. Apple retail stores to integrate iBeacon systems to assist with sales and services. Retrieved January 19, 2016 from http://www.macrumors.com/2013/11/16/apple-retail-stores-to-integrate-ibeacon-systems-to-assist-with-sales-and-services/.D. Park and J. Park. 2011. An enhanced ranging scheme using WiFi RSSI measurements for ubiquitous location. In 1st ACIS/JNU International Conference on Computers, Networks, Systems and Industrial Engineering (CNSI’11), 296--301.J. Partyka. 2012. A look at small indoor location competitors. GPS world. Available at: http://gpsworld.com/wirelesslook-small-indoor-location-competitors-13229/ {Last access January 31, 2016}.L. Pei, R. Chen, J. Liu, Z. Liu, H. Kuusniemi, Y. Chen, et al. 2011. Sensor assisted 3D personal navigation on a smart phone in GPS degraded environments. In 19th International Conference on Geoinformatics, 1--6.R. G. Priyanka Shah. 2012, May 01. Location based reminder using GPS for mobile (Android). ARPN Journal of Science and Technology 2, 4, 377--380.C. Rizos, G. Roberts, J. Barnes, and N. Gambale. 2010. Locata: A new high accuracy indoor positioning system. In Proceedings of the International Conference on Indoor Positioning and Indoor Navigation, Zurich, Switzerland, 15--17.R. Rowe, P. Duffett-Smith, M. Jarvis, and N. Graube. 2008. Enhanced GPS: The tight integration of received cellular timing signals and GNSS receivers for ubiquitous positioning. In IEEE/ION Position, Location and Navigation Symposium, 838--845.A. Roxin, J. Gaber, M. Wack, and A. Nait-Sidi-Moh. 2007. Survey of wireless geolocation techniques. In IEEE Globecom Workshops, 1--9.J. Ryoo, H. Kim, and S. Das. 2012. Geo-fencing: Geographical-fencing based energy-aware proactive framework for mobile devices. In IEEE 20th International Workshop on Quality of Service (IWQoS’12), 1--9.Souvik Sen, Jeongkeun Lee, Kyu-Han Kim, and Paul Congdon. 2013. Avoiding multipath to revive inbuilding WiFi localization. In Proceeding of the 11th Annual International Conference on Mobile Systems, Applications, and Services. ACM, New York, NY.Sensewhere LTD. 2011. Geo-fence technology and applications. Indoor Location Technology Leaders. Available at: http://www.sensewhere.com/images/geowhereDatasheet_compressed.pdf {Last access January 31, 2016}.I. Shafer and M. Chang L. 2010. Movement detection for power-efficient smartphone WLAN localization. In Proceedings of the 13th ACM International Conference on Modeling, Analysis, and Simulation of Wireless and Mobile Systems, ACM, New York, NY, 81--90.Aaron Strout and Mike Schneider. 2011. Location Based Marketing For Dummies. John Wiley & Sons, Hoboken, NJ.Fazli Subhan, Halabi Hasbullah, Azat Rozyyev, and Sheikh Tahir Bakhsh. 2011. Indoor positioning in Bluetooth networks using fingerprinting and lateration approach. In 2011 International Conference on Information Science and Applications (ICISA).Daisuke Taniuchi, Xiaopeng Liu, Daisuke Nakai, and Takuya Maekawa. 2015. Spring model based collaborative indoor position estimation with neighbor mobile devices. IEEE Journal of Selected Topics in Signal Processing 9, 2, 268--277.CSRICIII Working Group 3. 2013. E9-1-1 Location Accuracy: Indoor Location Test Bed Report. San Jose CA: The Communications Security, Reliability and Interoperability Council III. https://transition.fcc.gov/bureaus/pshs/advisory/csric3/CSRIC_III_WG3_Report_March_%202013_ILTestBedReport.pdf {Last access January 31, 2016}.Agoston Torok, Akos Nagy, Laszlo Kovats, and Peter Pach. 2014. DREAR-towards infrastructure-free indoor localization via dead-reckoning enhanced with activity recognition. In 8th International Conference on. Next Generation Mobile Apps, Services and Technologies (NGMAST’14).D. McHoul. 2008. U-TDOA Enabling New Location-based Safety and Security Solutions. TruePosition-White Paper, USA, 1--10.A. Waadt, G. Bruck, and P. Jung. 2009. An overview of positioning systems and technologies. In 2nd International Symposium on Applied Sciences in Biomedical and Communication Technologies (ISABEL’09), 1--5.M. Weyn and F. Schrooyen. 2008. A Wi-Fi Assisted GPS Positioning Concept. ECUMICT, Ghent, Belgium.S. Wibowo, M. Klepal, and D. Pesch. 2009. Time of flight ranging using off-the-self IEEE802. 11 WiFi tags. In Proceedings of the International Conference on Positioning and Context-Awareness (PoCA’09).O. Woodman and R. Harle. 2008. Pedestrian localisation for indoor environments. In Proceedings of the 10th International Conference on Ubiquitous Computing, 114--123.Yinfeng Wu, Ligong Li, Yongji Ren, Kefu Yi, and Ning Yu. 2014. A RSSI localization algorithm and implementation for indoor wireless sensor networks. Adhoc & Sensor Wireless Networks 22, 2.Zhuoling Xiao, Hongkai Wen, Andrew Markham, and Niki Trigoni. 2015. Robust pedestrian dead reckoning (R-PDR) for arbitrary mobile device placement. In International Conference on Indoor Positioning and Indoor Navigation. IEEE.J. Xiong and K. Jamieson. 2011. ArrayTrack: A fine-grained indoor location system. RN 11, 19.Yi Sun, Yubin Zhao, and J. Schiller. 2014. An autonomic indoor positioning application based on smartphone. In IEEE Wireless Communications and Networking Conference (WCNC’14).M. Youssef, A. Youssef, C. Rieger, U. Shankar, and A. Agrawala. 2006. Pinpoint: An asynchronous time-based location determination system. In Proceedings of the 4th International Conference on Mobile Systems, Applications and Services,165--176.Haejung Yun, Dongho Han, and C. Choong Lee. 2013. Understanding the use of location-based service applications: Do privacy concerns matter? Journal of Electronic Commerce Research 14, 3, 215.P. Zandbergen. 2009. Accuracy of iPhone locations: A comparison of assisted GPS, WiFi and cellular positioning. Transactions in GIS 13, s1, 5--25.Y. Zhao, M. Li, and F. Shi. 2010. Indoor radio propagation model based on dominant path. International Journal of Communications, Network and System Sciences 3, 3, 330--337.S. Zirari, P. Canalda, and F. Spies. 2010. WiFi GPS based combined positioning algorithm. In IEEE International Conference on Wireless Communications, Networking and Information Security (WCNIS’10), 684--688

Hybridisation of GNSS with other wireless/sensors technologies onboard smartphones to offer seamless outdoors-indoors positioning for LBS applications

Location-based services (LBS) are becoming an important feature on today’s smartphones (SPs) and tablets. Likewise, SPs include many wireless/sensors technologies such as: global navigation satellite system (GNSS), cellular, wireless fidelity (WiFi), Bluetooth (BT) and inertial-sensors that increased the breadth and complexity of such services. One of the main demand of LBS users is always/seamless positioning service. However, no single onboard SPs technology can seamlessly provide location information from outdoors into indoors. In addition, the required location accuracy can be varied to support multiple LBS applications. This is mainly due to each of these onboard wireless/sensors technologies has its own capabilities and limitations. For example, when outdoors GNSS receivers on SPs can locate the user to within few meters and supply accurate time to within few nanoseconds (e.g. ± 6 nanoseconds). However, when SPs enter into indoors this capability would be lost. In another vain, the other onboard wireless/sensors technologies can show better SP positioning accuracy, but based on some pre-defined knowledge and pre-installed infrastructure. Therefore, to overcome such limitations, hybrid measurements of these wireless/sensors technologies into a positioning system can be a possible solution to offer seamless localisation service and to improve location accuracy. This thesis aims to investigate/design/implement solutions that shall offer seamless/accurate SPs positioning and at lower cost than the current solutions. This thesis proposes three novel SPs localisation schemes including WAPs synchronisation/localisation scheme, SILS and UNILS. The schemes are based on hybridising GNSS with WiFi, BT and inertial-sensors measurements using combined localisation techniques including time-of-arrival (TOA) and dead-reckoning (DR). The first scheme is to synchronise and to define location of WAPs via outdoors-SPs’ fixed location/time information to help indoors localisation. SILS is to help locate any SP seamlessly as it goes from outdoors to indoors using measurements of GNSS, synched/located WAPs and BT-connectivity signals between groups of cooperated SPs in the vicinity. UNILS is to integrate onboard inertial-sensors’ readings into the SILS to provide seamless SPs positioning even in deep indoors, i.e. when the signals of WAPs or BT-anchors are considered not able to be used. Results, obtained from the OPNET simulations for various SPs network size and indoors/outdoors combinations scenarios, show that the schemes can provide seamless and locate indoors-SPs under 1 meter in near-indoors, 2-meters can be achieved when locating SPs at indoors (using SILS), while accuracy of around 3-meters can be achieved when locating SPs at various deep indoors situations without any constraint (using UNILS). The end of this thesis identifies possible future work to implement the proposed schemes on SPs and to achieve more accurate indoors SPs’ location

5G-PPP Technology Board:Delivery of 5G Services Indoors - the wireless wire challenge and solutions

The 5G Public Private Partnership (5G PPP) has focused its research and innovation activities mainly on outdoor use cases and supporting the user and its applications while on the move. However, many use cases inherently apply in indoor environments whereas their requirements are not always properly reflected by the requirements eminent for outdoor applications. The best example for indoor applications can be found is the Industry 4.0 vertical, in which most described use cases are occurring in a manufacturing hall. Other environments exhibit similar characteristics such as commercial spaces in offices, shopping malls and commercial buildings. We can find further similar environments in the media & entertainment sector, culture sector with museums and the transportation sector with metro tunnels. Finally in the residential space we can observe a strong trend for wireless connectivity of appliances and devices in the home. Some of these spaces are exhibiting very high requirements among others in terms of device density, high-accuracy localisation, reliability, latency, time sensitivity, coverage and service continuity. The delivery of 5G services to these spaces has to consider the specificities of the indoor environments, in which the radio propagation characteristics are different and in the case of deep indoor scenarios, external radio signals cannot penetrate building construction materials. Furthermore, these spaces are usually “polluted” by existing wireless technologies, causing a multitude of interreference issues with 5G radio technologies. Nevertheless, there exist cases in which the co-existence of 5G new radio and other radio technologies may be sensible, such as for offloading local traffic. In any case the deployment of networks indoors is advised to consider and be planned along existing infrastructure, like powerlines and available shafts for other utilities. Finally indoor environments expose administrative cross-domain issues, and in some cases so called non-public networks, foreseen by 3GPP, could be an attractive deployment model for the owner/tenant of a private space and for the mobile network operators serving the area. Technology-wise there exist a number of solutions for indoor RAN deployment, ranging from small cell architectures, optical wireless/visual light communication, and THz communication utilising reconfigurable intelligent surfaces. For service delivery the concept of multi-access edge computing is well tailored to host virtual network functions needed in the indoor environment, including but not limited to functions supporting localisation, security, load balancing, video optimisation and multi-source streaming. Measurements of key performance indicators in indoor environments indicate that with proper planning and consideration of the environment characteristics, available solutions can deliver on the expectations. Measurements have been conducted regarding throughput and reliability in the mmWave and optical wireless communication cases, electric and magnetic field measurements, round trip latency measurements, as well as high-accuracy positioning in laboratory environment. Overall, the results so far are encouraging and indicate that 5G and beyond networks must advance further in order to meet the demands of future emerging intelligent automation systems in the next 10 years. Highly advanced industrial environments present challenges for 5G specifications, spanning congestion, interference, security and safety concerns, high power consumption, restricted propagation and poor location accuracy within the radio and core backbone communication networks for the massive IoT use cases, especially inside buildings. 6G and beyond 5G deployments for industrial networks will be increasingly denser, heterogeneous and dynamic, posing stricter performance requirements on the network. The large volume of data generated by future connected devices will put a strain on networks. It is therefore fundamental to discriminate the value of information to maximize the utility for the end users with limited network resources

Localisation en intérieur et gestion de la mobilité dans les réseaux sans fils hétérogènes émergents

Au cours des dernières décennies, nous avons été témoins d'une évolution considérable dans l'informatique mobile, réseau sans fil et des appareils portatifs. Dans les réseaux de communication à venir, les utilisateurs devraient être encore plus mobiles exigeant une connectivité omniprésente à différentes applications qui seront de préférence au courant de leur contexte. Certes, les informations de localisation dans le cadre de leur contexte est d'une importance primordiale à la fois la demande et les perspectives du réseau. Depuis l'application ou de point de vue utilisateur, la fourniture de services peut mettre à jour si l'adaptation au contexte de l'utilisateur est activée. Du point de vue du réseau, des fonctionnalités telles que le routage, la gestion de transfert, l'allocation des ressources et d'autres peuvent également bénéficier si l'emplacement de l'utilisateur peuvent être suivis ou même prédit. Dans ce contexte, nous nous concentrons notre attention sur la localisation à l'intérieur et de la prévision transfert qui sont des composants indispensables à la réussite ultime de l'ère de la communication omniprésente envisagé. Alors que les systèmes de positionnement en plein air ont déjà prouvé leur potentiel dans un large éventail d'applications commerciales, le chemin vers un système de localisation à l'intérieur de succès est reconnu pour être beaucoup plus difficile, principalement en raison des caractéristiques difficiles à l'intérieur et l'exigence d'une plus grande précision. De même, la gestion de transfert dans le futur des réseaux hétérogènes sans fil est beaucoup plus difficile que dans les réseaux traditionnels homogènes. Régimes de procédure de transfert doit être sans faille pour la réunion strictes de qualité de service (QoS) des applications futures et fonctionnel malgré la diversité des caractéristiques de fonctionnement des différentes technologies. En outre, les décisions transfert devraient être suffisamment souples pour tenir compte des préférences utilisateur d'un large éventail de critères proposés par toutes les technologies. L'objectif principal de cette thèse est de mettre au point précis, l'heure et l'emplacement de puissance et de systèmes efficaces de gestion de transfert afin de mieux satisfaire applications sensibles au contexte et mobiles. Pour obtenir une localisation à l'intérieur, le potentiel de réseau local sans fil (WLAN) et Radio Frequency Identification (RFID) que l'emplacement autonome technologies de détection sont d'abord étudiés par des essais plusieurs algorithmes et paramètres dans un banc d'essai expérimental réel ou par de nombreuses simulations, alors que leurs lacunes sont également été identifiés. Leur intégration dans une architecture commune est alors proposée afin de combiner leurs principaux avantages et surmonter leurs limitations. La supériorité des performances du système de synergie sur le stand alone homologues est validée par une analyse approfondie. En ce qui concerne la tâche de gestion transfert, nous repérer que la sensibilité au contexte peut aussi améliorer la fonctionnalité du réseau. En conséquence, deux de tels systèmes qui utilisent l'information obtenue à partir des systèmes de localisation sont proposées. Le premier schéma repose sur un déploiement tag RFID, comme notre architecture de positionnement RFID, et en suivant la scène WLAN analyse du concept de positionnement, prédit l'emplacement réseau de la prochaine couche, c'est à dire le prochain point de fixation sur le réseau. Le second régime repose sur une approche intégrée RFID et sans fil de capteur / actionneur Network (WSAN) de déploiement pour la localisation des utilisateurs physiques et par la suite pour prédire la prochaine leur point de transfert à deux couches de liaison et le réseau. Etre indépendant de la technologie d'accès sans fil principe sous-jacent, les deux régimes peuvent être facilement mises en œuvre dans des réseaux hétérogènes [...]Over the last few decades, we have been witnessing a tremendous evolution in mobile computing, wireless networking and hand-held devices. In the future communication networks, users are anticipated to become even more mobile demanding for ubiquitous connectivity to different applications which will be preferably aware of their context. Admittedly, location information as part of their context is of paramount importance from both application and network perspectives. From application or user point of view, service provision can upgrade if adaptation to the user's context is enabled. From network point of view, functionalities such as routing, handoff management, resource allocation and others can also benefit if user's location can be tracked or even predicted. Within this context, we focus our attention on indoor localization and handoff prediction which are indispensable components towards the ultimate success of the envisioned pervasive communication era. While outdoor positioning systems have already proven their potential in a wide range of commercial applications, the path towards a successful indoor location system is recognized to be much more difficult, mainly due to the harsh indoor characteristics and requirement for higher accuracy. Similarly, handoff management in the future heterogeneous wireless networks is much more challenging than in traditional homogeneous networks. Handoff schemes must be seamless for meeting strict Quality of Service (QoS) requirements of the future applications and functional despite the diversity of operation features of the different technologies. In addition, handoff decisions should be flexible enough to accommodate user preferences from a wide range of criteria offered by all technologies. The main objective of this thesis is to devise accurate, time and power efficient location and handoff management systems in order to satisfy better context-aware and mobile applications. For indoor localization, the potential of Wireless Local Area Network (WLAN) and Radio Frequency Identification (RFID) technologies as standalone location sensing technologies are first studied by testing several algorithms and metrics in a real experimental testbed or by extensive simulations, while their shortcomings are also identified. Their integration in a common architecture is then proposed in order to combine their key benefits and overcome their limitations. The performance superiority of the synergetic system over the stand alone counterparts is validated via extensive analysis. Regarding the handoff management task, we pinpoint that context awareness can also enhance the network functionality. Consequently, two such schemes which utilize information obtained from localization systems are proposed. The first scheme relies on a RFID tag deployment, alike our RFID positioning architecture, and by following the WLAN scene analysis positioning concept, predicts the next network layer location, i.e. the next point of attachment to the network. The second scheme relies on an integrated RFID and Wireless Sensor/Actuator Network (WSAN) deployment for tracking the users' physical location and subsequently for predicting next their handoff point at both link and network layers. Being independent of the underlying principle wireless access technology, both schemes can be easily implemented in heterogeneous networks. Performance evaluation results demonstrate the advantages of the proposed schemes over the standard protocols regarding prediction accuracy, time latency and energy savingsEVRY-INT (912282302) / SudocSudocFranceF

Recent Advances in Indoor Localization: A Survey on Theoretical Approaches and Applications

Nowadays, the availability of the location information becomes a key factor in today’s communications systems for allowing location based services. In outdoor scenarios, the Mobile Terminal (MT) position is obtained with high accuracy thanks to the Global Positioning System (GPS) or to the standalone cellular systems. However, the main problem of GPS or cellular systems resides in the indoor environment and in scenarios with deep shadowing effect where the satellite or cellular signals are broken. In this paper, we will present a review over different technologies and concepts used to improve indoor localization. Additionally, we will discuss different applications based on different localization approaches. Finally, comprehensive challenges in terms of accuracy, cost, complexity, security, scalability, etc. are presente

Edge Artificial Intelligence for Real-Time Target Monitoring

The key enabling technology for the exponentially growing cellular communications sector is location-based services. The need for location-aware services has increased along with the number of wireless and mobile devices. Estimation problems, and particularly parameter estimation, have drawn a lot of interest because of its relevance and engineers' ongoing need for higher performance. As applications expanded, a lot of interest was generated in the accurate assessment of temporal and spatial properties. In the thesis, two different approaches to subject monitoring are thoroughly addressed. For military applications, medical tracking, industrial workers, and providing location-based services to the mobile user community, which is always growing, this kind of activity is crucial. In-depth consideration is given to the viability of applying the Angle of Arrival (AoA) and Receiver Signal Strength Indication (RSSI) localization algorithms in real-world situations. We presented two prospective systems, discussed them, and presented specific assessments and tests. These systems were put to the test in diverse contexts (e.g., indoor, outdoor, in water...). The findings showed the localization capability, but because of the low-cost antenna we employed, this method is only practical up to a distance of roughly 150 meters. Consequently, depending on the use-case, this method may or may not be advantageous. An estimation algorithm that enhances the performance of the AoA technique was implemented on an edge device. Another approach was also considered. Radar sensors have shown to be durable in inclement weather and bad lighting conditions. Frequency Modulated Continuous Wave (FMCW) radars are the most frequently employed among the several sorts of radar technologies for these kinds of applications. Actually, this is because they are low-cost and can simultaneously provide range and Doppler data. In comparison to pulse and Ultra Wide Band (UWB) radar sensors, they also need a lower sample rate and a lower peak to average ratio. The system employs a cutting-edge surveillance method based on widely available FMCW radar technology. The data processing approach is built on an ad hoc-chain of different blocks that transforms data, extract features, and make a classification decision before cancelling clutters and leakage using a frame subtraction technique, applying DL algorithms to Range-Doppler (RD) maps, and adding a peak to cluster assignment step before tracking targets. In conclusion, the FMCW radar and DL technique for the RD maps performed well together for indoor use-cases. The aforementioned tests used an edge device and Infineon Technologies' Position2Go FMCW radar tool-set

Collaborative Techniques for Indoor Positioning Systems

The demand for Indoor Positioning Systems (IPSs) developed specifically for mobile and wearable devices is continuously growing as a consequence of the expansion of the global market of Location-based Services (LBS), increasing adoption of mobile LBS applications, and ubiquity of mobile/wearable devices in our daily life. Nevertheless, the design of mobile/wearable devices-based IPSs requires to fulfill additional design requirements, namely low power consumption, reuse of devices’ built-in technologies, and inexpensive and straightforward implementation. Within the available indoor positioning technologies, embedded in mobile/wearable devices, IEEE 802.11 Wireless LAN (Wi-Fi) and Bluetooth Low Energy (BLE) in combination with lateration and fingerprinting have received extensive attention from research communities to meet the requirements. Although these technologies are straightforward to implement in positioning approaches based on Received Signal Strength Indicator (RSSI), the positioning accuracy decreases mainly due to propagation signal fluctuations in Line-of-sight (LOS) and Non-line-of-sight (NLOS), and the heterogeneity of the devices’ hardware. Therefore, providing a solution to achieve the target accuracy within the given constraints remains an open issue. The motivation behind this doctoral thesis is to address the limitations of traditional IPSs for human positioning based on RSSI, which suffer from low accuracy due to signal fluctuations and hardware heterogeneity, and deployment cost constraints, considering the advantages provided by the ubiquity of mobile devices and collaborative and machine learning-based techniques. Therefore, the research undertaken in this doctoral thesis focuses on developing and evaluating mobile device-based collaborative indoor techniques, using Multilayer Perceptron (MLP) Artificial Neural Networks (ANNs), for human positioning to enhance the position accuracy of traditional indoor positioning systems based on RSSI (i.e., lateration and fingerprinting) in real-world conditions. The methodology followed during the research consists of four phases. In the first phase, a comprehensive systematic review of Collaborative Indoor Positioning Systems (CIPSs) was conducted to identify the key design aspects and evaluations used in/for CIPSs and the main concerns, limitations, and gaps reported in the literature. In the second phase, extensive experimental data collections using mobile devices and considering collaborative scenarios were performed. The data collected was used to create a mobile device-based BLE database for testing ranging collaborative indoor positioning approaches, and BLE and Wi-Fi radio maps to estimate devices’ position in the non-collaborative phase. Moreover, a detailed description of the methodology used for collecting and processing data and creating the database, as well as its structure, was provided to guarantee the reproducibility, use, and expansion of the database. In the third phase, the traditional methods to estimate distance (i.e., based on Logarithmic Distance Path Loss (LDPL) and fuzzy logic) and position (i.e., RSSI-lateration and fingerprinting–9-Nearest Neighbors (9-NN)) were described and evaluated in order to present their limitations and challenges. Also, two novel approaches to improve distance and positioning accuracy were proposed. In the last phase, our two proposed variants of collaborative indoor positioning system using MLP ANNs were developed to enhance the accuracy of the traditional indoor positioning approaches (BLE–RSSI lateration-based and fingerprinting) and evaluated them under real-world conditions to demonstrate their feasibility and benefits, and to present their limitations and future research avenues. The findings obtained in each of the aforementioned research phases correspond to the main contributions of this doctoral thesis. Specifically, the results of evaluating our CIPSs demonstrated that the first proposed variant of mobile device-based CIPS outperforms the positioning accuracy of the traditional lateration-based IPSs. Considering the distances among collaborating devices, our CIPS significantly outperforms the lateration baseline in short distances (≤ 4m), medium distances (>4m and ≤ 8m), and large distances (> 8m) with a maximum error reduction of 49.15 %, 19.24 %, and 21.48 % for the “median” metric, respectively. Regarding the second variant, the results demonstrated that for short distances between collaborating devices, our collaborative approach outperforms the traditional IPSs based on BLE–fingerprinting and Wi-Fi–fingerprinting with a maximum error reduction of 23.41% and 19.49% for the “75th percentile” and “90th percentile” metric, respectively. For medium distances, our proposed approach outperforms the traditional IPSs based on BLE–fingerprinting in the first 60% and after the 90% of cases in the Empirical Cumulative Distribution Function (ECDF) and only partially (20% of cases in the ECDF) the traditional IPSs based on Wi-Fi–fingerprinting. For larger distances, the performance of our proposed approach is worse than the traditional IPSs based on fingerprinting. Overall, the results demonstrate the usefulness and usability of our CIPSs to improve the positioning accuracy of traditional IPSs, namely IPSs based on BLE– lateration, BLE–fingerprinting, and Wi-Fi–fingerprinting under specific conditions. Mainly, conditions where the collaborative devices have short and medium distances between them. Moreover, the integration of MLP ANNs model in CIPSs allows us to use our approach under different scenarios and technologies, showing its level of generalizability, usefulness, and feasibility.Cotutelle-yhteistyöväitöskirja

Real-Time Localization Using Software Defined Radio

Service providers make use of cost-effective wireless solutions to identify, localize, and possibly track users using their carried MDs to support added services, such as geo-advertisement, security, and management. Indoor and outdoor hotspot areas play a significant role for such services. However, GPS does not work in many of these areas. To solve this problem, service providers leverage available indoor radio technologies, such as WiFi, GSM, and LTE, to identify and localize users. We focus our research on passive services provided by third parties, which are responsible for (i) data acquisition and (ii) processing, and network-based services, where (i) and (ii) are done inside the serving network. For better understanding of parameters that affect indoor localization, we investigate several factors that affect indoor signal propagation for both Bluetooth and WiFi technologies. For GSM-based passive services, we developed first a data acquisition module: a GSM receiver that can overhear GSM uplink messages transmitted by MDs while being invisible. A set of optimizations were made for the receiver components to support wideband capturing of the GSM spectrum while operating in real-time. Processing the wide-spectrum of the GSM is possible using a proposed distributed processing approach over an IP network. Then, to overcome the lack of information about tracked devices’ radio settings, we developed two novel localization algorithms that rely on proximity-based solutions to estimate in real environments devices’ locations. Given the challenging indoor environment on radio signals, such as NLOS reception and multipath propagation, we developed an original algorithm to detect and remove contaminated radio signals before being fed to the localization algorithm. To improve the localization algorithm, we extended our work with a hybrid based approach that uses both WiFi and GSM interfaces to localize users. For network-based services, we used a software implementation of a LTE base station to develop our algorithms, which characterize the indoor environment before applying the localization algorithm. Experiments were conducted without any special hardware, any prior knowledge of the indoor layout or any offline calibration of the system