GPS calibrated ad-hoc localization for geosocial networking

LNCS v. 6406 is conference proceedings of UIC 2010Cost-effective localization for large-scale Geosocial networking service is a challenging issue in urban environment. This paper studies an ad-hoc localization technique which takes advantages of short-range interchanged location information for calibrating the location of mobile users carrying non-GPS mobile phones. We demonstrate by simulation that a small percentage of GPS-enabled mobile phones can greatly enable the localization of other non-GPS pedestrians in the urban environment. Based on the proposed localization technique, we implement a location-aware social networking tool called Mobile Twitter, similar to the microblogging service of Twitter, for fast propagation of social events happening in surroundings. Evaluation shows the our localization algorithm can achieve better accuracy of the location estimation and wider coverage as compared with the Amorphous algorithm and the Monte Carlo Localization (MCL) method. Moreover, we show that the Mobile Twitter implemented on an Android mobile phone is power-efficient in real-life usage scenarios. © 2010 Springer-Verlag.postprintThe 7th International Conference on Ubiquitous Intelligence and Computing (UIC) 2010, Xi'an, China, 26-29 October 2010. In Lecture Notes in Computer Science, 2010, v. 6406, p. 52-6

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. 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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. 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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. 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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. 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Advanced Location-Based Technologies and Services

Since the publication of the first edition in 2004, advances in mobile devices, positioning sensors, WiFi fingerprinting, and wireless communications, among others, have paved the way for developing new and advanced location-based services (LBSs). This second edition provides up-to-date information on LBSs, including WiFi fingerprinting, mobile computing, geospatial clouds, geospatial data mining, location privacy, and location-based social networking. It also includes new chapters on application areas such as LBSs for public health, indoor navigation, and advertising. In addition, the chapter on remote sensing has been revised to address advancements

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

FROM SMALL-WORLDS TO BIG DATA:TEMPORAL AND MULTIDIMENSIONAL ASPECTS OF HUMAN NETWORKS

In this thesis we address the close interplay among mobility, offline relationships and online interactions and the related human networks at different dimensional scales and temporal granularities. By generally adopting a data-driven approach, we move from small datasets about physical interactions mediated by human-carried devices, describing small social realities, to large-scale graphs that evolve over time, as well as from human mobility trajectories to face-to-face contacts occurring in different geographical contexts. We explore in depth the relation between human mobility and the social structure induced by the overlapping of different people's trajectories on GPS traces collected in urban and metropolitan areas. We define the notions of geo-location and geo-community which are operational in describing in a unique framework both spatial and social aspects of human behavior. Through the concept of geo-community we model the human mobility adopting a bipartite graph. Thanks to this graph representation we can generate a social structure that is plausible w.r.t. the real interactions. In general the modeling approach have the merit for reporting the mobility in a graph-theoretic framework making the study of the interplay mobility/sociality more affordable and intuitive. Our modeling approach also results in a mobility model, Geo-CoMM, which lies on and exploits the idea of geo-community. The model represents a particular instance of a general framework we provide. A framework where the social structure behind the preferred-location based mobility models emerges. We validate Geo-CoMM on spatial, temporal, pairwise connectivity and social features showing that it reproduces the main statistical properties observed in real traces. As concerns the offline/online interplay we provide a complete overview of the close connection between online and offline sociality. To reach our goal we gather data about offline contacts and social interactions on Facebook of a group of students and we propose a multidimensional network analysis which allows us to deeply understand how the characteristics of users in the distinct networks impact each other. Results show how offline and Facebook friends are different. This way we confirm and worsen the general intuition that online social networks have shifted away from their original goal to mirror the offline sociality of individuals. As for the role and the social importance, it becomes apparent that social features such as user popularity or community structure do not transfer along social dimensions, as confirmed by our correlation analysis of the network layers and by the comparison among the communities. In the last chapters we analyze the evolution of the online social network from a physical time perspective, i.e. considering the graph evolution as a graph time-series and not as a function of the network basic properties (number of nodes or links). As for the physical time in a user-centric viewpoint, we investigate the bursty nature of the link creation process in online social network. We prove not only that it is a highly inhomogeneous process, but also identify patterns of burstiness common to all nodes. Then we focus on the dynamic formation of two fundamental network building components: dyads and triads. We propose two new metrics to aid the temporal analysis on physical time: link creation delay and triangle closure delay. These two metrics enable us to study the dynamic creation of dyads and triads, and to highlight network behavior that would otherwise remain hidden. In our analysis, we find that link delays are generally very low in absolute time and are largely independent of the dates people join the network. To highlight the social nature of this metric, we introduce the term \textit{peerness} to quantify how well linked users overlap in lifetimes. As for triadic closure delay we first introduce an algorithm to extract of temporal triangle which enables us to monitor the triangle formation process, and to detect sudden changes in the triangle formation behavior, possibly related to external events. In particular, we show that the introduction of new service functionalities had a disruptive impact on the triangle creation process in the network

Urban Informatics

This open access book is the first to systematically introduce the principles of urban informatics and its application to every aspect of the city that involves its functioning, control, management, and future planning. It introduces new models and tools being developed to understand and implement these technologies that enable cities to function more efficiently – to become ‘smart’ and ‘sustainable’. The smart city has quickly emerged as computers have become ever smaller to the point where they can be embedded into the very fabric of the city, as well as being central to new ways in which the population can communicate and act. When cities are wired in this way, they have the potential to become sentient and responsive, generating massive streams of ‘big’ data in real time as well as providing immense opportunities for extracting new forms of urban data through crowdsourcing. This book offers a comprehensive review of the methods that form the core of urban informatics from various kinds of urban remote sensing to new approaches to machine learning and statistical modelling. It provides a detailed technical introduction to the wide array of tools information scientists need to develop the key urban analytics that are fundamental to learning about the smart city, and it outlines ways in which these tools can be used to inform design and policy so that cities can become more efficient with a greater concern for environment and equity

Urban Informatics

This open access book is the first to systematically introduce the principles of urban informatics and its application to every aspect of the city that involves its functioning, control, management, and future planning. It introduces new models and tools being developed to understand and implement these technologies that enable cities to function more efficiently – to become ‘smart’ and ‘sustainable’. The smart city has quickly emerged as computers have become ever smaller to the point where they can be embedded into the very fabric of the city, as well as being central to new ways in which the population can communicate and act. When cities are wired in this way, they have the potential to become sentient and responsive, generating massive streams of ‘big’ data in real time as well as providing immense opportunities for extracting new forms of urban data through crowdsourcing. This book offers a comprehensive review of the methods that form the core of urban informatics from various kinds of urban remote sensing to new approaches to machine learning and statistical modelling. It provides a detailed technical introduction to the wide array of tools information scientists need to develop the key urban analytics that are fundamental to learning about the smart city, and it outlines ways in which these tools can be used to inform design and policy so that cities can become more efficient with a greater concern for environment and equity

Urban Informatics

This open access book is the first to systematically introduce the principles of urban informatics and its application to every aspect of the city that involves its functioning, control, management, and future planning. It introduces new models and tools being developed to understand and implement these technologies that enable cities to function more efficiently – to become ‘smart’ and ‘sustainable’. The smart city has quickly emerged as computers have become ever smaller to the point where they can be embedded into the very fabric of the city, as well as being central to new ways in which the population can communicate and act. When cities are wired in this way, they have the potential to become sentient and responsive, generating massive streams of ‘big’ data in real time as well as providing immense opportunities for extracting new forms of urban data through crowdsourcing. This book offers a comprehensive review of the methods that form the core of urban informatics from various kinds of urban remote sensing to new approaches to machine learning and statistical modelling. It provides a detailed technical introduction to the wide array of tools information scientists need to develop the key urban analytics that are fundamental to learning about the smart city, and it outlines ways in which these tools can be used to inform design and policy so that cities can become more efficient with a greater concern for environment and equity

LIPIcs, Volume 277, GIScience 2023, Complete Volume

LIPIcs, Volume 277, GIScience 2023, Complete Volum