76 research outputs found

    Constrained Localization: A Survey

    Get PDF
    International audienceIndoor localization techniques have been extensively studied in the last decade. The wellestablished technologies enable the development of Real-Time Location Systems (RTLS). A good body of publications emerged, with several survey papers that provide a deep analysis of the research advances. Existing survey papers focus on either a specific technique and technology or on a general overview of indoor localization research. However, there is a need for a use case-driven survey on both recent academic research and commercial trends, as well as a hands-on evaluation of commercial solutions. This work aims at helping researchers select the appropriate technology and technique suitable for developing low-cost, low-power localization system, capable of providing centimeter level accuracy. The article is both a survey on recent academic research and a hands-on evaluation of commercial solutions. We introduce a specific use case as a guiding application throughout this article: localizing low-cost low-power miniature wireless swarm robots. We define a taxonomy and classify academic research according to five criteria: Line of Sight (LoS) requirement, accuracy, update rate, battery life, cost. We discuss localization fundamentals, the different technologies and techniques, as well as recent commercial developments and trends. Besides the traditional taxonomy and survey, this article also presents a hands-on evaluation of popular commercial localization solutions based on Bluetooth Angle of Arrival (AoA) and Ultra-Wideband (UWB). We conclude this article by discussing the five most important open research challenges: lightweight filtering algorithms, zero infrastructure dependency, low-power operation, security, and standardization

    Ultra-low power IoT applications: from transducers to wireless protocols

    Get PDF
    This dissertation aims to explore Internet of Things (IoT) sensor nodes in various application scenarios with different design requirements. The research provides a comprehensive exploration of all the IoT layers composing an advanced device, from transducers to on-board processing, through low power hardware schemes and wireless protocols for wide area networks. Nowadays, spreading and massive utilization of wireless sensor nodes pushes research and industries to overcome the main limitations of such constrained devices, aiming to make them easily deployable at a lower cost. Significant challenges involve the battery lifetime that directly affects the device operativity and the wireless communication bandwidth. Factors that commonly contrast the system scalability and the energy per bit, as well as the maximum coverage. This thesis aims to serve as a reference and guideline document for future IoT projects, where results are structured following a conventional development pipeline. They usually consider communication standards and sensing as project requirements and low power operation as a necessity. A detailed overview of five leading IoT wireless protocols, together with custom solutions to overcome the throughput limitations and decrease the power consumption, are some of the topic discussed. Low power hardware engineering in multiple applications is also introduced, especially focusing on improving the trade-off between energy, functionality, and on-board processing capabilities. To enhance these features and to provide a bottom-top overview of an IoT sensor node, an innovative and low-cost transducer for structural health monitoring is presented. Lastly, the high-performance computing at the extreme edge of the IoT framework is addressed, with special attention to image processing algorithms running on state of the art RISC-V architecture. As a specific deployment scenario, an OpenCV-based stack, together with a convolutional neural network, is assessed on the octa-core PULP SoC

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

    Full text link
    © 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

    D2D-based Cooperative Positioning Paradigm for Future Wireless Systems: A Survey

    Get PDF
    Emerging communication network applications require a location accuracy of less than 1m in more than 95% of the service area. For this purpose, 5G New Radio (NR) technology is designed to facilitate high-accuracy continuous localization. In 5G systems, the existence of high-density small cells and the possibility of the device-to-device (D2D) communication between mobile terminals paves the way for cooperative positioning applications. From the standardization perspective, D2D technology is already under consideration (5G NR Release 16) for ultra-dense networks enabling cooperative positioning and is expected to achieve the ubiquitous positioning of below one-meter accuracy, thereby fulfilling the 5G requirements. In this survey, the strengths and weaknesses of D2D as an enabling technology for cooperative cellular positioning are analyzed (including two D2D approaches to perform cooperative positioning); lessons learned and open issues are highlighted to serve as guidelines for future research

    Edge Artificial Intelligence for Real-Time Target Monitoring

    Get PDF
    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

    Recent Advances in Indoor Localization Systems and Technologies

    Get PDF
    Despite the enormous technical progress seen in the past few years, the maturity of indoor localization technologies has not yet reached the level of GNSS solutions. The 23 selected papers in this book present the recent advances and new developments in indoor localization systems and technologies, propose novel or improved methods with increased performance, provide insight into various aspects of quality control, and also introduce some unorthodox positioning methods

    Actuators for Intelligent Electric Vehicles

    Get PDF
    This book details the advanced actuators for IEVs and the control algorithm design. In the actuator design, the configuration four-wheel independent drive/steering electric vehicles is reviewed. An in-wheel two-speed AMT with selectable one-way clutch is designed for IEV. Considering uncertainties, the optimization design for the planetary gear train of IEV is conducted. An electric power steering system is designed for IEV. In addition, advanced control algorithms are proposed in favour of active safety improvement. A supervision mechanism is applied to the segment drift control of autonomous driving. Double super-resolution network is used to design the intelligent driving algorithm. Torque distribution control technology and four-wheel steering technology are utilized for path tracking and adaptive cruise control. To advance the control accuracy, advanced estimation algorithms are studied in this book. The tyre-road peak friction coefficient under full slip rate range is identified based on the normalized tyre model. The pressure of the electro-hydraulic brake system is estimated based on signal fusion. Besides, a multi-semantic driver behaviour recognition model of autonomous vehicles is designed using confidence fusion mechanism. Moreover, a mono-vision based lateral localization system of low-cost autonomous vehicles is proposed with deep learning curb detection. To sum up, the discussed advanced actuators, control and estimation algorithms are beneficial to the active safety improvement of IEVs

    Performance evaluation of Vehicular Ad Hoc Networks over high speed environment using NCTUns

    Get PDF
    Català: Cada any aproximadament un milió dues-centes mil persones moren en accidents de trànsit. D'aquesta dada es desprèn que els accidents de trànsit són la quarta causa de mortalitat al món. Degut a això, un gran nombre de governs i els majors fabricants de vehicles del món estan invertint temps i diners en recerca i desenvolupament per millorar la seguretat a les carreteres. Amb aquest objectiu, apareix el concepte de VANET: Vehicular Ad-hoc NETwork. Una VANET està basada en vehicles i estacions base intel·ligents que comparteixen informació a través de comunicacions inalàmbriques. Aquest intercanvi de dades podria tenir un gran impacte en la seguretat viària i la qualitat en la conducció però a més a més seria una nova font d' entreteniment mòbil. La millora en seguretat implicaria una reducció en el nombre d'accidents i les comunicacions inalàmbriques usades en mobilitat permetrien una optimització del transport. L'evolució de les VANETs en els últims anys i les seves aplicacions útils a les carreteres són les principals raons per dur a terme aquest projecte. El gran suport a aquest tipus de xarxes inalàmbriques sembla indicar que les VANETs són les xarxes del futur en entorns mòbils. En relació al projecte, el primer problema observat és que el protocol que s'usa específicament en VANETs (802.11p) només està disponible en pocs simuladors de xarxa i està en fase de desenvolupament. Per tant, la majoria de les funcions no estan implementades i això fa que el protocol no sigui madur. En conseqüència, es va triar un protocol àmpliament usat com és 802.11b per fer les proves en el simulador NCTUns. L?objectiu del projecte és avaluar el funcionament de VANETs usant el protocol 802.11b i el protocol d?encaminament AODV en un escenari d?autopista. Ajustant diferents paràmetres com el nombre de cotxes, la seva velocitat i el seu rang de cobertura és possible obtenir variacions en les mesures de pèrdues, throughput i retard extrem-a-extrem en la xarxa. El resultat final és que les mesures permeten saber quines són les comunicacions que es produeixen a la xarxa per cadascuna de les configuracions i la seva incidència en les condicions de conducció.Castellano: Cada año cerca de un millón doscientas mil personas fallecen en accidentes de tráfico. De este dato se desprende que los accidentes de tráfico son la cuarta causa de mortalidad en el mundo. Debido a esto, un gran número de gobiernos y los mayores fabricantes de vehículos del mundo están invirtiendo tiempo y dinero en investigación y desarrollo para mejorar la seguridad en las carreteras. Con este objetivo, aparece el concepto de VANET: Vehicular Ad-hoc NETwork. Una VANET se basa en vehículos y estaciones base inteligentes que comparten información por medio de comunicaciones inalámbricas. Este intercambio de datos podría tener un gran impacto en la seguridad vial y en la calidad de la conducción pero además sería una nueva fuente de entretenimiento móvil. La mejora en la seguridad implicaría una reducción en el número de accidentes y las comunicaciones inalámbricas utilizadas en movilidad permitirían optimizar el transporte. La evolución de las VANETs en los últimos años y sus aplicaciones útiles en las carreteras son las principales razones para llevar a cabo este proyecto. El gran apoyo a este tipo de redes inalámbricas parece indicar que las VANETs son las redes del futuro en entornos móviles. En relación al proyecto, el primer problema observado es que el protocolo específicamente utilizado en VANETs (802.11p) sólo está disponible en pocos simuladores de red y se encuentra en fase de desarrollo. Por lo tanto, la mayoría de funciones no están implementadas y esto hace que el protocolo no sea maduro. En consecuencia, se escogió un protocolo ampliamente utilizado como es 802.11b para realizar las pruebas en el simulador NCTUns. El objetivo del proyecto es evaluar el funcionamiento de VANETs utilizando el protocolo 802.11b y el protocolo de encaminamiento AODV en un escenario de autopista. Ajustando diferentes parámetros como el número de coches, su velocidad y su rango de cobertura es posible obtener variaciones en las medidas de pérdidas, throughput y retardo extremo-a-extremo en la red. El resultado final es que las medidas permiten saber cuáles son las comunicaciones que se producen en la red para cada una de las configuraciones y su incidencia en las condiciones de conducción.English: Every year about 1.2 million people die because of traffic accidents [1]. This means that traffic accidents are the fourth cause of mortality in the world. Therefore, several governments and the most important car manufacturers are investing time and money on research and development in order to improve road safety. At this respect, appears the concept of VANET: Vehicular Ad-hoc NETwork. A VANET is based on smart cars and base-stations that share information via wireless communications. This interchange of data may have a great impact on safety and driving quality but also could be another source of mobile entertainment. This improvement on safety would imply reducing the number of accidents. In addition, the use of wireless communications in mobility would lead to an optimization of transport. The evolution of VANETs in the last years and their useful applications on the road has been the main reason to develop this project. The great support of many people to this type of wireless networks suggests that VANETs are the networks of the future in mobile environments. Regarding the project, the first problem encountered is that the network protocol specially designed for VANETs, IEEE 802.11p, is only available in a few of the network simulators and is on phase of development. This fact means that most of the functions are not implemented so it cannot be considered as a mature protocol. As a consequence, a widely used protocol as IEEE 802.11b was chosen and all the tests were performed on NCTUns simulator. So the purpose of this project is to evaluate the performance of VANETs by using 802.11b protocol and AODV routing protocol in a highway scenario. By adjusting different parameters like number of cars, their speed and their range of coverage, variations on measures of loss ratio, throughput and end-to- end delay were detected on the network. Finally, the measures help to know about network communications for each of the cases and their incidence on driving conditions
    corecore