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LIPS: Learning Based Indoor Positioning System using mobile phone-based sensors

David Mascharka, Eric Manley
2016 2016 13th IEEE Annual Consumer Communications & Networking Conference (CCNC)  
In this paper we investigate the problem of localizing a mobile device based on readings from its embedded sensors utilizing machine learning methodologies.  ...  We consider a real-world environment, collect a large dataset of 3110 datapoints, and examine the performance of a substantial number of machine learning algorithms in localizing a mobile device.  ...  CONCLUSIONS AND FUTURE WORK In this work, we examined a large number of machine learning algorithms for indoor localization based on the sensors readily available in smartphones.  ... 
doi:10.1109/ccnc.2016.7444919 dblp:conf/ccnc/MascharkaM16 fatcat:lqoatrvhgnev7fl3y72idzzxtq

A semi-supervised learning approach for robust indoor-outdoor detection with smartphones

Valentin Radu, Panagiota Katsikouli, Rik Sarkar, Mahesh K. Marina
2014 Proceedings of the 12th ACM Conference on Embedded Network Sensor Systems - SenSys '14  
Towards this end, we present a general method employing semi-supervised machine learning and using only the lightweight sensors on a smartphone.  ...  The environmental context of a mobile device determines how it is used and how the device can optimize operations for greater efficiency and usability.  ...  We thank Charles Sutton for insightful discussions on semi-supervised learning; we thank Lama Nachman and anonymous reviewers for many helpful suggestions on improving the paper.  ... 
doi:10.1145/2668332.2668347 dblp:conf/sensys/RaduKSM14 fatcat:dfv3yx54onhclcpd7ebggmuevy

Indoor-Outdoor Detection Using a Smart Phone Sensor

Weiping Wang, Qiang Chang, Qun Li, Zesen Shi, Wei Chen
2016 Sensors  
The basic idea was simple: we applied a machine learning algorithm to classify the neighboring Global System for Mobile (GSM) communication cellular base station's signal strength in different environments  ...  In the era of mobile internet, Location Based Services (LBS) have developed dramatically. Seamless Indoor and Outdoor Navigation and Localization (SNAL) has attracted a lot of attention.  ...  Barometer and temperature sensors are not widely available in current mobile phones.  ... 
doi:10.3390/s16101563 pmid:27669252 pmcid:PMC5087352 fatcat:h67ifkjndjbrphhpjeglpb734i

Convolutional neural network-based activity monitoring for indoor localization

László Árvai
2021 Pollack Periodica  
There is no standardized technology exists for indoor localization, usually smart phone is used as a localization platform and the field strength of an existing radio frequency infrastructure is used as  ...  Detecting the walking step, turn, stairs motion type can refine the indoor position using digital indoor map as a reference.  ...  This solution provides the most accurate result, but it is not an option for a mobile phone based indoor navigation system.  ... 
doi:10.1556/606.2021.00370 fatcat:6pfof57xwfexjgpqhif6c7uaf4

Poster

Valentin Radu, Jiwei Li, Lito Kriara, Mahesh K. Marina, Richard Mortier
2012 Proceedings of the 10th international conference on Mobile systems, applications, and services - MobiSys '12  
We propose a hybrid approach for energyefficient indoor mobile phone localization by combining two wellknown techniques namely, pedestrian dead reckoning and WiFi fingerprinting.  ...  We consider the mobile phone location tracking problem in indoor environments.  ...  In fact, researchers have already started exploiting phone sensors to realize dead reckoning based mobile location tracking systems, mainly for outdoor environments [1, 2] .  ... 
doi:10.1145/2307636.2307717 dblp:conf/mobisys/RaduLKMM12 fatcat:tsvjyivxvze6lao56vlhi6bjuq

Low-Power Ambient Sensing in Smartphones for Continuous Semantic Localization [chapter]

Sînziana Mazilu, Ulf Blanke, Alberto Calatroni, Gerhard Tröster
2013 Lecture Notes in Computer Science  
To enable a continuous observation with minimal impact on power consumption, we propose to use low-power ambient sensors -pressure, temperature, humidity and light -integrated in phones.  ...  Extracting semantic meaning of locations enables a large range of applications including automatic daily activity logging, assisted living for elderly, as well as the adaptation of phone user profiles  ...  For both baselines we use the same machine learning technique, i.e, C4.5 trees, as for our approach with low-power sensors.  ... 
doi:10.1007/978-3-319-03647-2_12 fatcat:otal4bsosbfddftuvbm6omesiy

Pedestrian Positioning Using a Double-Stacked Particle Filter in Indoor Wireless Networks

Kwangjae Sung, Hyung Kyu Lee, Hwangnam Kim
2019 Sensors  
This paper introduces a pedestrian localization scheme performed on a mobile phone that leverages the RSS fingerprint-based method, dead reckoning (DR), and improved PF called a double-stacked particle  ...  Among the Bayes filters, while the particle filter (PF) can offer the most accurate positioning performance, it may require substantial computation time due to use of many samples (particles) for high  ...  Sensors 2019, 19, 3907  ... 
doi:10.3390/s19183907 fatcat:wij6gzx37jfvrmuza5obp4vfze

Indoor Location for Smart Environments with Wireless Sensor and Actuator Networks

Zhongliang Zhao, Stephane Kuendig, Jose Carrera, Blaise Carron, Torsten Braun, Jose Rolim
2017 2017 IEEE 42nd Conference on Local Computer Networks (LCN)  
In this work, we present Smart Syndesi, a system for creating indoor location-aware smart building environments using wireless sensor and actuator networks (WSANs).  ...  The indoor positioning system tracks the real-time location of occupants with high accuracy, which works as a basis for indoor location-based sensor actuation automation.  ...  For instance, the latest machine learning algorithms enable us to design efficient and scalable indoor positioning system without calibration process, which makes the system much more stable.  ... 
doi:10.1109/lcn.2017.65 dblp:conf/lcn/ZhaoKCCBR17 fatcat:ief5glmhzrbvfe3etgyb477t4y

Indoor Location for Smart Environments with Wireless Sensor and Actuator Networks [article]

Zhongliang Zhao, Stephane Kuendig, Jose Carrera, Blaise Carron, Torsten Braun, Jose Rolim
2021 arXiv   pre-print
In this work, we present Smart Syndesi, a system for creating indoor location-aware smart building environments using wireless sensor and actuator networks (WSANs).  ...  positioning system tracks the real-time location of occupants with high accuracy, which works as a basis for indoor location-based sensor actuation automation.To show how the multiple software/hardware  ...  For instance, the latest machine learning algorithms enable us to design efficient and scalable indoor positioning system without calibration process, which makes the system much more stable.  ... 
arXiv:1705.09543v2 fatcat:ct6tdmajtnbx3lzu65zix4jmky

FloorLoc-SL: Floor Localization System with Fingerprint Self-Learning Mechanism

Kornkanok Khaoampai, Kulit Na Nakorn, Kultida Rojviboonchai
2015 International Journal of Distributed Sensor Networks  
It has a self-learning algorithm for creating fingerprint in each floor. The self-learning algorithm utilizes sensors on the mobile phone for detecting trace of mobile phone user.  ...  Nowadays, a mobile phone plays an important role in daily life. There are many applications developed for mobile phones.  ...  and floor counting methodology for fingerprint self-learning using embedded sensors on the mobile phone.  ... 
doi:10.1155/2015/523403 fatcat:mgqn6ut74jg2xgotcoab46ap6e

Poster

Valentin Radu, Panagiota Katsikouli, Rik Sarkar, Mahesh K. Marina
2014 Proceedings of the 20th annual international conference on Mobile computing and networking - MobiCom '14  
The environmental context of a mobile device determines where/how it is used, which can be exploited for efficient operation and better usability.  ...  In this work we describe a general method using only the lightweight sensors on a smartphone to detect if a device is indoor or outdoor.  ...  Thus, the estimated accuracy of GPS localization can be used to detect if a user is indoors [3] . The primary drawback of GPS is that it is among the most energy hungry sensors on the phone.  ... 
doi:10.1145/2639108.2642916 dblp:conf/mobicom/RaduKSM14 fatcat:fnuawzu2grefpdisdvfts4huye

Mobile Geospatial Computing Systems for Ubiquitous Positioning

Liang Chen, Olivier Julien, Elena-Simona Lohan, Gonzalo Seco-Granados, Ruizhi Chen
2018 Mobile Information Systems  
context sensing, to indoor map assisted localization.  ...  With the recent developments in mobile computing and sensor technologies, mobile devices are able to meet the demanding requirements for geospatial computing.  ...  Leppäkoski et al. proposes a machine learning-based method to detect the user's home, work, and other visited places by utilizing mobile phone usage features.  ... 
doi:10.1155/2018/9138095 fatcat:ninbjnjjj5cltpppc2hqr4b6fa

GPS-Based Daily Context Recognition for Lifelog Generation Using Smartphone

Go Tanaka, Masaya Okada, Hiroshi Mineno
2015 International Journal of Advanced Computer Science and Applications  
The GPS data are then used to generate classification models by machine learning.  ...  In addition, optimal learning algorithms for machine learning were determined. The experimental results show that this method is highly accurate.  ...  Machine learning is used for recognizing whether the user is mobile or stationary. The explanatory variables for machine learning are the speed and the variance of the location.  ... 
doi:10.14569/ijacsa.2015.060216 fatcat:nhjph3l2fzfd5dlm3e7psem6ue

Using Received Strength Signal Indication for Indoor Mobile Localization Based on Machine Learning Technique

Manar Joundy Hazar
2020 Webology  
The techniques of triangulation in Euclidean geometry can be considered the base of a wide range of positioning techniques for sensor networks; where they deduced the sensor locations by using its geometrical  ...  This work presents a completely different method based on machine learning, where the data is obtained directly from the natural coordinate systems through the readings provided by Bluetooth Low Energy  ...  Several algorithms of machine learning can be used for regressions or classification.  ... 
doi:10.14704/web/v17i1/a206 fatcat:2tnmhqaglvfedmbq2bv6e5atim

Indoor Floor Localization Based on Multi-Intelligent Sensors

Min Zhao, Danyang Qin, Ruolin Guo, Xinxin Wang
2020 ISPRS International Journal of Geo-Information  
Finally, the inter-floor detection link based on machine learning is added to improve the overall localization accuracy of MIS-IFL.  ...  A method for indoor floor localization using multiple intelligent sensors (MIS-IFL) is proposed to decrease the localization errors, which consists of a fingerprint database construction phase and a floor  ...  Abbreviations The following abbreviations are used in this manuscript: IFL Indoor floor localization MIS-IFL IFL based on magnetic signal using multiple intelligent sensors AP Access point K-NN K nearest  ... 
doi:10.3390/ijgi10010006 fatcat:vrjowdyew5bx7al67id7i5tnka
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