Login Register Cart 0
Generic placeholder image

International Journal of Sensors, Wireless Communications and Control

Editor-in-Chief

ISSN (Print): 2210-3279
ISSN (Online): 2210-3287

Back
Journal Home About Journal Editorial Board Journal Insight Current Issue Volumes/Issues
Subscribe
Research Article

Crowd-sourced AI based Indoor Localization using Support Vector Regression and Pedestrian Dead Reckoning

Author(s): Thandu Nagaraju* and Murugeswari Rathinam

Volume 13, Issue 3, 2023

Published on: 15 June, 2023

Page: [155 - 166] Pages: 12

DOI: 10.2174/2210327913666230523114125

Price: $65

Abstract

Aims and Background: Artificial intelligence (AI) is expanding in the market daily to assist humans in a variety of ways. However, as these models are expensive, there is still a gap in the availability of AI products to the common public with high component dependency.

Objectives and Methodology: To address the issue of additional component dependency on AI products, we propose a model that can use available Smartphone resources to perceive real-world huddles and assist ordinary people with their daily needs. The proposed AI model is to predict the user’s indoor position (Node) at the computer science and engineering block of CMR Institute of Technology (CMRIT) by using Smartphone sensors and wireless signals. We used SVR to predict the regular walk steps needed between two Nodes and Pedestrian Dead Reckoning (PDR) to predict the walk steps needed while the signal was lost in the indoor environment.

Results: The Support vector regression (SVR) models make the locations to be available within the specified building boundaries for proper guidance. The PDR approach supports the user while signal loss between two Received Signal Strength Indicators (RSSI). The Pedestrian dead reckoning - Support Vector Regression (PD-SVR) results are showing 98% accuracy in NODE predictions with routing tables. The indoor positioning is 100% accurate with dynamic crowd-sourcing Node preparation.

Conclusion: The results are compared with other indoor navigation models K-nearest neighbor (KNN) and DF-SVM are given 95% accurate NODE estimation with minimal need for network components.

« Previous Next »
Graphical Abstract

[1]
J. Yan, G. He, A. Basiri, and C. Hancock, "3-D passive-vision-aided pedestrian dead reckoning for indoor positioning", IEEE Trans. Instrum. Meas., vol. 69, no. 4, pp. 1370-1386, 2020.
[http://dx.doi.org/10.1109/TIM.2019.2910923]
[2]
A. Singhal, S. Varshney, T.A. Mohanaprakash, R. Jayavadivel, K. Deepti, P.C.S. Reddy, and M.B. Mulat, "Minimization of latency using multitask scheduling in industrial autonomous systems", Wirel. Commun. Mob. Comput., vol. 2022, pp. 1-10, 2022.
[http://dx.doi.org/10.1155/2022/1671829]
[3]
B. Lashkari, J. Rezazadeh, R. Farahbakhsh, and K. Sandrasegaran, "Crowdsourcing and sensing for indoor localization in IoT: A review", IEEE Sens. J., vol. 19, no. 7, pp. 2408-2434, 2019.
[http://dx.doi.org/10.1109/JSEN.2018.2880180]
[4]
L. Sujihelen, R. Boddu, S. Murugaveni, M. Arnika, A. Haldorai, P.C.S. Reddy, S. Feng, and J. Qin, "Node replication attack detection in distributed wireless sensor networks", Wirel. Commun. Mob. Comput., vol. 2022, pp. 1-11, 2022.
[http://dx.doi.org/10.1155/2022/7252791]
[5]
Y. Sucharitha, Y. Vijayalata, and V.K. Prasad, "Predicting election results from twitter using machine learning algorithms", Recent Adv. Comput. Sci. Commun., vol. 14, no. 1, pp. 246-256, 2021.
[http://dx.doi.org/10.2174/2666255813999200729164142]
[6]
S. Suresh, V. Prabhu, V. Parthasarathy, R. Boddu, Y. Sucharitha, and G. Teshite, "A novel routing protocol for low-energy wireless sensor networks", J. Sens., vol. 2022, pp. 1-8, 2022.
[http://dx.doi.org/10.1155/2022/8244176]
[7]
K.A. Muthappa, A.S.A. Nisha, R. Shastri, V. Avasthi, and P.C.S. Reddy, "Design of high-speed, low-power non-volatile master slave flip flop (NVMSFF) for memory registers designs", Appl. Nanosci., pp. 1-10, 2023.
[http://dx.doi.org/10.1007/s13204-023-02814-5]
[8]
R.P.C. Shaker, and A. Sureshbabu, "An enhanced multiple linear regression model for seasonal rainfall prediction", Int. J. Sensors Wirel. Commun. Control, vol. 10, no. 4, pp. 473-483, 2020.
[http://dx.doi.org/10.2174/2210327910666191218124350]
[9]
Q. Li, X. Liao, and Z. Gao, "Indoor localization with particle filter in multiple motion patterns", 2020 IEEE Wireless Communications and Networking Conference (WCNC), 2020, pp. 1-6.
[http://dx.doi.org/10.1109/WCNC45663.2020.9120637]
[10]
R.P.C. Shaker, and Y. Sucharitha, "IoT-enabled energy-efficient multipath power control for underwater sensor networks", Int. J. Sensors Wirel. Commun. Control, vol. 12, no. 6, pp. 478-494, 2022.
[http://dx.doi.org/10.2174/2210327912666220615103257]
[11]
T. Kumrai, J. Korpela, Y. Zhang, K. Ohara, and T. Murakami, "Automated construction of Wi-Fi-based indoor logical location predictor using crowd-sourced photos with Wi-Fi signals", Pervasive Mob. Comput, p. 101742, 2023.
[12]
K.S. Anusha, R. Ramanathan, and M. Jayakumar, "Link distance-support vector regression (LD-SVR) based device free localization technique in indoor environment", Eng. Sci. Technol. an Int. J., vol. 23, no. 3, pp. 483-493, 2020.
[13]
I. Ashraf, M. Kang, S. Hur, and Y. Park, "MINLOC: Magnetic field patterns-based indoor localization using convolutional neural networks", IEEE Access, vol. 8, pp. 66213-66227, 2020.
[http://dx.doi.org/10.1109/ACCESS.2020.2985384]
[14]
K.S. Hosseini, M.H. Azaddel, M.A. Nourian, and A.A. Azirani, "Improving multi-floor wifi-based indoor positioning systems by fingerprint grouping", 2021 5th International Conference on Internet of Things and Applications (IoT), 2021.
[http://dx.doi.org/10.1109/IoT52625.2021.9469602]
[15]
A. Imran, H. Soojung, and P. Yongwan, "Enhancing performance of magnetic field based indoor localization using magnetic patterns from multiple smartphones", Sensors, vol. 20, no. 9, p. 2704, 2020.
[16]
F. Liu, J. Wang, J. Zhang, and H. Han, "An indoor localization method for pedestrians base on combined UWB/PDR/Floor Map", Sensors, vol. 19, no. 11, p. 2578, 2019.
[http://dx.doi.org/10.3390/s19112578] [PMID: 31174314]
[17]
P.C.S. Reddy, M. Pradeepa, S. Venkatakiran, R. Walia, and M. Saravanan, "Image and signal processing in the underwater environment", J. Nucl. Eng. Sci. Power Generat. Technol., vol. 10, no. 9, p. 2, 2021.
[18]
Y. Sucharitha, S. Vinothkumar, V. Rao Vadi, S. Abidin, and N. Kumar, "Wireless communication without the need for pre-shared secrets is consummate via the use of spread spectrum technology", J Nucl Ene Sci Power Generat Techno, vol. 10, no. 9, p. 2, 2021.
[19]
P.C. Reddy, and A.S. Babu, "A novel approach to analysis district level long scale seasonal forecasting of monsoon rainfall in andhra pradesh and telangana", Int. J. Adv. Res, vol. 8, no. 9, pp. 245-250, 2017.
[http://dx.doi.org/10.26483/ijarcs.v8i9.4928]
[20]
P.C.S. Reddy, G. Suryanarayana, and S. Yadala, "Data analytics in farming: rice price prediction in andhra pradesh", 2022 5th International Conference on Multimedia, Signal Processing and Communication Technologies (IMPACT), 2022, pp. 1-5.
[21]
R. Sabitha, A.P. Shukla, A. Mehbodniya, and L. Shakkeera, "A fuzzy trust evaluation of cloud collaboration outlier detection in wireless sensor networks", Ad Hoc Sens. Wirel. Netw., p. 53, 2022.

Rights & Permissions Print Cite
© 2024 Bentham Science Publishers | Privacy Policy