CFP last date
20 March 2025
Reseach Article

Optimizing Smart Library Spaces: Integrating PIR Sensors, Credit-based Booking Systems, and Advanced Algorithms for Efficient Resource Management and Space Allocation

by Mohamud Rashid Mohamud, H. Chege Nganga, Stephen Kiambi
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 186 - Number 63
Year of Publication: 2025
Authors: Mohamud Rashid Mohamud, H. Chege Nganga, Stephen Kiambi
10.5120/ijca2025924455

Mohamud Rashid Mohamud, H. Chege Nganga, Stephen Kiambi . Optimizing Smart Library Spaces: Integrating PIR Sensors, Credit-based Booking Systems, and Advanced Algorithms for Efficient Resource Management and Space Allocation. International Journal of Computer Applications. 186, 63 ( Jan 2025), 55-60. DOI=10.5120/ijca2025924455

@article{ 10.5120/ijca2025924455,
author = { Mohamud Rashid Mohamud, H. Chege Nganga, Stephen Kiambi },
title = { Optimizing Smart Library Spaces: Integrating PIR Sensors, Credit-based Booking Systems, and Advanced Algorithms for Efficient Resource Management and Space Allocation },
journal = { International Journal of Computer Applications },
issue_date = { Jan 2025 },
volume = { 186 },
number = { 63 },
month = { Jan },
year = { 2025 },
issn = { 0975-8887 },
pages = { 55-60 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume186/number63/optimizing-smart-library-spaces-integrating-pir-sensors-credit-based-booking-systems-and-advanced-algorithms-for-efficient-resource-management-and-space-allocation/ },
doi = { 10.5120/ijca2025924455 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2025-01-31T17:28:30.754556+05:30
%A Mohamud Rashid Mohamud
%A H. Chege Nganga
%A Stephen Kiambi
%T Optimizing Smart Library Spaces: Integrating PIR Sensors, Credit-based Booking Systems, and Advanced Algorithms for Efficient Resource Management and Space Allocation
%J International Journal of Computer Applications
%@ 0975-8887
%V 186
%N 63
%P 55-60
%D 2025
%I Foundation of Computer Science (FCS), NY, USA
Abstract

A smart library is designed to bring digital intelligence to objects and spaces, enabling real-time data-informed decisions. This aspect is enabled by the use of sensors that are connected to the internet, all collecting and sharing data and, in this case, data about traffic information in the library, enabling users to book seats, pinpoint locations of vacancy, and intentionally manage energy (power) and resources (labor). The benefit of monitoring occupancy data is introducing an efficient communication channel, revolutionizing a traditional library into a data source hub. This allows a smooth interaction with the user, monetization of the service, adjustment of spatial design and traffic flow, and movement to greener spaces. This paper provides an approach to the above advantages by incorporating a hardware system involving Passive Infrared (PIR) sensors, a node Micro-controller Unit (MCU), and a web app. The prototype system allows seat booking, calculation of library credits, and library occupancy prediction using the Random Forest model and Random Forest Regressor to optimize space allocation and resource management.

References
  1. Adaora Joy Udo-Anyanwu, “CHAPTER ONE ORIGIN OF LIBRARIES,” ResearchGate, pp. 1–22, Nov. 2021, Available:https://www.researchgate.net/publication/355942072_CHAPTER_ONE_ORIGIN_OF_LIBRARIES
  2. A. Ullah, M. Usman, and M. K. Khan, “Challenges in Delivering Modern Library Services in the 21st Century,” International Journal of Social Science Exceptional Research, vol. 2, no. 6, pp. 146–151, Jan. 2023, doi: https://doi.org/10.54660/ijsser.2023.2.6.146-151
  3. N. Haidar, Nouredine Tamani, Yacine Ghamri-Doudane, and Alain Bouju, “Occupant Behavior Prediction and Real-Time Correction-based Smart Building Energy Optimization,” GLOBECOM 2022 - 2022 IEEE Global Communications Conference, pp. 1–6, Dec. 2020, doi: https://doi.org/10.1109/globecom42002.2020.9348056.
  4. Q. Wang, H. Patel, and L. Shao, “A longitudinal study of the occupancy patterns of a university library building using thermal imaging analysis,” Intelligent Buildings International, vol. 15, no. 2, pp. 62–77, Nov. 2022, doi: https://doi.org/10.1080/17508975.2022.2147129.
  5. P. Liu, “Design and Implementation of Library Seating Management System,” Journal of Computer and Communications, vol. 12, no. 08, pp. 292–306, Jan. 2024, doi: https://doi.org/10.4236/jcc.2024.128018.
  6. A. Al-Fuqaha, M. Guizani, M. Mohammadi, M. Ayyash, S. A. Shah, and D. D. S. P. P., "Internet of Things: A Survey on Enabling Technologies, Protocols, and Applications," IEEE Communications Surveys & Tutorials, vol. 17, no. 4, pp. 2347-2376, Fourth Quarter 2015. doi: 10.1109/COMST.2015.2444095.
  7. J. Gubbi, R. Buyya, S. Marusic, and M. Palaniswami, "Internet of Things (IoT): A Vision, Architectural Elements, and Future Directions," Future Generation Computer Systems, vol. 29, no. 7, pp. 1645-1660, Sep. 2013. doi: 10.1016/j.future.2013.01.010.
  8. A. Zanella, N. Bui, A. Castellani, L. V. S. Pellegrini, and M. Zorzi, "Internet of Things: A Survey on Technologies, Protocols, and Applications," Computer Networks, vol. 56, no. 15, pp. 2787-2805, Oct. 2014. doi: 10.1016/j.comnet.2014.05.014.
  9. Hassan, Qusay; Khan, Atta; Madani, Sajjad (2018). Internet of Things: Challenges, Advances, and Applications. Boca Raton, Florida: CRC Press. p. 198.
  10. “IoT ecosystem: 4 key elements” [Online]. Available: https://www.avsystem.com/blog/iot-ecosystem/
  11. Okoronkwo et al, “Smart Library Seat, Occupant and Occupancy Information System, using Pressure and RFID Sensors. 10.1109/NEXTCOMP.2019.8883610.
  12. D. Chang and M. Kim, “Library Occupancy Sensor,” Jan. 2020.
  13. Azad Shokrollahi, J. A. Persson, Reza Malekian, Arezoo Sarkheyli-Hägele, and F. Karlsson, “Passive Intfrared Sensor-Based Occupancy Monitoring in Smart Buildings: A Review of Methodologies and Machine Learning Approaches,” Sensors, vol. 24, no. 5, pp. 1533–1533, Feb. 2024, doi: https://doi.org/10.3390/s24051533.
  14. GeeksforGeeks, “Supervised vs Reinforcement vs Unsupervised,” GeeksforGeeks, Sep. 19, 2024. https://www.geeksforgeeks.org/supervised-vs-reinforcement-vs-unsupervised/
  15. W. Wang, J. Chen, and T. Hong, “Occupancy prediction through machine learning and data fusion of environmental sensing and Wi-Fi sensing in buildings,” Automation in Construction, vol. 94, pp. 233–243, Oct. 2018, doi: https://doi.org/10.1016/j.autcon.2018.07.007.
  16. B. Sirmacek and M. Riveiro, “Occupancy Prediction Using Low-Cost and Low-Resolution Heat Sensors for Smart Offices,” Sensors, vol. 20, no. 19, p. 5497, Sep. 2020, doi: https://doi.org/10.3390/s20195497.
  17. D. Hong, Q. Gu, and K. Whitehouse, “High-dimensional Time Series Clustering via Cross-Predictability.,” International Conference on Artificial Intelligence and Statistics, pp. 642–651, Apr. 2017.
  18. JavaTpoint, “Machine Learning Random Forest Algorithm - Javatpoint,” www.javatpoint.com, 2021. https://www.javatpoint.com/machine-learning-random-forest-algorithm
Index Terms

Computer Science
Information Sciences
IoT
Machine Learning
Smart Spaces
Artificial Intelligence
Energy Efficiency
Predictive Analytics
Credits
Micro-controllers

Keywords

PIR Sensors NodeMCU Random Forest Random Forest Regressor Occupancy Model