Open Journal Systems

A novel relay placement method for smart energy IoT systems in offices with genetic algorithm

Nazanin Moosavi, Hassan Daryanavard

Article ID: 2183
Vol 1, Issue 1, 2023

DOI: https://doi.org/10.54517/cte.v1i1.2183
VIEWS - 194 (Abstract)

Abstract

Smart energy in large offices and organizations is an important research area of the Internet of Things (IoT). The energy efficiency of buildings is vital for the environment and global sustainability. To achieve satisfactory performance for this goal, WiFi access point (AP) indoor coverage is of high importance. As it costs a lot to add more WiFi to have good coverage in all parts of the office’s buildings, we consider relay node (RN) instead of adding more APs. So in this paper, we propose a novel RN placement in order to improve the indoor coverage of offices considering the signal attenuation using a path-loss model as the main measure for determining positions. The main problem is the placement of these RNs and the required number of them by considering existing APs. At first, we obtain the radio propagation model parameters by considering the data that are collected from the AP. Then based on these parameters, the proposed solution uses a genetic algorithm (GA) for RN placement optimization problem. The experimental results display the effectiveness of the proposed solution for the RN placement problem.

Keywords

smart office; Internet of Things (IoT); energy efficiency; relay node placement; path-loss; genetic algorithm

Full Text:

PDF

References

1. Ashraf N, Sheikh SA, Khan SA, et al. Simultaneous wireless information and power transfer with cooperative relaying for next-generation wireless networks: A review. IEEE Access 2021; 9: 71482–71504. doi: 10.1109/ACCESS.2021.3078703

2. Zannou A, Boulaalam A, Nfaoui EH. An optimal base stations positioning for the Internet of Things devices. In: Proceedings of 2021 7th International Conference on Optimization and Applications (ICOA); 19–20 May 2021; Wolfenbüttel, Germany.

3. Laghari AA, Wu K, Laghari RA, et al. A review and state of art of Internet of Things (IoT). Archives of Computational Methods in Engineering 2022; 29: 1395–1413. doi: 10.1007/s11831-021-09622-6

4. Heidari A, Navimipour NJ, Jamali MAJ, Akbarpour S. A green, secure, and deep intelligent method for dynamic IoT-edge-cloud offloading scenarios. Sustainable Computing: Informatics and Systems 2023; 38: 100859. doi: 10.1016/j.suscom.2023.100859

5. Heidari A, Jamali MAJ, Navimipour NJ, Akbarpour S. A QoS-aware technique for computation offloading in IoT-edge platforms using a convolutional neural network and Markov decision process. IT Professional 2023; 25(1): 24–39. doi: 10.1109/MITP.2022.3217886

6. Ahmadpour SS, Heidari A, Navimpour NJ, et al. An efficient design of multiplier for using in nano-scale IoT systems using atomic silicon. IEEE Internet of Things Journal 2023; 10(16): 14908–14909. doi: 10.1109/JIOT.2023.3267165

7. Ahdan S, Susanto ER, Syambas NR. Proposed design and modeling of smart energy dashboard system by implementing IoT (Internet of Things) based on mobile devices. In: Proceedings of 2019 IEEE 13th International Conference on Telecommunication Systems, Services, and Applications (TSSA); 3–4 October 2019; Bali, Indonesia. pp. 194–199.

8. Hossein Motlagh N, Mohammadrezaei M, Hunt J, Zakeri B. Internet of Things (IoT) and the energy sector. Energies 2020; 13(2): 494. doi: 10.3390/en13020494

9. Casado-Vara R, Martin-del Rey A, Affes S, et al. IoT network slicing on virtual layers of homogeneous data for improved algorithm operation in smart buildings. Future Generation Computer Systems 2020; 102: 965–977. doi: 10.1016/j.future.2019.09.042

10. Rahim A, Rahman A, Rahman MM, et al. Evolution of IoT-enabled connectivity and applications in automotive industry: A review. Vehicular Communications 2021; 27: 100285. doi: 10.1016/j.vehcom.2020.100285

11. Azharuddin M, Jana PK. A GA-based approach for fault tolerant relay node placement in wireless sensor networks. In: Proceedings of the 2015 Third International Conference on Computer, Communication, Control and Information Technology (C3IT); 7–8 February 2015; Hooghly, India. pp. 1–6.

12. Redhu S, Hegde RM. Optimal relay node selection in time-varying IoT networks using apriori contact pattern information. Ad Hoc Networks 2020; 98: 102065. doi: 10.1016/j.adhoc.2019.102065

13. Sarwesh P, Shet NSV, Chandrasekaran K. Effective integration of reliable routing mechanism and energy efficient node placement technique for low power IoT networks. International Journal of Grid and High Performance Computing 2017; 9(4): 16–35. doi: 10.4018/IJGHPC.2017100102

14. George J, Sharma RM. Relay node placement in wireless sensor networks using modified genetic algorithm. In: Proceedings of 2016 2nd International Conference on Applied and Theoretical Computing and Communication Technology (iCATccT); 21–23 July 2016; Bangalore, India. pp. 551–556.

15. Gupta SK, Kuila P, Jana PK. Genetic algorithm for k-connected relay node placement in wireless sensor networks. In: Kacprzyk J (editor). Advances in Intelligent Systems and Computing, Proceedings of the Second International Conference on Computer and Communication Technologies. Springer New Delhi; 2016. pp. 721–729.

16. Shukla A, Tripathi S. An effective relay node selection technique for energy efficient WSN-assisted IoT. Wireless Personal Communications 2020; 112(4): 2611–2641. doi: 10.1007/s11277-020-07167-8

17. Alsmady A, Awad F. Optimal Wi-Fi access point placement for RSSI-based indoor localization using genetic algorithm. In: Proceedings of 2017 8th International Conference on Information and Communication Systems (ICICS); 4–6 April 2017; Irbid, Jordan. pp. 287–291.

18. Saki M, Abolhasan M, Lipman J, Jamalipour A. A comprehensive access point placement for IoT data transmission through train-wayside communications in multi-environment based rail networks. IEEE Transactions on Vehicular Technology 2020; 69(10): 11937–11949. doi: 10.1109/TVT.2020.3006321

19. Lembo S, Horsmanheimo S, Honkamaa P. Indoor positioning based on RSS fingerprinting in a LTE network: Method based on genetic algorithms. In: Proceedings of 2019 IEEE International Conference on Communications Workshops (ICC Workshops); 20–24 May 2019; Shanghai, China.

20. Zhou B, Tu W, Mai K, et al. A novel access point placement method for WiFi fingerprinting considering existing Aps. IEEE Wireless Communications Letters 2020; 9(11): 1799–1802. doi: 10.1109/LWC.2020.2981793

21. Rappaport TS. Wireless Communications-Principles and Practice. Prentice Hall; 2002.