Design and Optimization Of Wireless Sensor Networks For IoT
DOI:
https://doi.org/10.22105/siot.v1i3.122Keywords:
Wireless sensor networks, Internet of things, Sensor technologies, Perception layer, Network development, Future trends, Challenges, Signal-to-noise ratio, Unattended systems, Smart networksAbstract
Wireless Sensor Networks (WSN) are an emerging multidisciplinary intersection of cutting-edge research fields, and their advantages in terms of freedom of formation, high signal-to-noise ratio, high strength, and unattended, which makes WSN have good prospects for application in the field of Internet of Things (IoT). Considering all the benefits that WSN offers, this paper reviews the development history of wireless sensor networks Internet of Things (WSN-IoT), analyzes the technologies used by sensors in the IoT, and illustrates the future developing patterns and remaining challenges, in conjunction with the leading technologies in the perception layer of the current network of things industry.
References
Zhao, X., Qu, Z., Tang, H., Tao, S., Wang, J., Li, B., & Shi, Y. (2023). A detection probability guaranteed energy-efficient scheduling mechanism in large-scale WSN. Alexandria engineering journal, 71, 451–462. https://doi.org/10.1016/j.aej.2023.03.059
Yadav, R., Sreedevi, I., & Gupta, D. (2023). Augmentation in performance and security of WSNs for IoT applications using feature selection and classification techniques. Alexandria engineering journal, 65, 461–473. https://doi.org/10.1016/j.aej.2022.10.033
Uthayakumar, G. S., Jackson, B., Ramesh Babu Durai, C., Kalaimani, A., Sargunavathi, S., & Kamatchi, S. (2023). Systematically efficiency enabled energy usage method for an IOT based WSN environment. Measurement: sensors, 25, 100615. https://doi.org/10.1016/j.measen.2022.100615
Jain, B. B., Gupta, N., & Raj, A. (2022). Numerical simulation of detection and classification of symmetrical and unsymmetrical faults using improved stockwell transform. International journal on recent technologies in mechanical and electrical engineering, 9(3), 75–80. https://doi.org/10.17762/ijrmee.v9i3.376
Suguna, M., & Sathiyabama, S. (2023). Shift invariant deep convolution neural learning for resource efficient healthcare data transmission in WSN. Measurement: sensors, 25, 100627. https://doi.org/10.1016/j.measen.2022.100627
Seyyedabbasi, A., Kiani, F., Allahviranloo, T., Fernandez-Gamiz, U., & Noeiaghdam, S. (2023). Optimal data transmission and pathfinding for WSN and decentralized IoT systems using I-GWO and Ex-GWO algorithms. Alexandria engineering journal, 63, 339–357. https://doi.org/10.1016/j.aej.2022.08.009
Santhosh, G., & Prasad, K. V. (2023). Energy optimization routing for hierarchical cluster based WSN using artificial bee colony. Measurement: sensors, 29, 100848. https://doi.org/10.1016/j.measen.2023.100848
Pavan Kumar, M. V. N. R., & Hariharan, R. (2022). Speed-up, and energy-efficient GPSR protocol for WSNs using IoT. Measurement: sensors, 23, 100411. https://doi.org/10.1016/j.measen.2022.100411
Priyanka, B. H. D. D., Udayaraju, P., Koppireddy, C. S., & Neethika, A. (2023). Developing a region-based energy-efficient IoT agriculture network using region- based clustering and shortest path routing for making sustainable agriculture environment. Measurement: sensors, 27, 100734. https://doi.org/10.1016/j.measen.2023.100734
Naeem, A. B., Senapati, B., Chauhan, A. S., Kumar, S., Orosco, J. C., & Gavilan, W. M. (2023). Deep learning models for cotton leaf disease detection with VGG-16. International journal of intelligent systems and applications in engineering, 11(2), 550–556. https://www.researchgate.net/publication/369377736
Li, W., & Kara, S. (2017). Methodology for monitoring manufacturing environment by using wireless sensor networks (WSN) and the internet of things (IoT). Procedia cirp, 61, 323–328. https://doi.org/10.1016/j.procir.2016.11.182
Fernandes, R.F., de Almeida, M.B. & Brandão, D. (2018). An energy efficient receiver-initiated MAC protocol for low-power WSN. Wireless pers commun, 100, 1517–1536. https://doi.org/10.1007/s11277-018-5651-3
Onasanya, A., & Elshakankiri, M. (2019). Secured cancer care and cloud services in iot/wsn based medical systems bt-smart grid and internet of things (pp. 23–35). Cham: springer international publishing. https://doi.org/10.1007/978-3-030-05928-6_3
Izaddoost, A., & Siewierski, M. (2020). Energy efficient data transmission in IoT platforms. Procedia computer science, 175, 387–394. https://doi.org/10.1016/j.procs.2020.07.055
Co, K. J., Ong, A. V., & Peradilla, M. (2021). WSN data collection and routing protocol with time synchronization in low-cost IoT environment. Procedia computer science, 191, 102–110. https://doi.org/10.1016/j.procs.2021.07.016
Kaur, L., & Kaur, R. (2021). A survey on energy efficient routing techniques in WSNs focusing IoT applications and enhancing fog computing paradigm. Global transitions proceedings, 2(2), 520–529. https://doi.org/10.1016/j.gltp.2021.08.001
Khetani, V., Gandhi, Y., Bhattacharya, S., Ajani, S. N., & Limkar, S. (2023). Cross-domain analysis of ML and DL: evaluating their impact in diverse domains. International journal of intelligent systems and applications in engineering, 11(7s), 253–262. https://www.ijisae.org/index.php/IJISAE/article/view/2951
Erhan, İ. M., & Georgiev, S. (2021). Adomian polynomials method for dynamic equations on time scales. Advances in the theory of nonlinear analysis and its application, 5(3), 300–315. https://doi.org/10.31197/atnaa.879367
Sherje, D. N. (2022). Content based image retrieval based on feature extraction and classification using deep learning techniques. Research journal of computer systems and engineering, 2(1), 16:22. https://doi.org/10.52710/rjcse.14
Hamza, B., Lakhal, H., Kamel, S., & Tahar, B. (2023). The nontrivial solutions for nonlinear fractional Schrödinger-Poisson system involving new fractional operator. Advances in the theory of nonlinear analysis and its application, 7(1), 121–132. https://doi.org/10.31197/atnaa.1141136