Machinery & Energetics

Exploring novel architectural elements and design concepts for wireless sensor nodes: A case study

Saurabh Mehta, Riya Modi, Pavlo Herasymenko, Serhii Usenko
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Abstract

Wireless Sensor Networks (WSNs) play a crucial role in modern communication systems, enabling efficient data collection and transmission across various domains. These networks are pivotal in applications such as environmental monitoring, industrial automation, smart cities, and healthcare systems, where real-time and accurate data is essential. The purpose of this study was to develop and validate a novel wireless sensor node architecture for gas sensing applications, with a focus on improving energy efficiency, communication reliability, modularity, and adaptability to diverse environments. The proposed design integrated multiple radio chips, a stack-based architecture, and directional antennas to improve communication robustness and adaptability. A current driver circuit was introduced to mitigate sensor saturation, ensuring accurate gas concentration measurements. The stack-based architecture enhanced modularity and scalability, facilitating seamless upgrades and compatibility with emerging technologies. Additionally, directional antennas and a dedicated medium access control protocol optimised communication efficiency and localisation accuracy. Experimental evaluations, conducted within advanced research facilities, validated the effectiveness of the proposed design under diverse environmental conditions. The hardware design approach presented in this paper focused on using low-cost, off-the-shelf components strategically integrated to deliver high functionality without increasing complexity or cost. The modular design also allows additional sensing features – such as temperature, humidity, or particulate matter detection – to be incorporated easily and economically. The results demonstrated significant improvements in data transmission reliability, power consumption optimisation, and sensor longevity. The system achieved a favourable balance between performance and affordability, making it highly suitable for scalable deployment in resource-constrained settings. This work contributes to the advancement of WSN-based gas monitoring by addressing critical challenges in hardware selection, communication efficiency, and adaptive sensor operation. The architecture presents a promising solution for deployment in dynamic and harsh environments where traditional systems may fail

Keywords

hardware platforms, node design, patch directional antenna, transceiver, microcontroller, wireless sensor network

Suggested citation
Mehta, S., Modi, R., Herasymenko, P., & Usenko, S. (2025). Exploring novel architectural elements and design concepts for wireless sensor nodes: A case study. Machinery & Energetics, 16(2), 108-116. https://doi.org/10.31548/machinery/2.2025.108
References
  1. Aghaei, B. (2011). Using wireless sensor networks in water, electricity, and gas industries. In Proceedings of the 3rd international conference on electronics computer technology (pp. 14-17). Kanyakumari: IEEE. doi: 10.1109/ICECTECH.2011.5941646.
  2. Amjad, M., Sharif, M., Afzal, M.K., & Kim, S.W. (2016). TinyOS – new trends, comparative views, and supported sensing applications: A review. IEEE Sensors Journal, 16(9), 2865-2889. doi: 10.1109/JSEN.2016.2519924.
  3. Bekal, P., Kumar, P., Mane, P., & Prabhu, G. (2024). A comprehensive review of energy-efficient routing protocols for query-driven wireless sensor networks. F1000Research, 12, article number 644. doi: 10.12688/f1000research.133874.3.
  4. Bhat, A.W., & Passi, A. (2022). Wireless sensor network motes: A comparative study. In Proceedings of the 9th international conference on computing for sustainable global development (INDIACom) (pp. 141-144). New Delhi: IEEE. doi: 10.23919/INDIACom54597.2022.9763269.
  5. Busacca, F., Galluccio, L., Palazzo, S., Panebianco, A., & Scarvaglieri, A. (2024). Balancing optimization for underwater network cost effectiveness (BOUNCE): A multi-armed bandit solution. In 2024 IEEE international conference on communications workshops (ICC Workshops) (pp. 1340-1345). Denver: IEEE. doi: 10.1109/ICCWorkshops59551.2024.10615825.
  6. Divyansh, T., Yugal, K., Arvind, K., & Pradeep, K.S. (2019). Applicability of wireless sensor networks in precision agriculture: A review. Wireless Personal Communications, 107, 471-512. doi: 10.1007/s11277-019-06285-2.
  7. Ezeigweneme, C., Nwasike, C., Adekoya, O., Biu, P., & Gidiagba, J. (2024). Wireless communication in electro-mechanical systems: Investigating the rise and implications of cordless interfaces for system enhancement. Engineering Science & Technology Journal, 5, 21-42. doi: 10.51594/estj.v5i1.720.
  8. Gong, L., Sharma, A., Henry, J., & Youssef, M. (2021). A novel solar harvesting modular wireless sensor mote for greenhouse applications: Design & implementation. In Proceedings of the IEEE energy conversion congress and exposition (ECCE) (pp. 802-807). Vancouver: IEEE. doi: 10.1109/ECCE47101.2021.9595744.
  9. Johnson, M., et al. (2009). A comparative review of wireless sensor network mote technologies. In Proceedings of IEEE SENSORS conference (pp. 1439-1442). Christchurch: IEEE. doi: 10.1109/ICSENS.2009.5398442.
  10. Kaddi, M., Omari, M., & Alnatoor, M. (2024). EECLP: A wireless sensor networks energy-efficient cross-layer protocol. Computers, Materials & Continua, 80, 1-10. doi: 10.32604/cmc.2024.052048.
  11. Kasar, S.P., Shekhar, A.K., Shah, M., & Mehta, S. (2013). An overview of localization techniques and algorithms for wireless sensor networks. In Proceedings of TEL-NET conference (pp. 261-265). Abu Dhabi: IEEE. doi: 10.1109/INNOVATIONS.2011.5893810.
  12. Luo, Y., Yang, Y., Hong, H., & Dai, Z. (2024). A simultaneous wireless power and data transfer system with full-duplex mode for underwater wireless sensor networks. IEEE Sensors Journal, 24(8), 12570-12583. doi: 10.1109/JSEN.2024.3373789.
  13. Maia, G., Guidoni, D.L., Aquino, A.L.L., & Loureiro, A.A.F. (2009). Improving an over-the-air programming protocol for wireless sensor networks based on small-world concepts. In Proceedings of the 12th MSWiM conference (pp. 261-267). New York: Association for Computing Machinery. doi: 10.1145/1641804.1641848.
  14. Mazin, М., & Onykiienko, Yu. (2023). Peculiarities of using Huffman and RLE methods for image compression in microcontroller systems. Technologies and Engineering, 24(6), 21-30. doi: 10.30857/2786-5371.2023.6.2.
  15. Mekonnen, T., Porambage, P., Harjula, E., & Ylianttila, M. (2017). Energy consumption analysis of high-quality multi-tier wireless multimedia sensor networks. IEEE Access, 5, 15848-15858. doi: 10.1109/ACCESS.2017.2737078.
  16. Petersen, S., Doyle, P., Vatland, S., Aasland, C.S., Andersen, T.M., & Sjong, D. (2007). Requirements, drivers, and analysis of wireless sensor network solutions for the oil & gas industry. Proceedings of the IEEE conference on emerging technologies and factory automation (EFTA 2007) (pp. 219-226). Patras: IEEE. doi: 10.1109/EFTA.2007.4416773.
  17. Qi, R., & Li, R. (2022). Network multimedia table tennis teaching design based on embedded microprocessor. Wireless Communications and Mobile Computing, 2022, article number 019857. doi: 10.1155/2022/2019857.
  18. Ricciardella, F., Vollebregt, S., Polichetti, T., Alfano, B., Massera, E., & Sarro, P.M. (2017). An innovative approach to overcome saturation and recovery issues of CVD graphene-based gas sensors. In Proceedings of IEEE SENSORS conference (pp. 1-3). Glasgow: IEEE. doi: 10.1109/ICSENS.2017.8234284.
  19. Shah, M., & Mehta, S. (2013). A discussion on hardware platforms for wireless sensor networks. In Proceedings of TEL-NET conference (pp. 253-260). Noida: Amity University.
  20. Singh, Y., & Walingo, T. (2024). Smart water quality monitoring with IoT wireless sensor networks. Sensors, 24(9), article number 2871. doi: 10.3390/s24092871.
  21. Thakkar, A., Chaudhari, K., & Shah, M. (2020). A comprehensive survey on energy-efficient power management techniques. Procedia Computer Science, 167, 1189-1199. doi: 10.1016/j.procs.2020.03.432.
  22. Yokoyama, T., Narieda, S., & Naruse, H. (2024). Optimization of sensor node placement for CO2 concentration monitoring based on wireless sensor networks in an indoor environment. IEEE Sensors Letters, 8(4), 1-4. doi: 10.1109/LSENS.2024.3373369.