A Power-Saving Classification Module for The Internet of Things ‎Enclosed Wireless Sensors Embedded in Smart Controllers

  • Authors

    • Dr. R. Gayathri Assistant Professor, Department of Electronics and Communication Engineering, ‎Karpagam Academy of Higher Education, Coimbatore
    • Dr. P. Gnanasundari Associate Professor, RV University, Bangalore
    • Dr. S. Padmanaban Professor, Department of Mathematics - Science and Humanities, ‎Karpagam Institute of Technology, Coimbatore ‎
    • Dr. B. Suganthi Associate Professor, Department of ECE, RVS College of Engineering and Technology, Coimbatore
    • Poornima R. Assistant Professor, Department of ECE, SNS College of Technology, Coimbatore
    • Prof. Nitin Chakole Assistant Professor, Department of Electronics and Communication Engineering, ‎ Ramdeobaba University, Nagpur
    https://doi.org/10.14419/q2va4b09

    Received date: September 11, 2025

    Accepted date: November 10, 2025

    Published date: November 15, 2025

  • DRL; IoT; Power Saving; Process Allocation; Smart Controllers

  • Abstract

    Internet of Things (IoT), combined with wireless sensor network (WSN) devices, interacts with smart controllers within industries or buildings, etc., to accomplish field jobs. The jobs/ tasks are completed based on the controller's decision, power-saving, and sustainable operation ‎intervals. To improve the power savings of such devices, a Control-dependent Process Classification Module (CdPCM) is proposed in this ‎article. This proposed module balances the controller operation time and the number of input controls based on a low-power sustainable ‎feature. The feature identifies the maximum operation time of the controller before reaching the recharging state. In this process, the IoT ‎layers are responsible for commissioning and decommissioning jobs for the low-power operations. Thus, the input controls are relocated ‎from the overloaded controllers to sustain their active state until an alternate controller is reassigned. Thus, the power-saving schedules are ‎allocated for the sensor-based controllers to accomplish the tasks with minimal inactive time. The factor classification based on the above ‎feature is recurrent until the controller operation interval is active, using deep recurrent learning networks. The proposed module improves ‎power saving by 11.22%, reduces the power consumption by 11.03% and idle power loss by 10.67% for the varying job intervals‎.

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  • How to Cite

    Gayathri, D. R. . ., Gnanasundari, D. P. . ., Padmanaban , D. S. ., Suganthi , D. B. ., R. , P. ., & Chakole, P. N. . . (2025). A Power-Saving Classification Module for The Internet of Things ‎Enclosed Wireless Sensors Embedded in Smart Controllers. International Journal of Basic and Applied Sciences, 14(7), 414-423. https://doi.org/10.14419/q2va4b09