Optimizing Internet-of-Things Energy Management: Integrating Theory of Inventive Problem Solving with Transfer Learning and Advanced Optimization Algorithms | Zendy

Abdul Razaque | Zendy; Meer Jaro Khan | Zendy; Dina S.M. Hassan | Zendy; Aizhan Kassymova | Zendy; Syed Rizvi | Zendy; Arslan Ali | Zendy; Vasily Valerievich Serbin | Zendy

Open Access

Optimizing Internet-of-Things Energy Management: Integrating Theory of Inventive Problem Solving with Transfer Learning and Advanced Optimization Algorithms

Author(s) -

Abdul Razaque,

Meer Jaro Khan,

Dina S.M. Hassan,

Aizhan Kassymova,

Syed Rizvi,

Arslan Ali,

Vasily Valerievich Serbin

Publication year - 2025

Publication title -

ieee access

Language(s) - English

Resource type - Magazines

SCImago Journal Rank - 0.587

H-Index - 127

eISSN - 2169-3536

DOI - 10.1109/access.2025.3590050

Subject(s) - aerospace , bioengineering , communication, networking and broadcast technologies , components, circuits, devices and systems , computing and processing , engineered materials, dielectrics and plasmas , engineering profession , fields, waves and electromagnetics , general topics for engineers , geoscience , nuclear engineering , photonics and electrooptics , power, energy and industry applications , robotics and control systems , signal processing and analysis , transportation

This article introduces the Energy-Efficient Theory of Inventive Problem Solving (EETRIZ) approach, designed to reduce electrical energy waste resulting from human mistakes in IoT-enabled environments. EETRIZ utilizes a novel integration of transfer learning and an activity-dependent environmental management algorithm to adjust settings dynamically according to real-time occupancy and activity data, hence improving energy efficiency. This system efficiently utilizes the advantages of the artificial intelligence-based adaptive gradient algorithm (AdaGrad) and root mean squared propagation (RMSProp) optimization methods to improve prediction accuracy through enhanced weight determination. EETRIZ is developed in the C programming language and is underpinned by comprehensive platforms and libraries, such as MPLAB, Nuvoton 8051 Series microcontroller unit (MCU) programming, GNU’s Not Unix multi-precision library (GMPLibrary)-GMP-5.1.1, and Miracle Library. Thorough hardware testing verifies that EETRIZ surpasses current solutions in energy efficiency, cost-effectiveness, accuracy, and user-friendliness. The system’s capacity to simultaneously control numerous IoT devices enhances its utility in various environments, including residences, workplaces, and educational facilities, providing a scalable solution to mitigate excessive energy consumption resulting from human mistakes.

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