ItemBook Chapter

Optimizing energy consumption in iot sensors through deep learning-based power management

Title
Optimizing energy consumption in iot sensors through deep learning-based power management
Creator
Azariya, David Samuel; Mohanraj, V.; Sathiyamoorthi, V.; Natarajan, Jayapandian; Manjunath, Chinthakunta
Description
The rapid growth of internet of things (IoT) devices necessitates efficient power management to curb escalating energy consumption. This chapter proposes a novel solution by employing deep learning techniques to optimize energy use in IoT sensors. The authors review existing IoT sensor energy consumption challenges and conventionalpower management limitations. Drawing ondeep learning's successes, they develop an architecture trained on curated sensor data. Practical implications span industries, scalability, and generalizability to diverse IoT setups. Economic insights highlight potential cost savings and benefits. In conclusion, the innovative deep learning-based approach addressesIoT energy challenges, offering a promising solution that optimizes usage and could reshape IoT device efficiency. This work opens avenues for hybrid strategies, merging deep learning with other techniques, further advancing energy efficient IoT systems. 2025 by IGI Global Scientific Publishing. All rights reserved.
Source
Optimization Tools and Techniques for Enhanced Computational Efficiency;pp.151-168
Date
01-01-2025
Publisher
IGI Global
Coverage
Azariya D.S., Sona College of Technology, India; Mohanraj V., Sona College of Technology, India; Sathiyamoorthi V., Government Polytechnic College, India; Natarajan J., Christ University, India; Manjunath C., Christ University, India
Rights
Restricted Access; Hardcopy may be available in the library
Relation
ISBN: 979-836936887-9; 979-836936885-5;
Format
online
Language
English
Type
Book chapter

Citation

Azariya, David Samuel; Mohanraj, V.; Sathiyamoorthi, V.; Natarajan, Jayapandian; Manjunath, Chinthakunta, “Optimizing energy consumption in iot sensors through deep learning-based power management,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 19, 2026, https://archives.christuniversity.in/items/show/24982.

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