Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Book Chapter Faculty Publications- Book Chapter Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Azariya, David Samuel; Mohanraj, V.; Sathiyamoorthi, V.; Natarajan, Jayapandian; Manjunath, Chinthakunta Title A name given to the resource Optimizing energy consumption in iot sensors through deep learning-based power management Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived Optimization Tools and Techniques for Enhanced Computational Efficiency;pp.151-168 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.4018/979-8-3693-6885-5.ch006" target="_blank" rel="noreferrer noopener">https://doi.org/10.4018/979-8-3693-6885-5.ch006</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105005043575?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105005043575?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant 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 Description An account of the resource 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. Publisher An entity responsible for making the resource available IGI Global Relation A related resource ISBN: 979-836936887-9; 979-836936885-5; Language A language of the resource English Type The nature or genre of the resource Book chapter Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online