Energy–Efficient IoT: Optimizing Consumption for a Sustainable Future
Dharmaiah Devarapalli1, Sri Datta Shanmukh Sai Yeddu2, Phani Harshitha Tupaakula3, Shaik Rehan Hamid4, Santhosh Jasti5
1Dr. Dharmaiah Devarapalli, Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, (A.P.), India.
2Sri Datta Shanmukh Sai Yeddu, Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, (A.P.), India.
3Phani Harshitha Tupaakula, Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, (A.P.), India.
4Shaik Rehan Hamid, Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, (A.P.), India.
5Santhosh Jasti, Student, Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, (A.P.), India.
Manuscript received on 08 December 2025 | First Revised Manuscript received on 31 December 2025 | Second Revised Manuscript received on 05 January 2026 | Manuscript Accepted on 15 January 2026 | Manuscript published on 30 January 2026 | PP: 12-17 | Volume-15 Issue-2, January 2026 | Retrieval Number: 100.1/ijitee.B121015020126 | DOI: 10.35940/ijitee.B1210.15020126
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Aiming at energy IoT applications for demand-side automation of electricity usage in residential and commercial buildings, this paper presents systems and methodologies that advance the research objectives. We developed and implemented an intelligent switch system that provides real-time energy feedback, automatic control, and optimisation to monitor the system’s energy performance metrics. Based on 58 households and a six-month field study, the system achieved an average saving of 24.7%, with a maximum saving of 37.2%. We consider the challenges of ubiquitous deployment, interoperability, security, and system cost. Further optimisations can be made toward energy efficiency, such as dynamic load balancing, machine-learning-based predictive models for SLA requirements , and adaptive scheduling algorithms. This paper demonstrates the feasibility of IoT for regulating household energy use through analyses of a prototype and a dataset. The prototype enables households to achieve approximately 412 watt-hours of annual energy savings, thereby illustrating the potential of energy management and the feasibility of the proposed system.
Keywords: Internet of Things, Energy Efficiency, Smart Buildings, Machine Learning, Power Consumption, Sustainable Energy, Smart Grid
Scope of the Article: Computer Engineering