Energy-Efficient Wireless Networking for IoT: Optimizing Sustainability and Resource Use

Abstract

This work introduces a full simulation-based approach for an integrated framework that improves the sustainability of IoT (Internet of Things) wireless networks by integrating low-power communication protocols (LoRaWAN, Zigbee, and BLE), energy harvesting (solar and kinetic), edge computing, and optimization techniques. We study energy efficiency through different frameworks. It was shown that both Zigbee and BLE performed very well, with their average energy consumptions being 1.7938J and 7.1752J and 100% node survival for 1000 rounds. Energy harvesting could give 20 J (solar) and 15 J (kinetic) cumulatively with an unfavorable depletion time (200 h – 203 h) for sustained supply. Edge computing reduced energy consumption from 10,000 J to 1,025 J by processing 90% of the data locally, resulting in an 89.75% reduction in energy usage while significantly improving computational efficiency. Dynamic allocation of resources converges to 100J in 4 iterations with uniform consumption of 1W per node. The combined scheme produced a total savings of 69.76% (10000J to 3024J) supported by 40% savings from edge computing, 30% from energy harvesting, 20% from low-power schemes, and 10% from optimization techniques. T-tests were performed, indicating strong significance (t (99) = 87.12, p < 0.001) in savings and data-to-energy correlation, which were correlated highly (r = 0.99), which are reasonable. Scalability was demonstrated, and limitations of static approaches are observed, which restricts the interpretation. Our work provides a baseline solution for sustainable IoT systems. While the low-power protocols offer 20 percent savings and energy harvesting 30 percent, it can be significantly increased through the judicious combination of all these measures to about 70 percent of overall savings of energy consumption, with 10 percent provided by intelligent control logic to meet the objectives of green networking.