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@article{168552,
        author = {T V Manohar and Dr. T  Dharini},
        title = {Energy Efficiency in IoT: Challenges, Techniques, and Future Directions},
        journal = {International Journal of Innovative Research in Technology},
        year = {2024},
        volume = {11},
        number = {5},
        pages = {1136-1142},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=168552},
        abstract = {The increasing deployment of Internet of Things (IoT) devices has introduced significant challenges regarding energy consumption and management, given the constrained resources of IoT sensors and actuators. Energy efficiency has become a critical concern, particularly as IoT systems scale in various domains, such as smart cities, healthcare, and agriculture. This paper provides a comprehensive review of the current challenges associated with energy efficiency in IoT devices, focusing on both hardware and software-based solutions. Several authors have explored low-power hardware design (Gupta et al., 2021; Kumar and Singh, 2019) and the development of energy-efficient communication protocols (Sharma et al., 2020; Brown et al., 2018). Techniques such as duty cycling (Wang et al., 2020), energy harvesting (Xia and Zhang, 2019), and data aggregation (Lee et al., 2021) have also been shown to reduce energy consumption in sensor networks. Additionally, the role of edge computing in minimizing data transmission energy (Chen et al., 2021; Zhang and Liu, 2020) and the potential of machine learning algorithms for adaptive energy optimization (Patel and Das, 2020; Li et al., 2021) are discussed. Through case studies and simulations, this paper illustrates how these techniques contribute to longer operational lifetimes in IoT applications. The future directions section explores emerging trends such as AI- driven optimization (Verma et al., 2021), the integration of renewable energy sources (Smith and Johnson, 2020), and cross-layer design approaches (Ahmed et al., 2019) for holistic energy management. The paper concludes by identifying research gaps and recommending pathways for future work on energy-efficient IoT systems.},
        keywords = {Energy efficiency, Internet of Things (IoT), low-power hardware, machine learning optimization.},
        month = {October},
        }