Exploring Sustainable Computing: IoT, Big Data, and Energy-Efficient Solutions

As a result of the quick expansion of the Internet of Things (IoT) and Big Data, there are emerging issues related to resource utilization, power management, and system size. As IoT ecosystems continue to grow, questions about sustainability while sustaining performance have emerged as a key topic. This survey paper explores the current literature on IoT, Big Data and green computing systems that have been proposed for use in the future with an emphasis on solutions that can reduce the carbon footprint of these systems whilst maintaining functionality. It introduces IoT and Big Data and their fundamental theories, discusses challenges including energy issues, data issues, real-time issues, and security issues, and introduces sustainable computing approaches including cloud, edge, and fog computing. Further, the paper discusses energy efficiency methods such as machine learning, low power design, energy harvesting, sustainability driven data analysis and predictive maintenance. The survey also points out the outstanding research issues in terms of scalability, energy-performance, and security-sustainability. Lastly, suggestions for further studies involve enhancing interaction between AI systems, creating energy-sensitive AI models, and enhancing the performance of combined structures. Hence, the objective of this paper is to present an understanding into constructing large-scale, energy efficient, and secure IoT systems, which can advance the sustainability of future IoT and Big Data systems.