Optimizing Energy Efficiency in Edge Computing: A Machine Learning Approach

2025-07-09T06:48:50+00:00

Edge computing has become an essential paradigm for processing data near its source, minimizing latency, and improving real-time decision-making in various applications, including the Internet of Things (IoT), autonomous systems, and smart settings. Nonetheless, the energy requirements of edge computing are daunting considering that edge devices are resource-limited. It discusses the application of machine learning techniques for energy efficiency optimization in edge computing by utilizing intelligent resource management, dynamic task offloading, adaptive power management, and predictive energy modeling. Leading state approaches in energy-aware machine learning, including deep reinforcement learning, regression-based optimization, and federated learning, show great potential to reduce power consumption while ensuring adequate system performance. They have great potential, but extending machine learning models to edge environments brings challenges such as computational overhead, real-time processing constraints, as well as security concerns. Addressing these limitations calls for lightweight, hardware-efficient algorithms, cross-domain collaboration, and integration with emerging technologies like 5G and IoT. This study reviews the recent state-of-the-art developments and the main challenges to facilitate energy efficient edge computing through machine learning. This research helps contribute to sustainable edge infrastructures capable of hosting scalable, elastic, high-performance applications through the synergy of intelligent computing and energy management.

Blockchain-Based Secure Data Sharing Framework for Smart Cities

2025-07-09T06:48:51+00:00

Procurement of blockchain technology by smart cities creates new possibilities to strengthen both security and privacy and transparent data systems in urban areas. The study analyzes blockchain-based secure data-sharing frameworks for smart cities through interviews with experts who belong to urban planning, cybersecurity, IoT engineering and city management fields. The authors performed interviews together with assessing established blockchain projects in smart cities to recognize both implementation obstacles and prospective features of blockchain data security systems. The research demonstrates blockchain technology provides many useful features but multiple obstacles limit its actual deployment. The adoption of blockchain for data sharing faces multiple obstacles which start with expensive startup expenses as well as standardization issues and regulatory obstacles. This study displays the necessity of public-private sector stakeholder coordination as well as the requirement to establish regulatory frameworks and provide urban population education. The integration of blockchain into urban data-sharing systems receives thorough analysis from different smart cities' interviews and case studies that provides this framework for future implementation. The research helps expand existing blockchain knowledge about smart city evolution by delivering essential guidelines to both policymakers and city administrators.

Spectrum of Engineering and Technology Applications Journal (SETAJ)

Optimizing Energy Efficiency in Edge Computing: A Machine Learning Approach

Blockchain-Based Secure Data Sharing Framework for Smart Cities