Distributed Edge Intelligence for Energy and Transportation Systems

The rapid digitalization of energy and transportation infrastructures has led to an unprecedented increase in data generation from distributed sensors, smart devices, and cyber physical systems. Traditional cloud centric architectures struggle to meet the stringent requirements of low latency, real-time decision-making, data privacy, and system resilience demanded by modern smart grids and intelligent transportation systems (ITS). Distributed Edge Intelligence (DEI) has emerged as a promising paradigm that integrates edge computing with artificial intelligence to enable localized data processing, autonomous control, and collaborative decision-making across networked edge nodes. This paper presents a comprehensive study on the application of distributed edge intelligence in energy and transportation systems. The proposed framework leverages decentralized learning, edge-level analytics, and cooperative intelligence to enhance system efficiency, reliability, and scalability. A detailed methodology is introduced, followed by an evaluation of performance improvements in terms of latency reduction, operational efficiency, and system robustness. The results demonstrate that distributed edge intelligence significantly outperforms centralized approaches, making it a critical enabler for next-generation smart energy and transportation infrastructures.