Jul 2026· Proceedings of the Institution of mechanical engineers. Part D, journal of automobile engineering· 0 citations· 94 references
TL;DR
A detailed survey on the latest trends in AI based renewable energy integration, smart grid, EV, battery management systems, and real-time transportation data analytics is presented and important concerns regarding the edge system’s security, scale-ability, connectivity, privacy, and the direction of future research on enabling smart transportation were discussed.
Abstract
The adoption rate of electric mobility, renewable energy systems, and smart transportation infrastructures has exacerbated the demand for real-time, high-performance and energy-efficient systems. While high latency, low bandwidth, and low responsiveness are commonly encountered drawbacks in existing cloud-based energy optimization techniques used in these mobility-driven systems. The deployment of Edge AI and IoT technologies will pave the path to efficient, low-latency and real-time distributed renewable energy optimization within the smart sustainable transportation system. This paper presents a detailed survey on the latest trends in AI based renewable energy integration, smart grid, EV, battery management systems, and real-time transportation data analytics. The application of deep learning, reinforcement learning, federated learning, and predictive analytics toward improved renewable energy generation forecasting, smart charging, load balancing, and Vehicle-to-Grid (V2G) co-ordination is also elaborated. Edge computing platforms and IoT devices can support a high-performance real-time monitoring, predictive maintenance and a self-sufficient energy distribution network for smart transportation. The experimental validation on contemporary research works reveal an average 70% reduction in transmission latency, more than 50% increase in the renewable energy consumption, approximately 30% increase in battery lifetime, and nearly 80% reduction in charging infrastructure offline duration for edge systems with AI assistance. Important concerns regarding the edge system’s security, scale-ability, connectivity, privacy, and the direction of future research on enabling smart transportation were also discussed.
The proposed intelligent energy harvesting framework provides an efficient and sustainable power solution for next-generation Internet of Things (IoT) devices by integrating multi-source ambient energy harvesting, Maximum Power Point Tracking (MPPT), hybrid energy storage, and machine learning-based energy management. The framework effectively harvests energy from solar, thermal, radio frequency (RF), vibration, and wind sources while optimizing power utilization through adaptive energy prediction and intelligent task scheduling. Experimental evaluation demonstrates that the proposed system achieves higher energy utilization, lower power consumption, improved communication reliability, and extended operational lifetime compared with conventional battery-powered IoT systems. Furthermore, the integration of cloud and edge computing enables real-time monitoring, predictive analytics, and scalable deployment across diverse IoT applications. Overall, the proposed framework offers a reliable, cost-effective, and environmentally sustainable solution for smart cities, healthcare, industrial automation, environmental monitoring, and precision agriculture, while providing a strong foundation for future research on AI-driven energy optimization and next-generation wireless-enabled self-powered IoT networks.
B. Vaishnavi, K. Siddhartha, B. Ramprasad· International Journal of Cre...· 0 citations
A Smart EV Charging Infrastructure that integrates Internet of Things (IoT)-enabled sensing, cloud-based monitoring, renewable energy sources, battery energy storage, and artificial intelligence (AI) based energy management to provide an efficient, reliable, and sustainable charging solution is proposed.
Bachali Poojitha, Budige Anush, P. K. Reddy· International Journal of Sci...· 0 citations
A Hybrid Digital Twin–IoT Framework that integrates real-time IoT sensing, cloud-edge computing, machine learning, and Digital Twin simulation for intelligent energy optimization for scalable, sustainable, and energy-efficient smart building management with improved reliability and decision-making is proposed.
Mahabala H. N.· International Journal of Mod...· 0 citations
In recent years, the concepts of sustainability and green computing have gained significant attention, particularly in the context of smart cities and their various transportation applications. The primary goal is to shift transportation from fuel-based systems to electric alternatives, reducing overall CO
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emissions. Motivated by this objective, this paper proposes a Green, Sustainable, and Energy-Efficient System for Transportation Applications in IoT Edge Cloud Networks. The focus is on designing an IoT edge cloud infrastructure to support sustainable and green transportation within smart cities. The system addresses various transportation-related tasks, including energy consumption monitoring, traffic and object detection, and optimal route planning, all while leveraging green edge cloud networks. To optimize performance, we propose a workload partitioning method based on a min-cut scheme that categorizes tasks into IoT-local, edge, and cloud-based workloads. This partitioning aims to reduce computational energy consumption and lower CO
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emissions, fostering a more eco-friendly environment. Additionally, we introduce the Energy-Efficient Application Partitioning and Task Scheduling (EAPTS) scheme, which efficiently divides and schedules tasks across different nodes. To validate the system, we implemented testbeds based on Oslo’s public transport scenario, used training data from the given dataset, and developed a simulator for a green, sustainable transport environment. Simulation results demonstrate that the proposed system effectively reduces CO
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emissions, energy consumption, and execution time for all operational tasks.
P. Khuwuthyakorn, A. Lakhan, Arnab Majumdar et al.· Scientific Reports· 0 citations
An AI-driven framework integrates hybrid energy harvesting mechanisms with Deep Reinforcement Learning (DRL) to optimize energy efficiency in IoT systems and achieves up to 300% improvement in network lifetime under low-energy harvesting conditions.
Elkhatim Abuelysar Elmobarak Mohammed Ali· Islamic University Journal o...· 0 citations
Environmental monitoring facilitates solutions to major worldwide challenges, including air pollution, climate change, and water resource degradation. Yet, conventional cloud-based IoT systems are unable to provide real-time solutions because of issues like latency, increased energy consumption, and limited scalability. This paper aims to present a positive environmental impact of edge computing for real-time environmental monitoring and provide a sustainable, energy-efficient, low-latency environmental monitoring solution. The Edge Computing Real-Time Environmental Monitoring (ECRM) framework of the paper achieves local data processing and decision-making through the integration of edge intelligence, collective, and low-power machine learning models at edge gateways. The framework achieves system responsiveness and low energy consumption through the integration of energy-aware task scheduling and adaptive data transmission strategies. Processed data for air quality (AQI), CO₂, humidity, and temperature levels substantially reduce the framework's reliance on cloud computing. The framework provides a 39% reduction in energy consumption and a 40% reduction in latency in comparison to established cloud system models. The reductions improve real-time system responsiveness and reduce network traffic. The research demonstrates that combining edge architecture and green computing is a potential solution for sustainable environmental monitoring. The proposed systems align with the goals of computing sustainability and future smart city solutions.
Dawakit Lepcha, Kanchan Thakur· 2026 4th International Confe...· 0 citations
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