Energy-Aware Federated Edge Intelligence for 6G-Enabled Smart Urban Networks

Communication-Aware Federated Learning for Energy Management in Edge-Cloud Autonomous Systems

ESP32-Based Edge Mesh: A Resilient Framework for Smart-City IoT

As urban populations grow, smart cities increasingly depend on real-time environmental monitoring to enable sustainable development and efficient urban management. Conventional IoT systems often suffer from limited communication range, high power consumption, and unreliable data transmission. This paper presents a novel, scalable IoT architecture for smartcity monitoring that fully leverages ESP32 microcontrollers with integrated edge computing. The proposed design combines ESP-NOW, Wi-Fi, and MQTT protocols within a mesh-enabled framework, reducing latency and energy usage while enhancing resilience and coverage. Data is preprocessed at the edge before centralized aggregation on a Raspberry Pi backend, minimizing network overhead. Experimental results demonstrate that the architecture reliably communicates data, maintains precision, and exhibits strong resilience against node failures, including automatic gateway node replacement when disruptions occur. The design ensures robust fault tolerance and efficient operation, making it a practical and cost-effective solution for nextgeneration urban monitoring infrastructures.

An Iot-Integrated Federated Edge Computing Paradigm for Privacy-Preserving Smart Campus Infrastructure

The rise in the use of the Internet of Things is resulting in extensive and varied data being generated on university campuses, especially in the domain of energy consumption and management in buildings. Conventional learning algorithms will fail in this scenario because they are restricted by privacy boundaries and communication cost considerations on edge devices. Against this background, we introduce a federated learning framework on campus. In which learning will occur at the edge nodes collaboratively. This scheme provides a resource-conscious multi-layer federating strategy that controls the number of devices taking part in the process as well as the frequency at which aggregation is done based on each device’s capability. Contrary to existing federating designs that assume each edge node is similar in ability, this design considers the differences in each campus environment. The experiments conducted using the realistic smart campus test bed reveal that our approach ensures stronger convergence, reduced communication overhead, as well as more accurate predictions of energy consumption, when compared to traditional Federated Averaging as well as the fixed aggregation strategy. Moreover, the approach allows for simplicity in the preservation of privacy, without losing scalability, especially for the smart campus setting that has been projected to be rather large.

Multi-mode energy harvesting–enabled edge computing architecture for industrial IoT environments

This work presents a multi-mode energy harvesting-assisted edge computing architecture, integrated with a joint optimization of energy consumption and communication behaviour, aimed at enhancing the sustainability, reliability and autonomy of operation in an industrial IoT context.

Green Federated Learning with Adaptive Model Pruning for Sustainable Edge Intelligence in Smart Industries

Due to the rapid proliferation of Industrial Internet of Things (IIoT) systems within smart industries, scalable, energy-efficient, and sustainable edge intelligence solutions are in demand. Despite the fact that Federated Learning (FL) allows collaborative training without raw data sharing, traditional FL methods have high communication overhead, huge model sizes, and require a lot of energy, which restricts their implementation in resource-constrained industrial settings. To address these issues, the present paper introduces a Green Federated Learning framework, GFed-AMP, a sustainable IIoT edge intelligent framework that includes Adaptive Model Pruning.The suggested framework incorporates the dynamic magnitude-based pruning of local updates to eliminate redundant parameters and lessen the computational complexity. A strategy of energy-conscious client selection assigns priority to the devices with larger residual energy and with lower carbon intensity, to make sure that participation is environmentally conscious. It has real-time energy and carbon monitoring modules that can be used to gauge power consumption and environmental effects during federated training. Moreover, a sparse-awares aggregation mechanism is an effective method which optimally manages pruned model updates and ensures stable convergence. Experimental assessment of industrial anomaly detection data sets shows that GFed-AMP can achieve up to 40% model reduction, 35% communication overhead reduction, and 30% overall energy reduction in comparison with traditional FedAvg. Notably, the decline in the accuracy of predictions is less than 1.5%, which proves robustness and stability of learning. Better scalability, bandwidth, and lower carbon emission are proven by statistical comparisons. All in all, the overall experience with GFed-AMP’s trade-off between accuracy, communication efficiency, and environmental sustainability is a viable green AI solution to Industry 4.0 deployments.

Event-Triggered Decentralized Intelligence with Energy-Aware Federated Learning for Real-World Systems

The rapid growth of large-scale interconnected systems, such as smart cities, industrial automation, and environmental monitoring, demands intelligent decision-making frameworks that are resilient, scalable, and resource-efficient. Traditional centralized intelligence approaches suffer from communication bottlenecks, high energy demands, and vulnerability to single points of failure, making them unsuitable for realworld deployment. This work introduces an event-triggered decentralized intelligence framework with energy-aware federated learning designed to address these challenges. In the proposed system, distributed nodes collaborate by exchanging model updates only when significant events or anomalies occur, rather than relying on continuous communication. This event-driven strategy substantially reduces bandwidth consumption while enabling timely adaptation to dynamic environments. To further enhance sustainability, the framework integrates energy-aware scheduling, allowing devices with limited power resources to contribute adaptively based on their energy profiles. A multilayer coordination mechanism ensures local autonomy and global consensus without centralized control. Experimental evaluations on representative real-world datasets demonstrate that the proposed method achieves competitive accuracy compared to conventional federated learning while reducing communication overhead by more than 40% and extending device lifetime in energy-constrained settings. Additionally, the framework incorporates Byzantine-resilient aggregation and is analyzed under communication latency and varying network topology conditions.