A Novel Collaborative Edge-Computing Framework for Intelligent Resource Management in Iot-Enabled Smart Cities

Resumen

The rapid proliferation of Internet of Things (IoT) devices in smart cities has generated unprecedented volumes of heterogeneous data requiring real-time processing, low latency, and efficient resource utilization. Traditional cloud-centric architectures often suffer from bandwidth limitations, communication delays, and scalability challenges when handling dynamic urban workloads. Edge computing has emerged as a promising paradigm by bringing computational resources closer to end devices; however, isolated edge nodes frequently experience resource imbalance, underutilization, and service degradation under varying workloads. This study proposes a novel collaborative edge-computing framework for intelligent resource management in IoT-enabled smart cities. The framework enables cooperative resource sharing among distributed edge nodes and incorporates intelligent workload-aware decision-making for task allocation and processing. By dynamically coordinating computational, storage, and network resources across multiple edge layers, the proposed model aims to reduce latency, improve resource utilization, enhance scalability, and ensure Quality of Service (QoS). The framework is designed to support diverse smart city applications, including intelligent transportation, environmental monitoring, healthcare, and smart energy management.