SEAMLESS DATA EXCHANGE: OPTIMIZING EDGE-TO-CLOUD INTERACTIONS IN IOT

Abstract

The rapid proliferation of Internet of Things (IoT) devices has led to an unprecedented increase in data generation, necessitating efficient mechanisms for storage, retrieval, and dissemination across edge and cloud environments. Traditional cloud-centric approaches often suffer from latency, bandwidth constraints, and privacy concerns, making them less effective for real-time IoT applications. This study introduces a robust framework for seamless data exchange and optimized search mechanisms between edge and cloud infrastructures. The proposed system leverages edge computing for localized processing, reducing response times, while cloud-driven resources ensure scalability, persistence, and advanced analytics. Furthermore, a hybrid search mechanism is designed to enhance query efficiency, enabling secure and context-aware retrieval of IoT data. By integrating adaptive caching, lightweight encryption, and distributed indexing, the framework achieves significant improvements in latency reduction, energy efficiency, and data confidentiality. Experimental evaluations demonstrate that the approach not only accelerates data retrieval and sharing but also strengthens resilience against network fluctuations and malicious access. This work highlights the potential of edge–cloud synergy to revolutionize IoT ecosystems by enabling reliable, secure, and scalable data exchange.