DRACO: data replication and collection framework for enhanced data availability and robustness in IoT networks

Abstract

The Internet of Things (IoT) bridges the gap between the physical and digital worlds, enabling seamless interaction with real-world objects via the Internet. However, IoT systems face significant challenges in ensuring efficient data generation, collection, and management, particularly due to the resource-constrained and unreliable nature of connected devices, which can lead to data loss. This article presents data replication and collection (DRACO), a framework that integrates a distributed hop-by-hop data replication approach with a routing-free mobile sink-based data collection strategy. DRACO enhances data availability, optimizes replica placement, and ensures efficient data retrieval even under node failures and varying network densities. Extensive network simulator-3 (ns-3) simulations demonstrate that DRACO outperforms state-of-the-art techniques, improving data availability by up to 15% and 34%, and replica creation by up to 18% and 40%, compared to greedy and random replication techniques, respectively. DRACO also ensures efficient data dissemination through optimized replica distribution and achieves superior data collection efficiency under varying node densities and failure scenarios as compared to commonly used uncontrolled sink mobility approaches, namely, random walk (RW) and self-avoiding RW (SA-RW). By addressing key IoT data management challenges, DRACO offers a scalable and resilient solution well-suited for emerging use cases, including industrial IoT device monitoring, smart city environmental sensing, agricultural IoT data collection, and disaster response networks, where maintaining data availability under device failures or intermittent connectivity is critical.