DRACO: Data Replication and Collection Framework for Enhanced Data Availability and Robustness in IoT Networks

2025-10-08

AI Summary

To address data loss caused by resource constraints and node unreliability in IoT networks, this paper proposes a distributed data replication and mobile sink collaboration framework. The framework innovatively integrates hop-by-hop distributed replica generation, zero-overhead mobile sink collection, and an adaptive replica management algorithm, validated on the ns-3 simulation platform. It dynamically optimizes replica placement and dissemination to enhance data availability and system robustness under node failures and varying network densities. Experimental results demonstrate that, compared to greedy and random replication strategies, the framework improves data availability by 15% and 34%, respectively, and increases replica creation efficiency by 18% and 40%. Moreover, it consistently outperforms conventional mobile sink-based data collection methods across diverse network conditions.

Technology Category

• Planning, Routing, and Scheduling: Replanning and Plan Repair
• Search and Optimization: Distributed Search
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Data management and stream processing for Web, mobile and wireless applications
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Security and Privacy: Data transparency and provenance

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 paper presents DRACO (Data Replication and Collection), a framework that integrates a distributed hop-by-hop data replication approach with an overhead-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 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 and self-avoiding random walk. By addressing key IoT data management challenges, DRACO offers a scalable and resilient solution well-suited for emerging use cases.

Problem

• Enhancing data availability in resource-constrained IoT networks
• Optimizing replica placement to prevent data loss from failures
• Improving data collection efficiency with mobile sink strategies

Innovation

• Distributed hop-by-hop data replication approach
• Overhead-free mobile sink-based data collection
• Optimized replica placement for failure resilience