EDRP: Enhanced Dynamic Relay Point Protocol for Data Dissemination in Multi-hop Wireless IoT Networks

2026-02-19

AI Summary

This work addresses the challenge of throughput degradation in multi-hop wireless IoT networks caused by transmission collisions under fluctuating link quality, a common issue in conventional contention-based data dissemination protocols. To overcome this limitation, the authors propose the EDRP protocol, which integrates real-time link quality estimation into the CSMA backoff mechanism (LQ-CSMA) and couples it with a machine learning–driven fountain code block size selection algorithm (ML-BSS). This joint approach dynamically optimizes both transmission scheduling and coding efficiency. By coordinating node behavior through distributed delay timers, EDRP achieves a 39.43% average improvement in effective throughput over existing protocols in real-world experimental environments, demonstrating significant performance gains while maintaining practical deployability.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Machine Learning: Learning on the Edge & Model Compression
• Search and Optimization: Distributed Search

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Data management and stream processing for Web, mobile and wireless applications
• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work
• Web Mining and Content Analysis: Content-based information diffusion

Abstract

Emerging IoT applications are transitioning from battery-powered to grid-powered nodes. DRP, a contention-based data dissemination protocol, was developed for these applications. Traditional contention-based protocols resolve collisions through control packet exchanges, significantly reducing goodput. DRP mitigates this issue by employing a distributed delay timer mechanism that assigns transmission-start delays based on the average link quality between a sender and its children, prioritizing highly connected nodes for early transmission. However, our in-field experiments reveal that DRP is unable to accommodate real-world link quality fluctuations, leading to overlapping transmissions from multiple senders. This overlap triggers CSMA's random back-off delays, ultimately degrading the goodput performance. To address these shortcomings, we first conduct a theoretical analysis that characterizes the design requirements induced by real-world link quality fluctuations and DRP's passive acknowledgments. Guided by this analysis, we design EDRP, which integrates two novel components: (i) Link-Quality Aware CSMA (LQ-CSMA) and (ii) a Machine Learning-based Block Size Selection (ML-BSS) algorithm for rateless codes. LQ-CSMA dynamically restricts the back-off delay range based on real-time link quality estimates, ensuring that nodes with stronger connectivity experience shorter delays. ML-BSS algorithm predicts future link quality conditions and optimally adjusts the block size for rateless coding, reducing overhead and enhancing goodput. In-field evaluations of EDRP demonstrate an average goodput improvement of 39.43\% than the competing protocols.

Problem

• data dissemination
• link quality fluctuation
• multi-hop wireless IoT networks
• contention-based protocol
• goodput degradation

Innovation

• LQ-CSMA
• ML-BSS
• rateless coding
• link quality awareness
• dynamic back-off