EDRP: Enhanced Dynamic Relay Point Protocol for Data Dissemination in Multi-hop Wireless IoT Networks
2026-02-19 • Networking and Internet Architecture
Networking and Internet Architecture
AI summaryⓘ
The authors study data sharing methods for Internet of Things (IoT) devices that are powered by the grid rather than batteries. They found that an existing protocol called DRP struggles with real-world changes in wireless link quality, causing data collisions and slower communication. To fix this, they created EDRP, which adjusts how devices wait to send data based on current link quality and uses machine learning to better manage data packet sizes. Their new method improved data delivery speed by about 39% in real-world tests compared to older protocols.
IoTData dissemination protocolCSMA (Carrier Sense Multiple Access)Link qualityBack-off delayRateless codesMachine learningGoodputBlock size selectionWireless communication
Authors
Jothi Prasanna Shanmuga Sundaram, Magzhan Gabidolla, Luis Fujarte, Shawn Duong, Jianlin Guo, Toshiaki Koike-Akino, Pu, Wang, Kieran Parsons, Philip V. Orlik, Takenori Sumi, Yukimasa Nagai, Miguel A. Carreira-Perpinan, Alberto E. Cerpa
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.