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| Auteurs principaux: | , , , , , , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2026
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| Sujets: | |
| Accès en ligne: | https://arxiv.org/abs/2602.17619 |
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| _version_ | 1866910027246206976 |
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| author | Sundaram, Jothi Prasanna Shanmuga Gabidolla, Magzhan Fujarte, Luis Duong, Shawn Guo, Jianlin Koike-Akino, Toshiaki Pu Wang Parsons, Kieran Orlik, Philip V. Sumi, Takenori Nagai, Yukimasa Carreira-Perpinan, Miguel A. Cerpa, Alberto E. |
| author_facet | Sundaram, Jothi Prasanna Shanmuga Gabidolla, Magzhan Fujarte, Luis Duong, Shawn Guo, Jianlin Koike-Akino, Toshiaki Pu Wang Parsons, Kieran Orlik, Philip V. Sumi, Takenori Nagai, Yukimasa Carreira-Perpinan, Miguel A. Cerpa, Alberto E. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_17619 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | EDRP: Enhanced Dynamic Relay Point Protocol for Data Dissemination in Multi-hop Wireless IoT Networks Sundaram, Jothi Prasanna Shanmuga Gabidolla, Magzhan Fujarte, Luis Duong, Shawn Guo, Jianlin Koike-Akino, Toshiaki Pu Wang Parsons, Kieran Orlik, Philip V. Sumi, Takenori Nagai, Yukimasa Carreira-Perpinan, Miguel A. Cerpa, Alberto E. Networking and Internet Architecture 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. |
| title | EDRP: Enhanced Dynamic Relay Point Protocol for Data Dissemination in Multi-hop Wireless IoT Networks |
| topic | Networking and Internet Architecture |
| url | https://arxiv.org/abs/2602.17619 |