Rescue‐Sink: Dynamic sink augmentation for RPL in the Internet of Things
Bibliographic record
Abstract
Abstract The Internet of Things (IoT) is largely built on the interconnection of low‐power networked devices, generally referred to as low‐power and lossy networks (LLNs). The prime routing protocol designed over IPv6, named routing protocol for LLNs (RPL), presents the main effort to standardize an IPv6‐based routing protocol for all LLNs. Routing protocol for LLNs has gained significant prominence in IoT research due to its flexibility in adapting to different topologies and could run in agnostic replicas over the same network to serve different applications. However, as RPL is based on virtualizing a tree topology, many challenges ensue in scaling with network traffic and diverse traffic patterns in the IoT. The current RPL standard focus on operation under a single sink, toward which all traffic flows, and thereby its survivability determines the lifetime of the IoT network. However, it mentions briefly in its RFC6550, the using of multiple roots. However, it does not study when, where, and how deploying multiple roots. In this paper, we propose a dynamic Rescue Sink protocol, which actively monitors the performance of IoT nodes in a given RPL network and introduces a dynamic mechanism for mitigating RPL performance by introducing new sinks when needed. We define a suffering index computed over intervals by RPL nodes in a decentralized approach, which monitors their tendency to yield high traffic load without inducing control overhead. Furthermore, our Rescue Sink protocol is designed in line with the RPL standard, and we elaborate on all the components to integrate with the standard. We present a thorough evaluation of our Sink Rescue protocol, using the Cooja simulator over the Contiki OS, most prevalently used in IoT devices. We demonstrate the performance improvements in terms of energy consumption and data delivery.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".