Introduction and Evaluation of Attachability for Mobile IoT Routing Protocols With Markov Chain Analysis
Bibliographic record
Abstract
Reliability of routing mechanisms in wireless networks is typically measured with Packet Delivery Ratio (PDR). Basically, PDR is reported with an optimistic assumption that the topology is fully constructed, and the nodes have started their packet transmission. This is despite the fact that prior to being able to transmit packets, nodes must first join the network, and then try to keep connected as much as possible. This is a key factor in the overall reliability provided by the routing protocols, especially in mobile IoT applications, where disconnections occur frequently. Nevertheless, there is a lack of appropriate metrics, which could evaluate the routing mechanisms from this perspective. Accordingly, this paper introduces attachability; a new metric for evaluating the capability of routing protocols in assisting the mobile or stationary nodes in joining, and maintaining their connections to the network. Our newly proposed metric is calculated via Markov chain analysis along with the sample frequency-based estimating technique. To evaluate attachability, we have simulated a mobile IoT infrastructure, and conducted a comprehensive set of experiments on different versions of the IPv6 Routing Protocol for Low-power and lossy networks (RPL). Based on our observations, attachability is significantly dependent on the employed metrics and path selection policies in the routing mechanisms. Among the three different versions of RPL, including the original version (ORPL), which is standardized for stationary IoT applications, and two mobility-aware versions, i.e., MARPL, and OMARPL, OMARPL showed up to 42%, and 10% of improvement in terms of attachability against ORPL, and MARPL, respectively.
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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 | 0.006 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".