Pruned Adaptive Routing in the heterogeneous Internet of Things
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Recent research endeavours are capitalizing on state of the art technologies to build a scalable Internet of Things (IoT). Envisioned as a technology to integrate the best of Wireless Sensor Networks and RFID systems, there is much promise for a global network of objects that are identifiable, track-able, and harmoniously informing. However, the realization of an IoT framework is hindered by many factors, the most pressing of which is attributed to the integration of these heterogeneous nodes and devices. A considerable subset of these nodes undergoes movement and dynamically enters and leaves the network backbone/topology. Routing packets and inter-nodal communication has received little attention; mainly due to the sheer reliance on the Internet as a backbone. However, spatially correlated entities in the IoT, and those which most often interact, would pose a significant overhead of communication if all intermediate packets need to be routed over distant backhauls. In remedy, we present a Pruned Adaptive IoT Routing (PAIR) protocol that selectively establishes routes of communication between IoT nodes. Since nodes in the IoT belong to different owners, we also introduce a pricing model to cater for the exchange of monetary costs by intermediate nodes to utilize their relaying resources. We also establish a cap on inter-nodal routing to dynamically utilize the Internet backbone if the source to destination distance surpasses a preset (case optimized) threshold. The PAIR routing protocol is elaborated upon, building upon the detailed system model presented in this paper. We finally present a use case to demonstrate the utility and practicality of PAIR in the heterogeneous IoT as it scales.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it