A Low-Cost Low-Power LoRa Mesh Network for Large-Scale Environmental Sensing
Bibliographic record
Abstract
Sustainability and climate monitoring efforts create a need for long-term in-situ sensing of large geographic areas. However, environmental monitoring in remote areas of developing countries remains impeded by a lack of low-cost, scalable Internet of Things (IoT) solutions. Whereas IoT systems for in-situ sensing abound, they mostly are either low-cost or suitable for large areas, but not both. In this article, we present a low-cost low-power network solution for in-situ sensing of areas up to hundreds of square kilometers. Taking advantage of LoRa technology, we develop a self-organizing mesh network that can be scaled to a hundred and more nodes. Scalability is achieved by developing methods that mitigate packet collisions during data collection. We present a protocol, called CottonCandy, with which nodes self-organize in a spanning-tree network topology in a distributed fashion. A power profile on a custom-built circuit board shows that CottonCandy nodes can run thousands of duty cycles on 2 AA batteries, sufficient to operate for years in many applications. Using off-the-shelf components, the cost of a CottonCandy node is less than U.S.$\$ $15. Evaluations by simulation show that CottonCandy networks with 100 nodes achieve a packet delivery ratio (PDR) of >90%. Measurements of an outdoor deployment with 15 nodes corroborate the high PDR in a real-life setting.
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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 | 0.000 | 0.001 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".