Multi-Linear LoRa network topology deployment with interference avoidance for white area monitoring
Bibliographic record
Abstract
The emergence of the Internet of Things (IoT) has given a new dimension for monitoring applications due in particular to new communication technologies such as LoRa/LoRaWAN. These innovations in the technology have driven the curiosity to use LoRa-based network in applications such as smart agriculture management and monitoring system, road tracks or railways monitoring, border monitoring, Oil and Gas, or even water pipeline supervision, etc. This kind of network is called linear network topology LoRa imposed by the linearity of monitored infrastructure. Some of the challenges faced in a linear network are mainly: interference management, energy efficiency and network lifetime increase.The main goal of this paper is to propose a Linear LoRa sensor network architecture for the monitoring of so-called white areas located on the southern Senegal in West Africa. The simplified architecture is composed by end device nodes and gateways communicating by LoRa radio links. The choice of such a physical topology is explained by the fairly complex nature of the node deployment environment, which is very rugged. The objective of this study is to find a better deployment of nodes to achieve a better coverage of infrastructure deployed in non-connected far areas.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".