Performance Evaluation of IoT Protocols for Environment Monitoring
Bibliographic record
Abstract
For the last few decades, environmental pollution has created adverse effects on humans and the ecosystem. The pollutant of natural origin or man-made may cause diseases, allergies, and widespread damages to humans, animals, and food crops. The environmental issues could be generated by pollution of all kinds, i.e. air pollution, water pollution, and climate changes. For example, the wildfires incidents in Canada have a massive influence on air pollution since the caused devastation has increased significantly over the past years. An environmental surveillance and monitoring system can be an effective tool to minimize the concern. However, developing a system for continuous interaction is a challenge due to the lack of communication coverage in far and isolated areas as well as power constraints. In this work we undertake a performance evaluation of an environment monitoring system applying the use of protocols and systems like Internet of Things (IoT), Message Queuing Telemetry Transport (MQTT), and Constrained Application Protocol (CoAP). This has the potential of being the leading technology since it makes machine-to-machine communication possible with minimum requirements. The proposed prototype allows the fixed ground node located on a remote site to communicate with a moving node like a drone. The transmitted data packets were analyzed based on overheads, latency. The Packet Delivery Rate reaches 90% for MQTT even with a 600-meter distance between the two nodes. Bandwidth usage of CoAP is around 85 bits/s with 5000 data packets transmission. The designed system aimed to demonstrate the merits of the selected IoT protocols.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".