A Comparison of IoT Communication Libraries: APIs and Performances
Bibliographic record
Abstract
The Internet of Things (IoT) and the number of deployed IoT devices are growing exponentially nowadays. These devices play pivotal roles in diverse domains, e.g., smart homes. Given their constrained processing and memory capacities, IoT devices communicate with one another through specialized protocols. \nThe two main IoT communication protocols are the Constrained Application Protocol (CoAP) and Message Queuing Telemetry Transport (MQTT). By March 1, 2023, there were 35 public libraries of CoAP and 40 of MQTT. These libraries have different characteristics, including levels of completeness and runtime performances. \nBecause of diverse requirements in different domains, the same protocol/library does not apply to any applications. Consequently, developers must select a library (e.g., Californium, java-coap, Paho MQTT, or HiveMQ MQTT Client) but they do not have access to comprehensive and clear comparisons of the API and performance of these protocols and their implementations, impeding their ability to make informed choices. \nIn this thesis, we implement multiple IoT scenarios using the CoAP and MQTT protocols and several of their implementation libraries. We conduct a comprehensive comparative analysis based on API and performance metrics, including static metrics, packet sizes, and runtime performance. \nWe thus provide developers with evidence to choose between CoAP and MQTT protocols and their libraries. In future work, we will expand this work to include other IoT protocols and libraries, more scenarios and metrics.
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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 | 0.007 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".