Implementation and Evaluation of Four Interoperable Open Standards for the Internet of Things
Bibliographic record
Abstract
Recently, researchers are focusing on a new use of the Internet called the Internet of Things (IoT), in which enabled electronic devices can be remotely accessed over the Internet. As the realization of IoT concept is still in its early stages, manufacturers of Internet-connected devices and IoT web service providers are defining their proprietary protocols based on their targeted applications. Consequently, IoT becomes heterogeneous in terms of hardware capabilities and communication protocols. Addressing these heterogeneities by following open standards is a necessary step to communicate with various IoT devices. In this research, we assess the feasibility of applying existing open standards on resource-constrained IoT devices. The standard protocols developed in this research are OGC PUCK over Bluetooth, TinySOS, SOS over CoAP, and OGC SensorThings API. We believe that by hosting open standard protocols on IoT devices, not only do the devices become self-describable, self-contained, and interoperable, but innovative applications can also be easily developed with standardized interfaces. In addition, we use memory consumption, request message size, response message size, and response latency to benchmark the efficiency of the implemented protocols. In all, this research presents and evaluates standard-based solutions to better understand the feasibility of applying existing standards to the IoT vision.
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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 | 0.031 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".