Application-Oriented Traffic Modeling of WiFi-Based Internet of Things Gateways
Bibliographic record
Abstract
Many Internet of Things (IoT) devices generate relatively small-sized data and have limited energy supply. These two factors limit their ability to connect directly to cloud servers through a wireless backbone network without imposing a burden on this network in providing efficient data transfer. In this article, we consider an IoT network architecture where a number of different IoT devices send their data wirelessly to an IoT gateway (or a fog node) via a WiFi network. We focus on characterizing incoming traffic patterns to the gateway for three typical IoT applications with real-time and nonreal-time data transfer requirements, such as video surveillance, smart city, and e-healthcare. Our study is based on generating real IoT traffic traces in a lab environment from various sensors and devices for the aforementioned applications and employing these traces to emulate a network of IoT nodes connected to a gateway via WiFi. In the conducted experiments, different homogenous and nonhomogeneous traffic patterns of the selected applications are examined for synchronized and unsynchronized data sources. Based on our empirical data, the experimental results reveal that the packet interarrival time distribution at the gateway is close to generalized Pareto distribution for homogeneous eHealth and smart city traffic, whereas the Weibull distribution is the nearest to model the empirical packet interarrival time for the rest of the examined traffic patterns. Moreover, we show that employing the experimental findings to analyze the delay performance of connecting the gateway to the cloud, given certain backbone network resources, leads to accurate results.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".