★ New · Smart City & Public Spaces engine v4.4.0

Synthetic IoT datasets from physics, not black boxes

Every row comes from a documented model — a 22-stage Smart City pipeline (UTCI, air quality, occupancy), Bergman glucose ODE, Pareto-tailed DDoS flows, RC thermal relaxation. Deterministic, seed-reproducible, CC0 public domain, and citable.

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6 IoT domains· Up to 10,000 rows / dataset· Seed-reproducible· CC0 public domain· KS-validated sidecar
★ New · v4.4.0

Smart City & Public Spaces — flagship reference engine

A normative, specification-driven 22-stage pipeline for urban public spaces, byte-reproducible from a decimal master_seed, emitting a 64-column CSV under a published data-dictionary contract.

  • UTCI thermal stress. Operational polynomial over air/radiant temperature, wind, and vapour pressure.
  • Air quality. Single-box PM2.5 / NO₂ transport with rolling 24-hour exposure.
  • Occupancy. Non-homogeneous Poisson arrivals + dwell-time occupancy reconstruction.
  • Acoustics. Energy-sum LAeq from traffic and pedestrian sources.
  • Compound risk. Thermal / air / noise / crowding → overall action level.
  • Reproducible. SHA-256 named-substream seeding; verified against a golden reference.
Explore Smart City datasets Read the v4.4.0 spec →
🏙️

Smart City & Public Spaces

22-stage urban pipeline (v4.4.0): UTCI thermal comfort, PM2.5/NO₂ air quality, NHPP occupancy, LAeq noise, and a compound public-space risk layer

🏠

Smart Home

RC thermal relaxation (ISO 13790), analytic CO₂ ODE, Magnus–Tetens psychrometric humidity, Markov occupancy, deadband HVAC

⚙️

Predictive Maintenance

Weibull degradation, ISO 10816 vibration zones, RUL-ready bearing temperature and current models

🏥

Medical IoT

Circadian HR/BP/SpO₂, NEWS2 scoring, Bergman Minimal Model (RK4) with Dalla Man (2007) meal absorption

🏭

IIoT Network

Modbus / OPC UA / DNP3 traffic, OT roles (PLC, HMI, SCADA, RTU), MitM / replay / false-data-injection attacks

🚗

Connected Vehicle

Driving state machine, GPS dead reckoning, 5-gear RPM model, event classifier (hard-brake / rapid-accel)

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Choose domain, configure parameters, download CSV.

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How IoTSyn Works

Transparent, reproducible data generation grounded in established mathematical frameworks.

1

Choose Domain & Parameters

Select from 6 IoT domains. Configure physical parameters — climate, equipment type, patient demographics, network topology. Defaults are calibrated from literature.

2

Physics-Based Generation

Data is generated from explicit mathematical models — Fourier decomposition, mass-balance ODEs, Weibull distributions, Markov chains. Every equation is documented.

3

Download & Cite

Download as CSV with metadata header. Each dataset includes its seed for exact reproduction, and auto-generated citations in APA, MLA, Chicago, IEEE, BibTeX, and Harvard.

Sample Mathematical Models (v3.2.0)

Indoor temperature — RC thermal relaxation (ISO 13790)

T(t+Δt) = T_target + (T(t) − T_target)·exp(−Δt/τ) + ε_AR(1)

CO₂ — analytic solution of mass-balance ODE

C(t+Δt) = C_ss + (C(t) − C_ss)·exp(−Δt/τ_vent), C_ss = C_out + n·G/Q

Glucose — Bergman Minimal Model (Bergman et al., 1979)

dG/dt = −(p₁ + X)·G + p₁·G_b + D(t); dX/dt = −p₂·X + p₃·(I − I_b)

DDoS flow duration — Pareto heavy-tail (α=1.2)

P(X > x) = (x_m / x)^α, X = x_m · (1 − U)^(−1/α)

Equipment degradation — Weibull CDF (ISO 10816)

D(t) = 1 − exp(−(t/L)^β)

Validation — Kolmogorov–Smirnov goodness-of-fit

D_n = sup_x |F_n(x) − F(x; θ̂)|; P(√n·D_n > t) = 2·Σ (−1)^(k−1)·exp(−2k²t²)
See complete methodology with all equations →

📄 Technical Report

Full mathematical specification of all 6 generators, with 10 academic references including Box-Muller, Knuth, Marsaglia-Tsang, and ISO 10816.

Read & cite the technical report →

📦 Public Dataset Repository

Generated datasets are periodically published to IoTDataset.com for direct browsing and download.

Visit IoTDataset.com →