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IoT & Smart City

Billions of micro-sensors are wiring the physical world to the digital one. Explore how a soil-moisture reading triggers an irrigation valve, how traffic lights self-coordinate, and how a vibration signature predicts a bearing failure.

7 simulations Sensors · Control PID · Edge AI

Simulations

Open any simulation — runs instantly in your browser

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Popular★★☆ Moderate
Adaptive Traffic Signal Control
Inductive loop sensors feed queue length to Webster's optimal cycle algorithm — green time splits adapt live. Compare fixed-time vs. adaptive plans on the same intersection.
PIDWebsterQueue
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★★☆ Moderate
HVAC PID Control — Thermal Building Model
A lumped-parameter RC thermal network for a room — tune PID gains and watch the thermostat hunt, overshoot or settle. The physics behind every smart-building energy-management system.
PIDThermal RCControl
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★☆☆ Beginner
Smart Irrigation — ET₀ Soil Model
Spatial soil-moisture grid + Penman-Monteith evapotranspiration rate drives zone-by-zone watering decisions. See how IoT sensors cut water use by 40% vs. timer-based systems.
ET₀Soil MoistureSchedule
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★★☆ Moderate
LoRaWAN Sensor Network Coverage
Boid-like spreading model for LoRaWAN radio propagation — place gateways on a city map and watch coverage circles account for spreading factor and link budget. Optimise placement.
LoRaWANLink BudgetCoverage
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★★★ Advanced
Predictive Maintenance — Vibration FFT
Simulate a rotating machine with gradually degrading bearing: watch the FFT spectrum grow a sideband until an anomaly-detection threshold fires. The algorithm behind Industry 4.0 CBM systems.
FFTAnomalyMTBF
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★★☆ Moderate
Smart Building Occupancy Flow
Agent-based occupancy model: pedestrian count sensors trigger HVAC zone activation, lighting groups and access control events — the real-time data loop inside a smart office.
OccupancyAgentsBMS
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★★☆ ModerateNew
Wearable Health Monitor — Alert System
Stochastic heart-rate time series with occasional arrhythmia events. A threshold alert and rolling-average anomaly detector trigger notifications — prototype of a consumer IoT health device.
ECGAlertWearable

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About IoT & Sensor Simulations

Sensor data, MQTT flows, signal noise, and embedded systems modelled

IoT and sensor simulations model the signal-processing and communication challenges that arise in networked embedded systems. Sensor noise simulations add Gaussian and impulse noise to a clean signal and apply moving-average, exponential, and Kalman filter algorithms, showing SNR improvement as a function of window size and noise variance. MQTT publish-subscribe flow visualisers animate message routing between simulated sensors, brokers, and subscriber clients.

Sampling-theorem demonstrations show aliasing artifacts when a signal is sampled below the Nyquist rate and how anti-aliasing filters prevent them. Wireless medium-access simulations model CSMA/CA collision avoidance and exponential back-off in a shared radio channel. These simulations cover the signal-processing and networking concepts central to the IoT stack — from sensor interface to cloud MQTT broker to data dashboard.

Each simulation in this category is built with accuracy and interactivity in mind. The underlying mathematical models are the same ones used in academic research and professional engineering — just made accessible through a web browser. Changing parameters in real time and observing the results is one of the most effective ways to build intuition for complex scientific and engineering concepts.

Key Concepts

Topics and algorithms you'll explore in this category

LoRaWAN CoverageRSSI, path loss, and sensor network reach
Adaptive Traffic SignalsPID and reinforcement learning for green-wave
HVAC PID ControlHeating/cooling regulation with PID feedback loops
Edge vs Cloud LatencyOffloading decisions for real-time sensor data
Smart Grid BalancingLoad prediction and renewable dispatch optimisation
Wireless PropagationFriis equation and urban path-loss models

Frequently Asked Questions

Common questions about this simulation category

What IoT and smart-city topics are covered?
LoRaWAN sensor network coverage, adaptive traffic signal control (PID and RL), HVAC building automation, edge vs cloud latency trade-offs, and smart grid renewable dispatch.
What is the LoRaWAN coverage simulation?
It places gateways on a city map and computes signal strength using a log-distance path-loss model. You can adjust gateway height, frequency, and spreading factor to see how coverage areas change.
How does the adaptive traffic signal work?
A PID controller measures queue length at each approach and adjusts green phase durations to minimise total delay. A reinforcement learning mode trains an agent to outperform the fixed-time baseline.