Aether-Edge
Decentralized edge-native building management system that eliminates Age of Information latency from 19.4 seconds to near-zero.
Overview
Aether-Edge is a decentralized, predictive building management system designed to address a fundamental flaw in traditional centralized HVAC control: data staleness. By moving computation to the edge — directly onto sensor nodes — Aether-Edge eliminates the round-trip latency that causes comfort violations and energy waste in conventional architectures.
Problem Statement
Centralized Building Management Systems (BMS) operate on a poll-compute-push cycle: sensors send readings to a central controller, which computes control decisions and pushes commands back. Under network load or sensor failure, this cycle introduces significant Age of Information (AoI) — the staleness of the data the controller is acting on. In a simulated 5-zone university building floor, the centralized baseline produced an average AoI of 19.4 seconds, resulting in measurable comfort violations (temperature overshoot) and unnecessary cooling plant usage.
Approach
Edge-Native Architecture
Rather than polling a central server, each zone's sensor node runs a local prediction model. The node continuously simulates its own thermal state using a Finite-Time Centered-Space (FTCS) heat diffusion solver, updating its internal model at each time step. Control decisions (e.g., whether to activate cooling) are made locally based on the predicted future temperature, not a stale past reading.
Physics-Based Prediction
The FTCS solver discretizes the heat diffusion PDE across the zone's spatial grid. At each timestep, each node computes the expected temperature distribution in its zone for the next k steps, using current readings as initial conditions and known thermal parameters (wall conductivity, occupant heat load, HVAC capacity) from the knowledge base.
Network Topology Simulation
NetworkX was used to model the sensor network topology — nodes represent sensor/actuator pairs per zone, and edges represent inter-zone thermal coupling and communication links. This allowed the simulation to model scenarios where nodes share state with neighbors to improve boundary-zone prediction accuracy.
3D Visualization
PyVista rendered the building floor as a 3D volume, with temperature distributions color-mapped across the spatial grid. This made it straightforward to visually verify that the FTCS solver was producing physically plausible heat diffusion patterns.
Testing
The project includes 190+ unit and integration tests covering the FTCS solver correctness, network topology construction, edge prediction accuracy, and end-to-end comfort metric computation. All tests are JSON-driven and configurable via a central config file.
Results
Age of Information reduced from 19.4 seconds (centralized) to ~0 seconds (edge) — control decisions are now made on real-time local state, not stale polled readings.
- Comfort violations (time-integrated temperature overshoot) reduced significantly across all 5 simulated zones.
- Cooling plant energy consumption decreased as the controller no longer over-compensates for stale data.
- 190+ tests pass on a JSON-driven configuration, making it straightforward to benchmark different building topologies.
Reflection
This project reinforced how critical the Age of Information metric is in real-time control systems — it's not just about bandwidth, but about whether your controller is making decisions on current reality. The FTCS solver was the most challenging component to validate; getting the boundary conditions right at zone interfaces required careful numerical analysis. Future work could integrate a learned thermal model (e.g., a small neural network) to replace the physics-based FTCS solver for zones where the thermal parameters are uncertain.
Assets
