Distributed AI Fire Detection System A Spatial-Temporal Edge Computing Approach

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Distributed AI Fire Detection System A Spatial-Temporal Edge Computing Approach

This paper presents the design, implementation, and mathematical foundations of a distributed artificial intelligence system for real-time fire detection and propagation prediction. The system deploys five Raspberry Pi three B plus nodes as edge computing units, each equipped with a BME six eighty multi-parameter environmental sensor. Sensor data streams are windowed, preprocessed, and fed into a two-stage AI ensemble: a Long Short-Term Memory network for fire phase classification, and a Multi-Layer Perceptron for spatial fire propagation detection. Inference is executed in a synchronized manner: all five nodes are processed at the same temporal index T, enabling spatially aware fire spread detection that would be impossible with independent per-node inference. The controller orchestrates distributed Long Short-Term Memory execution via SSH and SFTP, achieving end-to-end latency of seven to ten seconds per node per sequence, well within the sixty-second window production rate.

One Introduction

Early fire detection is critical for minimizing structural damage and protecting lives. Traditional smoke detectors provide binary alerts with no spatial context. Modern IoT deployments can do far better: a network of environmental sensors distributed across a physical space can detect fire phases before visible flame appears and, critically, predict whether fire is likely to spread from one zone to another.

The key research contribution of this system is joint spatial-temporal inference: rather than processing each sensor node independently, the controller waits until all N equals five nodes have completed preprocessing for time index T, then triggers inference simultaneously. This allows Model B (the propagation detector) to receive the full spatial state vector of the environment, enabling cross-node fire spread prediction.

The remainder of the paper is organized as follows. Section Two describes the hardware and software architecture. Section Three formalizes the data acquisition and windowing procedure. Section Five details the preprocessing pipeline. Sections Six and Seven present the Long Short-Term Memory and Multi-Layer Perceptron models, respectively. Section Eight formalizes the joint inference mechanism. Section Nine describes the distributed orchestration protocol. Section Ten covers output structure and logging, and Section Twelve analyzes system performance.

Two System Architecture

Two point two Software Stack

Three point one Sensor Sampling

Three point two Sliding Window Construction

Four Preprocessing Pipeline

Four point two Statistical Feature Engineering

Five Detailed Preprocessing Pipeline and Feature Engineering

Five point two Raw Input Data

Five point three Block One: Statistical Feature Engineering

Five point three point one Mean

Five point three point two Standard Deviation

Five point three point three Minimum and Maximum

Five point three point four Delta, Trend Direction.

Five point three point five Base Feature Vector.

Five point four Block Two: Cross-Channel Derived Features.

Five point four point one Final Statistical Feature Vector.

Five point five Block Three: StandardScaler Normalization

Five point six Block Four: Per-Timestep Feature Enrichment

Five point six point one First Differences (Velocity)

Five point six point two Cross-Channel Interaction at Timestep

Five point six point three Squared Gas Resistance

Five point six point four Rate of Change of Gas (Acceleration)

Five point six point five Squared Pressure

Five point six point six Final Per-Timestep Feature Vector

Five point seven Complete Preprocessing Output Summary

Five point eight Standardization for LSTM Input

Five point nine LSTM Feature Tensor Construction

Six Model A: LSTM Fire Phase Classifier

Six point two LSTM Cell Equations

Forget gate

Input gate

Candidate cell state

Cell state update

Output gate

Hidden state

Six point three Classification Head

Six point four Distributed LSTM Execution

Seven Model B: MLP Propagation Detector

Seven point four. Event Thresholding

Eight Joint Spatial-Temporal Inference

Eight point two. Synchronization Condition

Eight point three. Joint State Vector

Eight point four. Alert Level Function

Nine Distributed Orchestration

Nine point two Task Dispatch Protocol

Nine point three Communication Complexity

Ten Output Structure and Logging

Ten point two Inference Log

Eleven Detailed Data Flow into Model A LSTM and Model B MLP: Training and Inference

Eleven point two Data Flow into Model A LSTM Fire Phase Classifier

Eleven point two point two Mini-Batch Construction

Eleven point two point three LSTM Forward Pass

Forget gate

Input gate

Candidate cell state

Cell state update

Output gate and hidden state

Eleven point two point four Classification Head

Eleven point two point five Training: Loss Function

Eleven point two point six Training: Backpropagation Through Time B P T T

Eleven point two point seven Training: Adam Optimizer

Eleven point two point eight Inference: Live Forward Pass

Eleven point three Data Flow into Model B: MLP Propagation Detector

Neighbor phase encoding

Neighbor confidence

Phase differential

Inter-node gas resistance differential

Eleven point three point three Full Model B Input Vector

Eleven point three point four MLP Forward Pass

Eleven point three point six Training: Backpropagation

Eleven point three point seven Inference: Live Propagation Detection

Eleven point four. Complete Training-to-Inference Timeline

Twelve point one. Latency Budget

Twelve point three. Scalability

Thirteen. Conclusion

Overview

The document describes the design and implementation of a distributed AI system for early fire detection, utilizing edge computing and environmental sensor data to predict both fire occurrence and spread with improved spatial context.

Key Points

1The system uses five Raspberry Pi nodes for real-time data processing
2A two-stage AI ensemble includes a Long Short-Term Memory network and a Multi-Layer Perceptron
3Joint spatial-temporal inference enhances prediction accuracy
4The architecture allows for synchronized processing across all nodes
5The preprocessing pipeline ensures data is ready for AI model input.

Details

Authors: Thierry Isaac N'kouka
Category: Technology and Engineering

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