Edge Triggering in IoT Mesh Networks: A Comparative Monte Carlo Study of Seven Detection Algorithms

TL;DR

This study compares seven detection algorithms for IoT mesh networks, demonstrating that a combined three-defence approach achieves perfect detection with no false positives under realistic noise conditions.

Contribution

It introduces TSNFA, a novel detection method that outperforms alternatives by integrating spectral, temporal, and adaptive noise-floor defenses.

Findings

TSNFA achieves 100% detection rate with zero false positives.

Competing algorithms lack at least one of the three defenses, leading to higher false positives.

The three-defence combination is necessary and sufficient for autonomous edge triggering.

Abstract

Real-time event detection in Internet of Things (IoT) mesh sensor networks presents significant challenges due to time-varying noise conditions, limited computational resources at edge nodes, and the need for autonomous operation without centralised coordination. This paper presents a comprehensive Monte Carlo simulation study comparing the Temporal Spectral Noise-Floor Adaptation (TSNFA) method against six alternative detection algorithms, evaluated across a 200-node mesh network over 24 hours with realistic noise models including 60 Hz electromagnetic interference (EMI), sinusoidally drifting noise power (+/- 6 dB), and intermittent digital switching bursts. TSNFA achieves 100% detection rate with zero false positives, uniquely combining three interlocking defences: spectral band selection, temporal persistence filtering, and adaptive noise-floor tracking. Every competing algorithm omits at least one of these three defences and fails correspondingly, with false-positive rates ranging from 0 (Send-on-Delta, which also detects nothing) to 13,387,930 (broadband energy ratio). These results identify the three-defence combination as necessary and sufficient for autonomous edge triggering in resource-constrained IoT deployments.