Edge Triggering in IoT Mesh Networks: A Comparative Monte Carlo Study of Seven Detection Algorithms
Sergii Makovetskyi, Lars Thomsen

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…
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