Microcontroller-based internet of things control system for autonomous hidden camera detection using adaptive decision-making and hybrid MCDM techniques

Abstract

The current proliferation of hidden mode surveillance cameras has initiated significant debates on personal privacy as well as information security issues. In this paper, a microcontroller-based model of an IoT system has been implemented for the auto-detection of hidden cameras based on decision-making mechanisms. The model employs a NodeMCU/ESP8266 microcontroller, in conjunction with a passive infrared sensor, which triggers both reflective and emissive properties associated with hidden camera lenses. To increase accuracy, three distinct algorithms have been utilized in conjunction. The results have been communicated in real time via a Telegram Bot interface, making it user-independent. The results showed 95% accuracy, response time of 120 ms, and a false-positive ratio of 2% in controlled indoor experiments, verifying the reliability of the proposed IoT model. This research evaluates a novel approach of combining Analytic Hierarchy Process-Simple Additive Weighting (AHP-SAW), along with ELECTRE-PROMETHEE outrank, as a joint Multi-Criteria Decision-Making approach, indicating the supremacy of the Dynamic Threshold Adjustment (DTA) technique, following Hierarchical Decision-Making Algorithm (HDMA), along with Probabilistic Camera Presence Estimation (PCPE) for probabilistic inference of surveillance risk. The positive outcomes confirm the proposed IoT model's efficacy, as it provides a fast, precise, cost-efficient, as well as energy-efficient model, facilitating hidden camera sensing. The proposed model provides a scalable, smart, and privacy-focused approach, especially useful for hotels, conference rooms, and office spaces.