article · Discover Computing
Unauthorised surveillance through hidden cameras poses severe privacy and corporate security concerns. An automated Internet of Things system provides a low-cost method for detecting concealed cameras. Built around a NodeMCU/ESP8266 microcontroller and a passive infrared sensor, the device identifies the reflective and emissive signatures typical of camera lenses. It relies on multiple combined decision-making techniques, including dynamic threshold adjustments and probabilistic estimation, to evaluate surveillance risks with minimal error. Alerts are generated autonomously and dispatched in real time via a Telegram Bot interface, eliminating the need for continuous operator monitoring. Controlled indoor testing demonstrated that the unit achieves a 95 percent detection accuracy, a rapid response time of 120 milliseconds, and a low false-positive rate of two percent. The design offers an energy-efficient, scalable solution for protecting private environments.
Hidden surveillance devices present serious risks to individual privacy and confidential business operations. Manual detection methods are often expensive, slow, or technically complex. Deploying an autonomous, low-cost sensor system allows businesses and individuals to protect sensitive areas, such as meeting rooms and rented accommodations, against unauthorised recording without requiring specialised technical knowledge or continuous supervision.
The system is targeted at hospitality operators, corporate security teams, and facility managers securing hotel rooms, offices, and conference spaces. The technology appears to be at an applied and tested stage, having completed functional validation in controlled indoor settings. Transitioning to a viable commercial product will require packaging the hardware into an unobtrusive form factor and conducting broader real-world performance trials beyond controlled conditions.
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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.
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DOI: 10.1007/s10791-026-10480-8
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