This research outlines the creation of a tri-band radio-frequency (RF) detection and monitoring system aimed at

addressing the increasing abuse of mobile phones and other wireless electronic gadgets in classrooms and exam

and a 1N34A germanium diode-based RF detector for sensing cellular signals in the 900 MHz to 2.4 GHz

spectrum. A TFT display interface, along with an alert system, enables real-time tracking and alerts. The system

is developed to encompass standard classroom and exam hall settings (150 m² to 450 m²) and accommodates

adjustable sensitivity and time-controlled functionality. Experimental findings showed that Wi-Fi signals display

consistent behavior, Bluetooth signals manifest as sporadic spikes, and cellular signals present as burst

transmissions, facilitating dependable classification. The proposed solution provides an affordable, flexible, and

unobtrusive system for improving academic honesty and minimizing wireless examination misconduct.

Keywords: Transceiver, Germanium diode, Radio frequency, RSSI, Detector

The growing presence and misuse of wireless-enabled devices in classrooms and examination settings pose a

ubiquitous wireless connectivity in educational settings often leads to distraction, reduced student engagement

(Eserinune, 2015), and potential misuse, particularly during assessments when unauthorized communication

may occur (Educause Review, 2009; Elma Fe E. Gupit). While wireless-enabled devices can enhance learning

when appropriately regulated, their unregulated use is widely acknowledged as a significant source of distraction

and academic misconduct, thereby necessitating effective monitoring and control strategies. Students

increasingly exploit these technologies during examinations to gain unfair advantages by accessing unauthorized

materials, notes, or digital resources (Nyamawe & Mtonyole; Madara & Namango, 2016). In addition, these

devices are often used to enable covert communication with peers within or outside examination venues, thereby

facilitating cheating and collaborative malpractice (Saka, Ologun, & Nelson, 2022; Selwyn, 2016). Traditional

strategies for mitigating wireless device misuse in academic environments have relied on policy enforcement,

support vector machines and random forests—have demonstrated high detection accuracy and reduced false

alarms (Misra et al., 2010; Arjoune et al., 2020). Nevertheless, these methods are often computationally intensive

and data-dependent, limiting their suitability for low-cost, real-time embedded applications. Additionally, the

increasing sophistication of modern wireless technologies, including encryption and MAC address

randomization, has reduced the effectiveness of traditional network-based monitoring, necessitating physical-

layer sensing methods such as RSSI analysis, spectral occupancy estimation, and temporal signal

characterization (Sharma & Kalekar, 2026). Despite these advances, many existing systems remain limited to

single-band operation or specific technologies and fail to provide a comprehensive, real-time view of the RF

environment. There is therefore a clear need for a cost-effective, non-intrusive, and multi-band RF monitoring

system capable of simultaneously detecting and classifying Wi-Fi, Bluetooth, and cellular signals. The proposed

solutions. Nevertheless, the broad use of these technologies has also amplified chances for exam misconduct via

illicit internet access, hidden device-to-device communication, and distant information sharing (Singh &

Sharma, 2024). The growing prevalence of smartphones, smartwatches, wireless earbuds, and other compact

wireless gadgets has made detection and monitoring more challenging, as these devices can function subtly and

avoid traditional surveillance techniques (Selwyn, 2016; Eryenyu, Atibun, & Biira, 2025). Different methods

have been suggested to manage unapproved wireless communication in educational settings. Conventional

techniques, such as manual oversight, stringent supervision, and enforcement of institutional policies, are still

commonly utilized but are frequently limited by human factors and the growing complexity of wireless

technologies (Abiebhode & Ifechukwu, 2024). RF jamming methods have been researched and proven effective

in disrupting cellular and Wi-Fi communications; nonetheless, their use is constrained by legal limitations,

these methods are usually confined to basic detection and lack sophisticated features like multi-band observation,

RSSI measurement, spectral occupancy assessment, temporal behavior analysis, and intelligent signal

categorization. Recent research has investigated spectrum sensing, deep learning, and software-defined radio

(SDR)-based architectures to enhance detection precision (Zhang & Luo, 2024). While these systems showed

potential effectiveness, their reliance on costly hardware and intricate setups restricts their applicability for broad

use in educational settings (H et al., 2025; Bajic et al., 2012; Ismail et al., 2024). Received Signal Strength

Indicator (RSSI) is widely acknowledged as a valuable metric for detecting and classifying wireless signals.

RSSI offers an effective assessment of received signal strength and facilitates the analysis of wireless signal

patterns over time (Mohsin, Abdulameer, & Khudhair, 2017). Earlier research has shown that Wi-Fi, Bluetooth,

and cellular technologies possess unique RSSI profiles and temporal features, where Wi-Fi demonstrates

for signal categorization and wireless activity tracking. Despite these improvements, current systems continue

for analyzing RSSI, evaluating spectral occupancy, characterizing temporal behavior, and generating heatmaps.

The proposed system offers a low-cost, passive, scalable, and regulation-adhering solution for wireless activity

tracking, aiding in decreasing examination misconduct and improving academic integrity within educational

settings.

1N34A germanium diode-based RF detector and the NRF2401 transceiver module. The NRF24L01 interfaces

with the ESP32 via SPI, enabling precise control and fast communication for scanning the 2.4 GHz ISM band

(2.400–2.525 GHz). It uses its Received Power Detector (RPD) feature to continuously scan 125 channels for

RF energy. To estimate approximate RSSI values, calculate spectral occupancy, and examine temporal patterns,

all crucial for differentiating between intermittent Bluetooth transmissions resulting from frequency hopping and

continuous Wi- Fi signals, the ESP32 aggregates these detection results over time. For cellular signal detection,

the 1N34A germanium diode-based RF detector was connected to the ESP32 via its ADC input, enabling analog

signal acquisition. The diode transforms high- frequency RF signals between 900 MHz and 2.3 GHz into a

proportionate DC voltage using the envelope detection principle. An RC filter conditions this signal before the

ESP32' s ADC digitizes it. The system can identify burst- type cellular activity, typical of time- scheduled mobile

communication, by mapping the resulting digital values to equivalent RSSI levels. To distinguish between Wi-

spectral occupancy and temporal characteristics. This makes it possible to clearly distinguish and categorize

cellular, Bluetooth, and Wi- Fi signals, enabling precise RF activity monitoring. The system is powered by a

2000 mAh rechargeable battery, ensuring portability and 6–7 hours of operation. The TFT display and buzzer,

integrated for real- time monitoring and alerting, improve situational awareness for users, making it an effective,

affordable, and non- intrusive solution for wireless signal monitoring.

The system design and implementation involved hardware integration, firmware development, RF signal

acquisition, real-time processing, visualization, calibration, and performance evaluation. The ESP32

microcontroller, as depicted in Figures 1 and 2, served as the main control and processing unit because of its

high computational capacity, integrated ADC channels, support for SPI communication, and low power

Figure 2: 1N34A germanium diode detector

For Wi-Fi and Bluetooth sensing, the NRF24L01 transceiver module was interfaced with the ESP32 via the SPI

protocol to enable high-speed communication during spectral scanning. The NRF24L01 continuously scanned

125 channels within the 2.4 GHz ISM band, using its Received Power Detector (RPD) to detect RF activity

above a predefined threshold. Detection counts accumulated across channels and over time samples, and were

used to estimate RSSI values and evaluate spectral occupancy using:

wireless activity patterns and spectral behavior. The TFT display and buzzer alert subsystem, incorporated for

portability, energy efficiency, and suitability for real-time wireless monitoring applications in academic

environments.

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