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Stream/Area:
-
Subject:
-
Paper ID:
IJSREM65573
Volume:
10 |
6 |
Jun 2026
Pages:
1-4
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.
Electronics and telecommunication engineering
Savitribai Phule university
Electronics and telecommunication engineering
Savitribai Phule university
Electronics and telecommunication engineering
Savitribai Phule university
Electronics and telecommunication engineering
Savitribai Phule university
Reliable cellular connectivity is increasingly central to daily communication, yet end users routinely encounter signal fluctuation, coverage gaps, and call instability that commercial telecom monitoring platforms are too costly or too infrastructure-heavy to diagnose at a local scale. This paper presents the Hybrid Telecom Network Analyzer, a low-cost embedded-IoT system that performs real-time signal mapping by combining a SIM900A GSM module, an Arduino Uno microcontroller, and a Python-based data analytics pipeline. The Arduino Uno polls the GSM module over serial communication using AT command queries (AT+CSQ) to obtain received signal strength indicator (RSSI) values, which are streamed to a host computer and processed using Pandas and NumPy for data structuring and statistical analysis. Processed signal trends are rendered through Matplotlib and exposed to the end user via an interactive Streamlit dashboard supporting live visualization of network behaviour. A simulated-data fallback routine was implemented to sustain continuous dashboard operation during intervals of module non-response, ensuring uninterrupted demonstration of the analytics and visualization layers independent of transient hardware faults. The complete prototype was assembled using jumper-wire interconnects without a breadboard, validated for serial data integrity, and evaluated for dashboard responsiveness under varying signal conditions. Results confirm that the integration of embedded telecom hardware with a Python data-science stack yields a scalable, low-cost alternative to commercial network-monitoring infrastructure, suitable for academic, research, and small-scale telecom diagnostic applications.
KEYWORDS
This paper presented the Hybrid Telecom Network Analyzer, a low-cost embedded-IoT system integrating a SIM900A GSM module, an Arduino Uno microcontroller, and a Python-based data-analytics and dashboard stack for real-time telecom signal monitoring. The hardware-to-software data pipeline was implemented and verified end-to-end, with the Pandas/NumPy/Matplotlib/Streamlit chain reliably converting serial telemetry into a live, interpretable dashboard. Intermittent GSM modem response reliability was identified as the principal hardware-level limitation encountered during testing, and a transparent simulated-data fallback was used to keep the analytics pipeline demonstrably operational independent of that constraint. Future work should prioritize resolving the AT+CSQ response reliability issue — through improved power regulation, modem firmware verification, or substitution of the SIM900A with a more deterministic cellular module — so that the validated analytics pipeline can operate on fully live data. Additional extensions include GPS-tagged signal mapping for spatial coverage analysis, cloud-based historical data storage, and a lightweight machine-learning layer for predictive signal-quality forecasting, building on the modular architecture established in this work.
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Publication Date
6/27/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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Conflict of Interest
The authors declare no conflict of interest related to this research work.
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No funding information available for this research.
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