Signal Mapper: Real-Time Crowdsourced Mapping of Cellular and Wi-Fi Signal Strength

Signal Mapper: Real-Time Crowdsourced Mapping  of Cellular and Wi-Fi Signal Strength

1st Akshai Krishna A

Department of Computer Science and Engineering

Panimalar Institute of Technology

Chennai, India

akshaykrishna1983@gmail.com

2rd Mrs. B. Bala Abirami

Department of Computer Science and Engineering

Panimalar Institute of Technology

Chennai, India

bala.bami@gmail.com

Abstract—"Signal Mapper" is a real-time, community-driven mobile application designed to provide users with localized insights into cellular and Wi-Fi signal strengths. It utilizes crowdsourced data to generate interactive heat maps, allowing users to visualize real-time connectivity and monitor network reliability across diverse locations. A machine learning model predicts signal strength in under-mapped areas to bridge data gaps using terrain features and historical data. The app offers offline mode, custom alerts, and signal history tracking features, making it a valuable tool for travelers, remote workers, and network engineers. By integrating user-driven improvements and predictive analytics, Signal Mapper enhances coverage accuracy and fills gaps left by traditional network maps. This paper discusses the system architecture, machine learning implementation, and the impact of real-time signal tracking on network reliability and user experience. Future enhancements include improved privacy mechanisms and collaborations with telecom providers.

Keywords—Crowdsourced Mapping, Signal Strength Prediction, Machine Learning, Wireless Networks, Real-Time Data, Network Coverage, Mobile Application, Geospatial Analytics