Performance Evaluation of AI-Based Predictive Connectivity Models for Bluetooth Low Energy Sensor Networks

  • Authors

    • Samreen Fatima College, India. Author

    DOI:

    https://doi.org/10.67228/30715725/IJIARE-V9I2P102

    Published 04-08-2026

  • Artificial Intelligence, Bluetooth Low Energy, Connectivity Prediction, Machine Learning, Predictive Maintenance, Received Signal Strength Indicator, Sensor Networks, Wireless Internet of Things

    Issue

    Vol. 9 No. 2 (2026)

    Section

    Articles

  • Abstract

    The advantages of low power consumption, low cost and intermittent wireless transmission of Bluetooth Low Energy (BLE) make it a good choice for sensor networks with limited resources. But in dynamic radio conditions, mobility of nodes, interference, and varying traffic patterns could cause degradation in connectivity and packet loss, retransmissions, and energy usage. This paper proposes a framework to assess the performance of the artificial intelligence (AI) based predictive connectivity models for BLE sensor networks. The methodology proposed encompasses a combination of received signal strength indicator (RSSI), packet delivery ratio (PDR), connection interval, latency, retransmission statistics and features related to energy to forecast impending connectivity degradation. In addition to threshold-based connectivity management, lightweight machine learning models, such as logistic regression, decision trees, random forests and compact neural network are taken into consideration. The performance is measured by the classification accuracy, precision, recall, F1-score, prediction lead time and energy related indicators. A comparative evaluation framework based on percentages is included to show how the evaluation is performed. The study highlights the need for reliability of the model, low computational requirements, and deployment feasibility, enabling predictive connectivity management for low-power Internet of Things applications.

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