Lightweight AI Models for Predictive Bluetooth Low Energy Connection Management

  • Authors

    • Vishwaram Software Engineer, USA. Author

    DOI:

    https://doi.org/10.67228/30713498/IJADSMC-V9I3P103

    Published 07-08-2026

  • Bluetooth Low Energy, Lightweight Artificial Intelligence, Predictive Connection Management, Internet Of Things, Machine Learning, Adaptive Connection Intervals, Energy Efficiency, Embedded Intelligence, Link Reliability, And Edge Computing

    Issue

    Vol. 9 No. 3 (2026)

    Section

    Articles

  • Abstract

    Internet of Things (IoT), Wearable devices, Smart healthcare, Industrial monitoring, Battery powered sensor networks are all heavily using Bluetooth Low Energy (BLE). Reliable BLE connections under dynamic mobility, varying signal strength, interference and heterogeneous traffic conditions are still a challenge, however. Traditional connection-management techniques are often based on static parameters, threshold-based decisions or reactive adaptation, leading to higher probability of connections being dropped, energy waste, and communication latency. This paper introduces a lightweight artificial intelligence framework for predictive BLE connection management, which includes compact machine learning models, temporal connection-state analysis, and adaptive parameter selection. The proposed framework relies on received signal strength indication (RSSI), packet delivery ratio, retransmission behavior, connection interval, and recent disconnection history for estimating the quality of the connections in the short term. The mechanism for predicting connection parameters is efficient in computational requirements, and respects the constraints of the BLE protocol and the resource constraints of devices. It is a framework that can be used in non-cloud-based embedded systems with limited resources. A structured approach to evaluation is created to measure the accuracy of prediction, connection stability, latency, and energy efficiency. As the experimental measurements are not provided, the proposed evaluation targets are clearly separated from the validated empirical results.

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