Predictive Communication Control for Reliable Bluetooth Low Energy Connections in Autonomous Edge Systems

  • Authors

    • Jospeh Kuruvilla Software Engineer, USA. Author

    DOI:

    https://doi.org/10.67228/3071642X/IJCFDE-V9I3P103

    Published 07-09-2026

  • Bluetooth Low Energy (BLE), Predictive Communication Control, Autonomous Edge Systems, Machine Learning, Wireless Connection Reliability, Adaptive Communication, Edge Computing, Energy Efficiency, Packet Delivery Ratio, Internet Of Things (Iot)

    Issue

    Vol. 9 No. 3 (2026)

    Section

    Articles

  • Abstract

    BLE is widely used in autonomous edge systems due to its low power consumption, cost-effectiveness, and short-range wireless connectivity. However, signal interference, packet loss, latency fluctuations, and limited energy resources can affect connection stability. To address these challenges, we propose a machine learning-based predictive communication control framework that uses real-time metrics and historical patterns to predict communication failures and dynamically optimize connection intervals, transmission methods, and recovery strategies. By enabling edge-based decision-making, the framework aims to improve connection reliability, reduce latency and connection drops, and enhance energy efficiency without relying on continuous cloud connectivity. The framework can be applied to autonomous robotics, industrial monitoring, wearables, and intelligent IoT systems, with performance evaluated through connection reliability, packet delivery ratio, latency, reconnection time, and energy consumption.

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