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.

  • References

    [1] Cho, K., Park, W., Hong, M., Park, G., Cho, W., Seo, J., & Han, K. (2015). Analysis of latency performance of Bluetooth Low Energy (BLE) networks. Sensors, 15(1), 59–78. https://doi.org/10.3390/s150100059

    [2] Gomez, C., Oller, J., & Paradells, J. (2012). Overview and evaluation of Bluetooth Low Energy: An emerging low-power wireless technology. Sensors, 12(9), 11734–11753. https://doi.org/10.3390/s120911734

    [3] Liu, L., Niu, M., Zhang, C., & Shu, J. (2022). Light Gradient Boosting Machine-based link quality prediction for wireless sensor networks. Wireless Communications and Mobile Computing, 2022, Article 8278087. https://doi.org/10.1155/2022/8278087

    [4] Arora, A. S., Yachamaneni, T., & Kotadiya, U. (2021). Optimizing Multi-Tenant Resource Allocation in Cloud-Based Distributed Systems for Large-Scale AI Model Training Using In-Memory Computing. International Journal of Emerging Research in Engineering and Technology, 2(1), 37-46.

    [5] Paruchuri, J. K. (2022). Survey of cloud-native workflow orchestration with Apache Airflow. International Journal of Computer Techniques (IJCT), 9(3), 1-15.

    [6] Brahmandam, L. M. K. (2026). A Decision Framework for Multi-Cloud Microservice Deployment across AWS and GCP: Empirical Evaluation of EKS, Cloud Functions, Cloud Run, and Cross-Cloud Networking Patterns. International Journal of Emerging Trends in Computer Science and Information Technology, 7(1), 365-373. https://doi.org/10.63282/3050-9246.IJETCSIT-V7I1P152

    [7] Sandra, K. (2024). Data ecosystem modernization ROI: Measurement frameworks and case studies. International Journal of Computer Science Engineering Techniques, 12(6), 1–5.

    [8] Cerar, G., Yetgin, H., Mohorčič, M., & Fortuna, C. (2021). Machine learning for wireless link quality estimation: A survey. IEEE Communications Surveys & Tutorials, 23(2), 696–728. https://doi.org/10.1109/COMST.2021.3053615

    [9] Shu, J., et al. (2017). Research on link quality estimation mechanism for wireless sensor networks based on support vector machine. Chinese Journal of Electronics. https://doi.org/10.1049/cje.2017.01.013

    [10] Seknametla, P. R., & Sunkara, R. (2025). Applying AIOps for Predictive Incident Management in DevOps-Driven Cloud Infrastructure. International Journal, 12(6).

    [11] Mach, P., & Becvar, Z. (2017). Mobile edge computing: A survey on architecture and computation offloading. IEEE Communications Surveys & Tutorials, 19(3), 1628–1656. https://doi.org/10.1109/COMST.2017.2682318

    [12] Kong, L., Tan, J., Huang, J., Chen, G., et al. (2022). Edge-computing-driven Internet of Things: A survey. ACM Computing Surveys. https://doi.org/10.1145/3555308

    [13] Chen, M., Challita, U., Saad, W., Yin, C., & Debbah, M. (2019). Artificial neural networks-based machine learning for wireless networks: A tutorial. IEEE Communications Surveys & Tutorials, 21(4), 3039–3071. https://doi.org/10.1109/COMST.2019.2926625

    [14] Kotadiya, U., Arora, A. S., & Yachamaneni, T. (2024). Intelligent Orchestration of Cloud-Native Applications Using Google Cloud Platform and Microservices-Based Architectures. International Journal of AI, BigData, Computational and Management Studies, 5(4), 106-114.

    [15] Sutton, R. S., & Barto, A. G. (2018). Reinforcement learning: An introduction (2nd ed.). MIT Press. http://incompleteideas.net/book/the-book-2nd.html

    [16] Paruchuri, J. K. (2021). Exactly-once semantics in distributed stream processing at scale. International Journal of Computer Science Engineering Techniques (IJCSE), 5(1), 1-15.

    [17] Uddandarao, D. P., Mahajan, A., & Krishnamsetty, P. Double Machine Learning for Downstream Causal Measurement: A Hybrid Econometric-ML Framework for Feature Valuation.

    [18] Gantikota, S. (2024). Shift-Left Security for Decentralized Engineering Organizations: Embedding SAST, DAST, and Penetration Testing Throughout the Software Development Lifecycle in University and Research Computing Environments. International Journal of Emerging Research in Engineering and Technology, 5(4), 175-179. https://doi.org/10.63282/3050-922X.IJERET-V5I4P118

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