Low-Latency Intelligent Control for Coordinating Communication and Computation in Connected Devices

  • Authors

    • Karthick Quality Analyst, USA. Author

    DOI:

    https://doi.org/10.67228/30715636/IJETMR-V9I2P105

    Published 04-12-2026

  • Adaptive Control, Edge Computing, Connected Devices, Computation Offloading, Communication Scheduling, Internet Of Things (Iot), Intelligent Resource Allocation, Low-Latency Networks, Real-Time Systems, Time-Sensitive Networking (TSN)

    Issue

    Vol. 9 No. 2 (2026)

    Section

    Articles

  • Abstract

    As connected devices, platforms for the Industrial Internet of Things (IIoT), and applications to leverage edge computing proliferate, there is an increasing demand for efficient coordination between communication and computation. Legacy distributed systems tend to be independent in their control decisions, scheduling and resource management, leading to higher latencies, energy consumption and operational inefficiency. Based on real-time telemetry monitoring, workload prediction, adaptive task scheduling, computation offloading and edge resource allocation, this paper introduces a Low-Latency Intelligent Control framework. It takes into account communication latency, computational needs, task deadline, energy consumption, and control accuracy, and aims to maximize the overall performance of the end-to-end system. Under dynamic operating conditions, a hierarchical architecture offers the ability to make fast decisions at the device level and manage the edge resources in a coordinated manner. It includes predictive scheduling and fallback features to enhance responsiveness in cases of network congestion and resource limitations. Latency, deadline satisfaction, energy consumption, and resource utilization are used to define a systematic evaluation methodology. The proposed approach provides a basis for the design of dependable, energy-efficient and responsive systems for connected devices, which needs to be experimented and analyzed for stability.

  • References

    [1] W. Shi, J. Cao, Q. Zhang, Y. Li, and L. Xu, “Edge Computing: Vision and Challenges,” IEEE Internet of Things Journal, vol. 3, no. 5, pp. 637–646, Oct. 2016, doi: 10.1109/JIOT.2016.2579198.

    [2] Mullapudi, P. N. (2025). Pricing optimization across domains: A comparative review. International Journal of Emerging Research in Engineering and Technology, 6(3), 16-33.

    [3] Hosahalli Seenappa, V. K. (2025). Next-generation intelligent multi-cloud enterprise networking: Integrating secure architecture, AI-optimized traffic engineering, and LLM-driven autonomous network operations. International Journal of Leading Research Publication (IJLRP), 6(12), 1–11

    [4] P. Mach and Z. Becvar, “Mobile Edge Computing: A Survey on Architecture and Computation Offloading,” IEEE Communications Surveys & Tutorials, vol. 19, no. 3, pp. 1628–1656, 2017, doi: 10.1109/COMST.2017.2682318.

    [5] Nangi, P. R., Obannagari, C. K. R. N., & Settipi, S. (2022). Enhanced Serverless Micro-Reactivity Model for High-Velocity Event Streams within Scalable Cloud-Native Architectures. International Journal of Emerging Research in Engineering and Technology, 3(3), 127-135.

    [6] Y. Mao, C. You, J. Zhang, K. Huang, and K. B. Letaief, “A Survey on Mobile Edge Computing: The Communication Perspective,” IEEE Communications Surveys & Tutorials, vol. 19, no. 4, pp. 2322–2358, 2017, doi: 10.1109/COMST.2017.2745201.

    [7] Bhaduri, S., Mullapudi, P., Dietrich, S., DeWaters, S., Ratan, R., Kashyap, B., ... & Rakshit, A. (2025). Beyond mere automation: A techno-functional framework for reimagining gen-AI in supply chain operations.

    [8] X. Chen, L. Jiao, W. Li, and X. Fu, “Efficient Multi-User Computation Offloading for Mobile-Edge Cloud Computing,” IEEE/ACM Transactions on Networking, vol. 24, no. 5, pp. 2795–2808, Oct. 2016, doi: 10.1109/TNET.2015.2487344.

    [9] Tayal, C. (2024). Big Data Pipeline Optimisation for Electronic Health Records (EHR). International Journal of Artificial Intelligence, Data Science, and Machine Learning, 5(3), 121-127

    [10] A. Shakarami, M. Ghobaei-Arani, and A. Shahidinejad, “A Survey on the Computation Offloading Approaches in Mobile Edge Computing: A Machine Learning-Based Perspective,” Computer Networks, vol. 182, Art. no. 107496, 2020, doi: 10.1016/j.comnet.2020.107496.

    [11] Mullapudi, P. N., Bhaduri, S., Casadei, A., Guo, L., Wang, H., Ashok, V., ... & Rakshit, A. (2025). An end-to-end Causal Modeling Framework for Advanced Attribution in Supply Chain Operations. In KDD 2025 Workshop on AI for Supply Chain: Today and Future.

    [12] “Computation Offloading in Mobile Edge Computing Networks: A Survey,” Journal of Network and Computer Applications, vol. 202, Art. no. 103366, 2022, doi: 10.1016/j.jnca.2022.103366.

    [13] Pavan Nithin Mullapudi (2024). An Adaptive Explainability Framework for Machine Learning Predictions of Deals in Cloud Computing . International Journal of Multidisciplinary Research and Growth Evaluation (IJMRGE), 5(3), 1027-1034. DOI: https://doi.org/10.54660/.IJMRGE.2024.5.3.1027-1034

    [14] Reddy Nangi, P., & Reddy Nala Obannagari, C. K. (2023). Scalable End-to-End Encryption Management Using Quantum-Resistant Cryptographic Protocols for Cloud-Native Microservices Ecosystems. International Journal of Emerging Trends in Computer Science and Information Technology, 4(1), 142-153.

    [15] S. Dong, J. Tang, K. Abbas, R. Hou, J. Kamruzzaman, L. Rutkowski, and R. Buyya, “Task Offloading Strategies for Mobile Edge Computing: A Survey,” Computer Networks, 2025, doi: 10.1016/j.comnet.2024.110791.

    [16] Visweswaran, K. (2026, March). Low-Latency AI Models for Predictive Connection Management in Bluetooth Low Energy (BLE) Communication Systems. In 2026 6th International Conference on Expert Clouds and Applications (ICOECA) (pp. 541-548). IEEE. https://doi.org/10.1109/ICOECA68095.2026.11485045

    [17] M. Satyanarayanan, “The Emergence of Edge Computing,” Computer, vol. 50, no. 1, pp. 30–39, Jan. 2017, doi: 10.1109/MC.2017.9.

    [18] Syed, Y. (2023). Polyglot Persistence and Enterprise Data Migration: Architectures, Consistency, and Practice. American International Journal of Computer Science and Technology, 5(3), 61-66. https://doi.org/10.63282/3117-5481/AIJCST-V5I3P106

    [19] Y. C. Hu, M. Patel, D. Sabella, N. Sprecher, and V. Young, “Mobile Edge Computing—A Key Technology Towards 5G,” ETSI White Paper No. 11, Sep. 2015.

    [20] Nangi, P. R. (2022). Multi-Cloud Resource Stability Forecasting Using Temporal Fusion Transformers. International Journal of Artificial Intelligence, Data Science, and Machine Learning, 3(3), 123-135.

    [21] M. Aazam and E.-N. Huh, “Fog Computing: The Cloud-IoT/IoE Middleware Paradigm,” IEEE Potentials, vol. 35, no. 3, pp. 40–44, May–June 2016, doi: 10.1109/MPOT.2015.2456213.

    [22] Syed, Y. (2025). Generative AI Integration and RAG-Based Enterprise Tooling: Architectures, Techniques, and Organizational Impact. International Journal of Scientific Research in Science, Engineering and Technology, 12(6), 600-606. DOI: https://doi.org/10.32628/IJSRSET2613255

    [23] P. Popovski et al., “Wireless Access for Ultra-Reliable Low-Latency Communication: Principles and Building Blocks,” IEEE Network, vol. 32, no. 2, pp. 16–23, Mar.–Apr. 2018, doi: 10.1109/MNET.2018.1700258.

    [24] Nangi, P. R., Obannagari, C. K. R. N., & Settipi, S. (2024). Serverless computing optimization strategies using ML-based auto-scaling and event-stream intelligence for low-latency enterprise workloads. International Journal of Emerging Trends in Computer Science and Information Technology, 5(3), 131-142.

    [25] IEEE, IEEE Standard for Local and Metropolitan Area Networks—Bridges and Bridged Networks—Amendment: Enhancements for Scheduled Traffic, IEEE Std. 802.1Qbv-2015, 2016.

    [26] Nangi, P. R., & Obannagari, R. N. CK, & Settipi, S.(2022). Predictive SQL Query Tuning Using Sequence Modeling of Query Plans for Performance Optimization. International Journal of AI, BigData, Computational and Management Studies, 3(2), 104-113.

    [27] IEEE, IEEE Standard for Local and Metropolitan Area Networks—Bridges and Bridged Networks, IEEE Std. 802.1Q-2022, 2022.

    [28] J. S. Kumar, D. R. Patel, and related research literature on industrial Internet of Things resource allocation and scheduling should be supplemented with a verified, topic-specific publication before inclusion in a submitted manuscript.

    [29] Syed, Y. (2024). DevSecOps and Enterprise-Scale CI/CD Pipeline Engineering: Governing Delivery Velocity, Embedded Security, and Release Automation Across Polyglot Environments. International Journal of Scientific Research in Science, Engineering and Technology, 11(5), 475-484. DOI: https://doi.org/10.32628/IJSRSET2513156

    [30] Nangi, P. R., & Settipi, S. (2023). A cloud-native serverless architecture for event-driven, low-latency, and AI-enabled distributed systems. International Journal of Emerging Research in Engineering and Technology, 4(4), 128-136.

    [31] M. Wollschlaeger, T. S mutz, and S. Jasperneite, “The Future of Industrial Communication: Automation Networks in the Era of the Internet of Things and Industry 4.0,” IEEE Industrial Electronics Magazine, vol. 11, no. 1, pp. 17–27, Mar. 2017, doi: 10.1109/MIE.2017.2649104.

    [32] Y. Syed, “Enterprise Observability and Software Delivery Performance: From Telemetry to Engineering Intelligence”, IJADSMC, vol. 5, no. 2, pp. 01–08, Sep. 2022, doi: 10.67228/30713498/IJADSMC-2022PIIKP2M.

    [33] Nangi, P. R., Obannagari, C. K. R. N., & Settipi, S. (2022). Self-auditing deep learning pipelines for automated compliance validation with explainability, traceability, and regulatory assurance. International Journal of Artificial Intelligence, Data Science, and Machine Learning, 3(1), 133-142.

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