AI-Enabled Thermal and Energy Optimization for Next-Generation Wearable Computing Architectures

  • Authors

    • Thaneswaran Department of CSE, MAM College of Engineering, India. Author

    DOI:

    https://doi.org/10.67228/30713498/IJADSMC-V9I3P102

    Published 07-06-2026

  • Artificial Intelligence, Wearable Computing, Thermal Management, Energy Optimization, Dynamic Voltage And Frequency Scaling, Edge Computing, Battery-Aware Scheduling, Machine Learning, Thermal Prediction, Adaptive Resource Management

    Issue

    Vol. 9 No. 3 (2026)

    Section

    Articles

  • Abstract

    The advancement of next-generation wearable computing systems has enabled continuous health monitoring, augmented reality (AR), and personalized digital services. However, integrating processors, AI accelerators, sensors, wireless communication, and compact batteries creates significant thermal and energy management challenges. Traditional reactive approaches cannot effectively adapt to changing workloads, environmental conditions, and user needs. This paper proposes an AI-enabled framework for optimizing thermal behavior and energy consumption in wearable devices. The framework integrates sensor monitoring, workload characterization, thermal prediction, energy estimation, adaptive task scheduling, dynamic voltage and frequency scaling (DVFS), and intelligent power management. Machine learning predicts thermal and energy requirements, while constraint-based decision-making balances performance, temperature, battery life, and user comfort. A conceptual architecture and experimental evaluation methodology are presented to assess the framework. Since no experimental measurements were collected, the study focuses on expected outcomes, evaluation criteria, and testable hypotheses rather than numerical performance claims. The proposed framework provides a foundation for developing wearable systems that are energy-efficient, thermally safe, reliable, and comfortable for continuous use.

  • References

    [1] Banerjee, S., Mukherjee, T., & Dutt, N. (2007). Exploiting application and concurrency awareness for energy-efficient mapping of streaming applications on MPSoCs. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 26(8), 1401–1412. https://doi.org/10.1109/TCAD.2007.900334

    [2] 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

    [3] 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.

    [4] 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

    [5] Butts, J. A., & Sohi, G. S. (2000). A static power model for architects. In Proceedings of the 33rd Annual IEEE/ACM International Symposium on Microarchitecture (pp. 191–201). IEEE. https://doi.org/10.1109/MICRO.2000.898070

    [6] 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

    [7] Casadei, A., Bhaduri, S., Malshe, R., Mullapudi, P., Ratan, R., Pole, A., & Rakshit, A. (2025). Scaling Causal Mediation for Complex Systems: A Framework for Root Cause Analysis. arXiv preprint arXiv:2512.14764.

    [8] 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

    [9] Hennessy, J. L., & Patterson, D. A. (2019). Computer architecture: A quantitative approach (6th ed.). Morgan Kaufmann.

    [10] 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.

    [11] Horowitz, M. (2014). 1.1 Computing's energy problem (and what we can do about it). In 2014 IEEE International Solid-State Circuits Conference Digest of Technical Papers (ISSCC) (pp. 10–14). IEEE. https://doi.org/10.1109/ISSCC.2014.6757323

    [12] 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

    [13] 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.

    [14] Koomey, J. G., Berard, S., Sanchez, M., & Wong, H. (2011). Implications of historical trends in the electrical efficiency of computing. IEEE Annals of the History of Computing, 33(3), 46–54. https://doi.org/10.1109/MAHC.2010.28

    [15] 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.

    [16] 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

    [17] 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.

    [18] Mittal, S. (2014). A survey of techniques for improving energy efficiency in embedded computing systems. International Journal of Computer Aided Engineering and Technology, 6(4), 440–459. https://doi.org/10.1504/IJCAET.2014.063408

    [19] Wang, Y., Lin, X., & Pedram, M. (2014). A near-optimal model-based control algorithm for the concurrent management of processor energy and temperature. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 33(3), 345–358. https://doi.org/10.1109/TCAD.2013.2294912

    [20] 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.

    [21] Virendra Jangid. (2025). AI-Powered Cross-Border Securities Processing and ADR Platform Optimization. International Journal of Leading Research Publication, 6(11). https://doi.org/10.70528/IJLRP.v6.i11.2233

    [22] 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.

    [23] Zhang, Y., & Chatha, K. S. (2010). Approximation-aware scheduling for voltage-scaled hard real-time systems. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 29(1), 118–131. https://doi.org/10.1109/TCAD.2009.2034395

    [24] 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.

    [25] 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.

    [26] Sunkara, R. (2024). Hardware-in-the-Loop Power Profiling Automation for Consumer Streaming Devices: A Multi-Lab Framework for Regulatory Compliance Validation. International Journal of Emerging Trends in Computer Science and Information Technology, 5(4), 187-191. https://doi.org/10.63282/3050-9246.IJETCSIT-V5I4P121

    [27] 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.

    [28] Nangi, P. R., & Obannagari, R. N. CK, & Settipi, S.(2024). A Federated Zero-Trust Security Framework for Multi-Cloud Environments Using Predictive Analytics and AI-Driven Access Control Models. International Journal of Emerging Research in Engineering and Technology, 5(2), 95-107.

  • Downloads