Energy Efficient Algorithm Design for Edge Computing in Resource Constrained Internet of Things Networks

Hannah Arendt Walter

Energy Efficient Algorithm Design for Edge Computing in Resource Constrained Internet of Things Networks

Authors

Hannah Arendt Walter

IoT Systems Architect, Australia.

Keywords:

Edge computing, IoT, energy efficiency, algorithm design, resource-constrained networks, adaptive scheduling, hybrid computation

Synopsis

With the increasing prevalence of Internet of Things (IoT) devices in smart cities, healthcare, agriculture, and industrial applications, the demand for real-time processing has led to the rapid adoption of edge computing. However, IoT nodes are typically resource-constrained, possessing limited energy, memory, and computational capacity. This paper addresses the critical challenge of designing energy-efficient algorithms tailored for edge computing in such constrained networks. We propose a hybrid approach that combines lightweight computation models with adaptive scheduling and offloading mechanisms to optimize energy consumption without compromising latency and throughput. Our analysis demonstrates improved energy performance when compared to traditional cloud-based and non-optimized edge deployments.

References

[1] Chatterjee, R., Bera, S., & Misra, S. (2023). Federated learning for energy-aware scheduling in IoT. IEEE Transactions on Industrial Informatics, 19(2), 2301-2312. https://doi.org/10.1109/TII.2022.3188199

[2] Gundaboina A. Data Loss Prevention in Healthcare: Advanced Strategies for Protecting PHI in Cloud Environments. Journal of Artificial Intelligence, Machine Learning and Data Science 2023 1(2), 3045-3051. DOI: doi.org/10.51219/JAIMLD/anjan-gundaboina/628

[3] He, Y., Lin, Y., & Lin, S. (2021). RL-based task allocation for IoT edge computing. IEEE Access, 9, 11523-11533. https://doi.org/10.1109/ACCESS.2021.3051234

[4] Gundaboina, A. (2024). HITRUST Certification Best Practices: Streamlining Compliance for Healthcare Cloud Solutions. International Journal of Computer Science and Information Technology Research, 5(1), 76–94. https://ijcsitr.org/index.php/home/article/view/IJCSITR_2024_05_01_008

[5] Khan, R., Rehman, S. U., & Zaman, S. (2019). Edge intelligence with deep learning on IoT devices. Journal of Network and Computer Applications, 136, 62-79. https://doi.org/10.1016/j.jnca.2019.03.004

[6] Satyanarayanan, M. (2017). The emergence of edge computing. Computer, 50(1), 30–39. https://doi.org/10.1109/MC.2017.9

[7] Uppuluri, V. (2023). Design and Deployment of Predictive Models for Influenza Breakthrough Infections Using Pharmacy Test Data. Journal of Artificial Intelligence, Machine Learning & Data Science, 1(2), 3031–3037. https://doi.org/10.51219/JAIMLD/vijitha-uppuluri/626

[8] Shi, W., Cao, J., Zhang, Q., Li, Y., & Xu, L. (2016). Edge computing: Vision and challenges. IEEE Internet of Things Journal, 3(5), 637–646. https://doi.org/10.1109/JIOT.2016.2579198

[9] Potla, R.B. (2023). Supplier Collaboration Portals for Component Manufacturers: Procure-to-Pay Automation and Working-Capital Outcomes. International Journal of Artificial Intelligence (ISCSITR-IJAI), 4(1), 16–40. https://doi.org/10.63397/ISCSITR-IJAI_04_01_002

[10] Yu, W., Liang, F., He, X., Hatcher, W. G., Lu, C., Lin, J., & Yang, X. (2018). A survey on the edge computing for the Internet of Things. IEEE Access, 6, 6900–6919. https://doi.org/10.1109/ACCESS.2017.2778504

[11] Vallemoni, R.K. (2023). Merchant Onboarding and Risk Scoring: Data Governance, Master Data, and Golden-Record Strategies. ISCSITR - International Journal of Scientific Research in Information Technology (ISCSITR-IJSRIT), 4(1), 16–41. https://doi.org/10.63397/ISCSITR-IJSRIT_04_01_002

[12] Yousefpour, A., Ishigaki, G., & Gour, R. (2022). A taxonomy of edge computing services. Future Generation Computer Systems, 114, 458–475. https://doi.org/10.1016/j.future.2020.08.002

[13] Vallemoni, R.K. (2023). Data Lineage and Metadata in Payment Ecosystems: Auditability and Regulatory Readiness across the Life Cycle. Frontiers in Computer Science and Artificial Intelligence, 2(1), 46–58. https://doi.org/10.32996/fcsai.2023.2.1.5

[14] Zhang, Y., Wang, X., & Liu, H. (2020). Adaptive energy-aware task offloading in IoT-fog-cloud networks. IEEE Internet of Things Journal, 7(3), 2345-2356. https://doi.org/10.1109/JIOT.2019.2956478