Open Access Open Access  Restricted Access Subscription Access

Efficient IoT Deployment: Reducing Bandwidth Costs via Edge-Cloud Collaboration

Wobiageri Ndidi Abidde, Ibanibo Tamunotonye Sotonye

Abstract


The rapid proliferation of Internet of Things (IoT) devices has introduced significant challenges in managing data transmission, bandwidth usage, and latency. This paper explores an efficient IoT deployment framework that leverages the synergistic collaboration between edge computing and cloud resources to optimize Quality of Service (QoS), with a primary focus on reducing network load and bandwidth costs. The proposed method employs a hierarchical processing model where preliminary data filtering, aggregation, and analytics are performed at the edge nodes, significantly minimizing the volume of data transmitted to the cloud. A dynamic workload orchestration algorithm is introduced, which intelligently allocates tasks between the edge and cloud based on real-time bandwidth availability, processing capacity, and data criticality. Additionally, lightweight machine learning models deployed at the edge enable predictive analytics and anomaly detection, further reducing unnecessary data transfers. Simulation and prototype testing were conducted using a smart city traffic monitoring scenario with over 5,000 simulated IoT sensors. The results demonstrated a reduction in data transmission volume by up to 68%, bandwidth cost savings of approximately 55%, and a 30% improvement in response time for time-sensitive events. These outcomes underscore the effectiveness of edge-cloud collaboration in enabling scalable, cost-efficient IoT deployments, especially in bandwidth-constrained environments.


Full Text:

PDF

References


Statista. (2023). Internet of Things (IoT) connected devices installed base worldwide from 2015 to 2030. Retrieved from https://www.statista.com/statistics/471264/iot-number-of-connected-devices-worldwide/

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

Botta, A., De Donato, W., Persico, V., & Pescapé, A. (2016). Integration of cloud computing and internet of things: A survey. Future Generation Computer Systems, 56, 684–700. https://doi.org/10.1016/j.future.2015.09.021

Satyanarayanan, M., Simoens, P., Xiao, Y., Pillai, P., Chen, Z., & Ha, K. (2017). Edge analytics in the Internet of Things. IEEE Pervasive Computing, 16(2), 24–31. https://doi.org/10.1109/MPRV.2017.29

Liu, J., Mi Y., Zhang X., Li X. (2024).Task graph offloading via deep reinforcement learning in mobile edge computing. Future Generation Computer Systems. (158) 545-555 https://doi.org/10.1016/j.future.2024.04.034

Hu, P., Dhelim, S., Ning, H., & Qiu, T. (2020). Survey on fog computing: Architecture, key technologies, applications and open issues. Journal of Network and Computer Applications, 98, 27–42. https://doi.org/10.1016/j.jnca.2020.102870

Al-Turjman, F., & Abujubbeh, M. (2019). IoT-enabled smart grid via SM: An overview. Future Generation Computer Systems, (96) 579-590 https://doi.org/10.1016/j.future.2019.02.012

Zhou, F., Lin, X., & Xu, J. (2023). Intelligent edge computing for IoT systems: A survey. ACM Computing Surveys, 56(3), 1–38. https://doi.org/10.1145/3587200

Letaief, K.B., Shi Y., Lu J. Lu, J. (2021) Edge Artificial Intelligence for 6G: Vision, Enabling Technologies, and Applications, IEEE Journal on Selected Areas in Communications. DOI: 10.1109/JSAC.2021.3126076


Refbacks

  • There are currently no refbacks.