Abstract
With the integration and development of the Internet of Things (IoT), the service model of IoT has gradually evolved from traditional “data collection and business processing” to “providing ubiquitous and universal services through collaboration between cloud, edge, and end resources”. However, traditional methods rely on centralized servers, which pose challenges in terms of trust, cost, and single points of failure. To overcome these challenges, we propose a distributed intelligent collaborative scheduling mechanism. First, we establish a distributed collaborative environment based on blockchain and construct a resource scheduling model. Second, we propose an intelligent collaborative mechanism for workload balancing based on reinforcement learning. This mechanism ensures the efficient allocation of resources across the network. Lastly, we design simulation experiments to evaluate the effectiveness of the proposed mechanism, and the results demonstrate its efficiency.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Al-Fuqaha, A., Guizani, M., Mohammadi, M., et al.: Internet of things: a survey on enabling technologies, protocols, and applications. IEEE Commun. Surv. Tutorials 17(4), 2347–2376 (2015)
Rui, J., Sun, D.: Architecture design of the internet of things based on cloud computing. In: Seventh International Conference on Measuring Technology & Mechatronics Automation. IEEE (2015)
Singh, J., Pasquier, T., Bacon, J., et al.: Twenty security considerations for cloud-supported internet of things. IEEE Internet Things J. 3(3), 269–284 (2017)
Yi, X., Helal, A.: Scalable cloud-sensor architecture for the internet of things. IEEE Internet Things J. 3(3), 285–298 (2016)
Lin, J., Yu, W., Zhang, N., et al.: A survey on internet of things: architecture, enabling technologies, security and privacy, and applications. IEEE Internet Things J. 4(5), 1125–1142 (2017)
Nguyen, D.C., Pathirana, P.N., Ding, M., et al.: Integration of blockchain and cloud of things: architecture, applications and challenges. IEEE Commun. Surv. Tutorials 22(4), 1 (2020)
Dai, H.N., Zheng, Z., Zhang, Y.: Blockchain for internet of things: a survey. IEEE Internet Things J. 6(5), 8076–8094 (2019)
Jiang, H.B., Li, J., Zhao, P., et al.: Location privacy-preserving mechanisms in location-based services: a comprehensive survey. ACM Comput. Surv. 54(1), 1–36 (2021)
Nakamoto, S.: Bitcoin: A peer-to-peer electronic cash system (2008). https://bitcoin.org/bitcoin.pdf
Huang, H., Xue, Y., Wu, J., Tao, Y., Hu, M.: Temporal computing resource allocation scheme with end device assistance. IEEE Internet Things J. 9(18), 16884–16896 (2022)
Wu, H., Zhang, Z., Guan, C., et al.: Collaborate edge and cloud computing with distributed deep learning for smart city internet of things. IEEE Internet Things J. 7(9), 8099–8110 (2019)
Liu, C.H., Chen, Z., Tang, J., et al.: Energy-efficient UAV control for effective and fair communication coverage: a deep reinforcement learning approach. IEEE J. Sel. Areas Commun. 36(9), 2059–2070 (2018)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Wang, L., He, W., liu, R., Liu, F. (2024). Distributed Intelligent Collaborative Scheduling Mechanism for Cloud-Edge-End Resources in IoT. In: Jin, H., Pan, Y., Lu, J. (eds) Computer Networks and IoT. IAIC 2023. Communications in Computer and Information Science, vol 2060. Springer, Singapore. https://doi.org/10.1007/978-981-97-1332-5_5
Download citation
DOI: https://doi.org/10.1007/978-981-97-1332-5_5
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-97-1331-8
Online ISBN: 978-981-97-1332-5
eBook Packages: Computer ScienceComputer Science (R0)