Abstract
In the parallel computing task scheduling of wireless sensor networks, the resources of the edge server itself are limited, and the scheduling speed is slow. To solve this problem, a task scheduling method for wireless sensor multimedia big data parallel computing based on bee colony algorithm is designed. Design GSM wireless signal processing multi-core scheme and implement wireless signal processing. Message passing model is used as parallel programming model in scheduling. Improved the bee colony algorithm, designed the artificial bee colony algorithm, and realized the task scheduling of multimedia big data parallel computing based on the artificial bee colony algorithm. Test the scheduling performance of the design method. The test results show that the scheduling speed of the design method is fast, and the overall resource utilization is higher than 0.91.
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
Zhao, N.: Cloud computing platform non-recurring task parallel scheduling simulation. Comput. Simul. 38(1), 5 (2021)
Qi, Q., Zhang, L., Wang, J., et al.: Scalable parallel task scheduling for autonomous driving using multi-task deep reinforcement learning. IEEE Trans. Veh. Technol. 69(11), 13861–13874 (2020)
Saleem, U., Liu, Y., Jangsher, S., et al.: Mobility-aware joint task scheduling and resource allocation for cooperative mobile edge computing. IEEE Trans. Wireless Commun. (99), 1 (2020)
Zhou, C., Wu, W., He, H., et al.: Deep reinforcement learning for delay-oriented IOT task scheduling in space-air-ground integrated network. IEEE Trans. Wirel. Commun. (99) 1 (2020)
Liu, S., He, T., Dai, J.: A survey of CRF algorithm based knowledge extraction of elementary mathematics in Chinese. Mob. Netw. Appl. (2021)
Nie, L., Wang, X., Sun, W., et al.: Imitation-learning-enabled vehicular edge computing: toward online task scheduling. IEEE Netw. 35(3), 102–108 (2021)
Al-Habob, A.A., Dobre, O.A., Armada, A.G., Muhaidat, S., et al.: Task scheduling for mobile edge computing using genetic algorithm and conflict graphs. IEEE Trans. Veh. Technol. (99), 1 (2020)
Huang, X., Yu, R., Ye, D., et al.: Efficient workload allocation and user-centric utility maximization for task scheduling in collaborative vehicular edge computing. IEEE Trans. Veh. Technol. (99), 1 (2021)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
About this paper
Cite this paper
Lin, T., Zhong, F. (2024). Task Scheduling Method of Wireless Sensor Multimedia Big Data Parallel Computing Based on Bee Colony Algorithm. In: Wang, B., Hu, Z., Jiang, X., Zhang, YD. (eds) Multimedia Technology and Enhanced Learning. ICMTEL 2023. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 534. Springer, Cham. https://doi.org/10.1007/978-3-031-50577-5_29
Download citation
DOI: https://doi.org/10.1007/978-3-031-50577-5_29
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-50576-8
Online ISBN: 978-3-031-50577-5
eBook Packages: Computer ScienceComputer Science (R0)