The State of Art and Future Development of Internet of Things Warehousing Technology
DOI:
https://doi.org/10.54097/hset.v32i.5183Keywords:
IoT warehousing technology, logistics information transfer, Edge computing.Abstract
The Internet of Things (IoT) has received a lot of attention from the government, academia, and enterprise enterprises for its amazing software prospects, and the IoT warehousing technology has received vast attention. In this paper, primarily based on the study of the concept, workflow, and system components of the IoT storage system, we propose how to optimize the present IoT storage system scheme. The evaluation focuses on two aspects: the records transmission science of the system, and the enchantment area of the system algorithm. This paper gives the improvement technique of logistics information transfer in the common warehouse management process and additionally proposes the use of edge computing most desirable for optimization. This system has the advantages of being fast, convenient, accurate, efficient, and rather automatic in access, monitoring, inventory, and picking.
Downloads
References
International Telecommunications Union, "The Internet of Things," ITU News, 13-16 (2005).
Gustavo, R., Mario, M., Carlos, D., "Early in-frastructure of an Internet of Things in Spaces for Learning," Eighth IEEE International Conference on Advanced Learning Technologies, 381-383 (2008).
Amardeo, C., Sarma. and Girão., et al., "Identities in the Future Internet of Things," Wireless Pers Common 2009 (49), 353-363 (2008).
Huang, Z., Ji, Q., Chen, D., "Intelligent logistics warehousing system research of Internet of things," Automation instrument 32(03), 12-15 (2011).
Pan S., "IoT technology application in the future of logistics," Journal of transportation in China 2022(07), 125-126 (2022).
Barbera, M. V., Kosta, S. and Mei, A., et al., "To offload or not to offload? The bandwidth and energy costs of mobile cloud computing," Infocom, IEEE, (2013).
Chen, M., Hao, Y. and Hu, L., et al., "Edge-CoCaCo: Toward Joint Optimization of Computation, Caching, and Communication on Edge Cloud," IEEE Wireless Communications 25(3), 21-27 (2018).
Zhou, L., Dan, W. and Chen, J., et al., "Greening the Smart Cities: Energy-Efficient Massive Content Delivery via D2D Communications," IEEE Transactions on Industrial Informatics, 1-1 (2018).
Hao, Y., Miao, Y. and Hu, L., et al., "Smart-Edge-CoCaCo: AI-Enabled Smart Edge with Joint Computation, Caching, and Communication in Heterogeneous IoT," Network, IEEE, 58-64 (2019).
Liao, H., Jia, Z. and Wang, R., et al., "Adaptive Learning-Based Delay-Sensitive and Secure Edge-End Collaboration for Multi-Mode Low-Carbon Power IoT," China Communications 19(7), 324-336 (2022).
Hu, L., Miao, Y. and Wu, G., et al., "iRobot-Factory: An intelligent robot factory based on cognitive manufacturing and edge computing," Future Generation Computer Systems 90(10), (2018).
Do, Q. V., Hoan, T., and I. Koo., "Optimal Power Allocation for Energy-efficient Data Transmission Against Full-duplex Active Eavesdroppers in Wireless Sensor Networks," IEEE Sensors Journal, 1-1 (2019).
Liang, X., Member, S., IEEE, and Xie, C., et al., "User-Centric View of Unmanned Aerial Vehicle Transmission Against Smart Attacks," IEEE Transactions on Vehicular Technology, 1-1 (2017).
Downloads
Published
Issue
Section
License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.







