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Multimedia and multimodal sensing with edge computing for personalized healthcare supply chain system data optimization

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Abstract

Multimedia and multimodal sensing with the edge computing for the personalized healthcare supply chain system data optimization is studied in this manuscript. In the agile supply chain management information system, the cross-platform and heterogeneity of data exchange and business logic invocation is inevitable. For the efficient analysis, our application scenarios will be focused on the personalized healthcare supply chain system. For the theoretical basis novelty, we consider the two core aspects. (1) For the Multimedia System and Multimodal Sensing, they will undertake the role of interface optimization and the comprehensive data optimization and (2) for the Edge Computing and the Data Optimization. The data center will become an important foundation for the hospital to then achieve digital transformation. For the designed system, we use the Unique Device Identification (UDI) for the product tagging, the blockchain for the safety guarantee, and the Supply-Processing-Distribution (SPD) based model are considered. For the optimization of the performance, the Gale-Shapley model considering the complex network is designed. Through the comparison experiment, the proposed model is tested under different environment. It has been reflected that the proposed model is efficient and accurate. For testing the robustness of the model, the dynamic simulation is added.

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Funding

Henan soft science research accounting special project in 2022, “Carbon Audit Research on Regional Energy Economic Structure Transformation from the Perspective of ‘Double Carbon’”, Grant/Award Number: N0.222400410518.

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Correspondence to Xishuan Zhang.

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Liu, Z., Zhang, X. Multimedia and multimodal sensing with edge computing for personalized healthcare supply chain system data optimization. Pers Ubiquit Comput 27, 955–972 (2023). https://doi.org/10.1007/s00779-022-01679-9

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