A hybrid crowdsensing approach with cloud-edge computing framework for design innovation in smart product-service systems

• Main Authors: Zheng, Pai, Chen, Chun-Hsien
• Other Authors: School of Electrical and Electronic Engineering
• Format: Conference or Workshop Item
• Language: English
• Published: 2021
• Subjects: Engineering::Manufacturing; Cloud Computing; Hybrid Crowdsensing
• Online Access: https://hdl.handle.net/10356/146878
• Institution: Nanyang Technological University

Smart product-service systems (Smart PSS) is an emerging IT-driven value proposition paradigm to depict today's companies' emphasis towards servitization. In technical aspect, the rapid development of Information and Communications Technology (ICT) and Artificial Intelligence (AI) techniques trigger the introduction of a tremendous number of smart, connected products (SCPs). They serve as the media and tools for various e-services generation in a smart environment. Meanwhile, in business aspect, it has a vision of shared value as an ecosystem with sustainability concerns. Despite few existing works discussing about its concepts, scarcely any report has been given on an effective IT infrastructure or design innovation mechanism to realize such paradigm. Aiming to fill this gap, this work proposes a hybrid crowdsensing approach with a four-layered cloud-edge computing framework for design innovation in the Smart PSS context. By leveraging the cloud-edge computing technique, data stream can be processed locally for value delivery at the edge nodes with less latency. Meanwhile, big data analytics with less temporal urgency from multiple edge nodes can be conducted in the cloud layer instead. Furthermore, due to the unique characteristics of Smart PSS, both human intelligence from online data sources (e.g. crowdsourcing) and machine intelligence from offline data sources (e.g. built-in-sensors) should be considered integrally for its design innovation. Hence, a hybrid crowd sensing approach combining both mobile crowdsensing (MCS) and static sensing mechanisms is further introduced. It is hoped this research can offer manufacturers/service providers with useful insights and better understandings in their value creation process.