Knowledge Transfer and Crowdsourcing in Cyber-Physical-Social Systems

Abstract

• Presents a crowdsourcing model for cyber-physical-social systems • Highlights the issues of massive data generated in cyber-physical space • Discusses how knowledge transfer can be used to solve an issue effectively The rapid development of cyber-physical systems results in vast amount of heterogeneous data generated every day. To deal with unstructured data and maintain its security and privacy in smart manufacturing, it is necessary to merge social space with cyber-physical systems to develop cyber-physical-social systems. Crowdsourcing and knowledge transfer can be effective approaches to solve problems of manufacturing and product development, such as inviting hackers to break the security bridge to test the efficacy of the measures and handling enormous data generated in cyber-physical-social system. Crowdsourcing is a novel computing paradigm that leverages human effort to tackle computationally difficult issues, whereas knowledge transfer helps complete the new assignment based on quick access to the existing knowledge. As a result, an enhanced annotation for the work may be done at a low cost via suitable knowledge transfer. This paper introduces cyber-physical-social system and highlights the challenges and issues arising from massive data generated over the internet by various sources, and discusses how pattern recognition techniques can be used to identify anomalies or attacks. It also defines the terms knowledge transfer and crowdsourcing and explains how they can be effectively used to solve a problem in cyber-physical-social systems.