Closed-Loop Lifecycle Management of Service and Product in the Internet of Things: Semantic Framework for Knowledge Integration

Abstract

This paper describes our conceptual framework of closed-loop lifecycle information sharing for product-service in the Internet of Things (IoT). The framework is based on the ontology model of product-service and a type of IoT message standard, Open Messaging Interface (O-MI) and Open Data Format (O-DF), which ensures data communication.(1) Background: Based on an existing product lifecycle management (PLM) methodology, we enhanced the ontology model for the purpose of integrating efficiently the product-service ontology model that was newly developed; (2) Methods: The IoT message transfer layer is vertically integrated into a semantic knowledge framework inside which a Semantic Info-Node Agent (SINA) uses the message format as a common protocol of product-service lifecycle data transfer; (3) Results: The product-service ontology model facilitates information retrieval and knowledge extraction during the product lifecycle, while making more information available for the sake of service business creation. The vertical integration of IoT message transfer, encompassing all semantic layers, helps achieve a more flexible and modular approach to knowledge sharing in an IoT environment; (4) Contribution: A semantic data annotation applied to IoT can contribute to enhancing collected data types, which entails a richer knowledge extraction. The ontology-based PLM model enables as well the horizontal integration of heterogeneous PLM data while breaking traditional vertical information silos; (5) Conclusion: The framework was applied to a fictive case study with an electric car service for the purpose of demonstration. For the purpose of demonstrating the feasibility of the approach, the semantic model is implemented in Sesame APIs, which play the role of an Internet-connected Resource Description Framework (RDF) database.

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“…The specification and use of standard models and interfaces ease the data integration processes, and therefore, efforts in that direction have been done in the environmental domain, with the O&M data model [11], the SensorML language [72] and the transactional part of the sensor observation service (SOS) [73], among others. The same problem arises also in IoT [74] and Smart City [75] applications. Therefore, similar efforts have been undertaken in these areas to reach standard interfaces and models.…”

“…The semantic integration of data sources has already been identified as a challenging key functionality to be supported in advanced data infrastructures [82], including those in the scope of environmental data [83], and those in the scope of IoT [74]. To achieve this, three main tasks have to be undertaken.…”

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“…Servitisation and innovation are powerful tools to change the market and to quickly respond customer needs [4]. Many researchers have attempted to improve business efficiency and to create innovation [6][7], by fulfilling customer needs [8][9][10][11] in the entire lifecycle by applying lifecycle management namely; Product Lifecycle Management (PLM), Service Lifecycle Management (SLM) and Product Service System (PSS). Creating strategies to offer customers a fast delivery with appropriate costs and flexible routes are key factors to compete in the global market [12].…”

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