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With the development of the Internet of Things (IoT), massive numbers of IoT devices (smart sensors, cameras, phones, and so on) have been deployed and utilized in various environments, supporting numerous smart-world applications. Those devices communicate with computing infrastructure servers over network infrastructures to send relevant collected data in order to link physical objects to the cyber world. As the number of IoT devices increases rapidly, the volume of collected data likewise increases prodigiously. Thus, how to search for and through specific IoT datasets among the enormous amount of data become critical issues for potential data consumers. Moreover, various IoT devices and applications establish IoT-based systems, also known as the smart-world systems or smart cyber–physical systems (CPS), such as smart grids, smart transportation, smart healthcare, smart cities, smart homes, and smart manufacturing systems, among others. However, the individual CPS are independently designed and deployed, such that they collect and analyze data independently, with no information sharing or interconnection, raising serious challenges in searching for valuable information. Thus, in order to efficiently and precisely utilize the IoT datasets, suitable search techniques designed for the IoT environments are fundamental. In this paper, we first summarize popular web search techniques and survey existing research on the search and analysis related to the IoT. We then outline the opportunities and challenges of the IoT search techniques. Furthermore, we propose a problem space for the IoT search techniques and provide a clear view of potential future research directions.

Search Engine for the Internet of Things: Lessons From Web Search, Vision, and Opportunities