Cognitive Internet of Things: A New Paradigm Beyond Connection

By Qihui Wu, Guoru Ding, Yuhua Xu, Shuo Feng, Zhiyong Du, Jinlong Wang, Keping Long. IEEE Internet of Things Journal, 2014

523 citations recorded by the source corpus.

Current research on Internet of Things (IoT) mainly focuses on how to enable general objects to see, hear, and smell the physical world for themselves, and make them connected to share the observations. In this paper, we argue that only connected is not enough, beyond that, general objects should have the capability to learn, think, and understand both physical and social worlds by themselves. This practical need impels us to develop a new paradigm, named cognitive Internet of Things (CIoT), to empower the current IoT with a “brain” for high-level intelligence. Specifically, we first present a comprehensive definition for CIoT, primarily inspired by the effectiveness of human cognition. Then, we propose an operational framework of CIoT, which mainly characterizes the interactions among five fundamental cognitive tasks: perception-action cycle, massive data analytics, semantic derivation and knowledge discovery, intelligent decision-making, and on-demand service provisioning. Furthermore, we provide a systematic tutorial on key enabling techniques involved in the cognitive tasks. In addition, we also discuss the design of proper performance metrics on evaluating the enabling techniques. Last but not the least, we present the research challenges and open issues ahead. Building on the present work and potentially fruitful future studies, CIoT has the capability to bridge the physical world (with objects, resources, etc.) and the social world (with human demand, social behavior, etc.), and enhance smart resource allocation, automatic network operation, and intelligent service provisioning.

Subfields: Artificial Intelligence, Computer Networks and Communications, Control and Systems Engineering

View Paper: https://www.semanticscholar.org/paper/e90c7e2972170b8ac031b31568aa53fd23965963

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Citations in the corpus

A Review of Machine Learning and IoT in Smart Transportation (2019, background): “This need led to the creation of the term “Cognitive IoT” (CIoT) [10].”
The smart circular economy: A digital-enabled circular strategies framework for manufacturing companies (2020, background): “Fifteen papers mentioned one or more workflows synonymous with data collection, data integration, data storage, data processing, and data analysis (Addo-Tenkorang & Helo, 2016; Babar & Arif, 2017 Siow et al., 2018; Tsai et al., 2014; ur Rehman et al., 2018; Wu et al., 2014).”
A Survey on Network Methodologies for Real-Time Analytics of Massive IoT Data and Open Research Issues (2017, background): “Similarly, we can find the benefits of integration of radio technologies with IoT in these papers [35], [177]–[180].”
An Amateur Drone Surveillance System Based on the Cognitive Internet of Things (2017, background): “, brain-empowered IoT or cognitive IoT as follows [9]: “Cognitive IoT is a new network paradigm, where (physical/virtual) things or objects are interconnected and behave as agents, with minimum human intervention, the things interact with each other following a context-aware perception-action cycle,”
A Survey of Machine and Deep Learning Methods for Internet of Things (IoT) Security (2018, result): “According to two previous studies [239], [240], IoT systems cannot be sustained without comprehensive cognitive capability because of growing issues.”
End-to-End Learning From Spectrum Data: A Deep Learning Approach for Wireless Signal Identification in Spectrum Monitoring Applications (2017, methodology): “To address these challenges, recent research work proposed a CR-based IoT [21], [22] to enable dynamic spectrum sharing among heterogeneous wireless networks.”