The Internet of Things: A Survey from the Data-Centric Perspective

By Charu C. Aggarwal, Naveen Ashish, Amit P. Sheth. Managing and Mining Sensor Data, 2013

332 citations recorded by the source corpus.

Advances in sensor data collection technology, such as pervasive and embedded devices, and RFID Technology have lead to a large number of smart devices which are connected to the net and continuously transmit their data over time. It has been estimated that the number of internet connected devices has overtaken the number of humans on the planet, since 2008. The collection and processing of such data leads to unprecedented challenges in mining and processing such data. Such data needs to be processed in real-time and the processing may be highly distributed in nature. Even in cases, where the data is stored offline, the size of the data is often so large and distributed, that it requires the use of big data analytical tools for processing. In addition, such data is often sensitive, and brings a number of privacy challenges associated

Subfields: Artificial Intelligence, Computer Networks and Communications, Computer Science Applications

View Paper: https://www.semanticscholar.org/paper/5c2e5464f280ef0fc289b3fddcd2ff431f3040c6

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Citations in the corpus

Detecting concept drift in data streams using model explanation (2018, background): “They are becoming ever-more prevalent as a consequence of the exponentially increasing number of automatic data feeds, sensory networks and Internet of Things (Aggarwal et al., 2013).”
A survey of Internet-of-Things: Future vision, architecture, challenges and services (2014, background): “Things having identities and virtual personalities operating in smart spaces using intelligent interfaces to connect and communicate within social, environmental, and user contexts [1-3].”
Software-Defined Networking for Internet of Things: A Survey (2017, background): “Huge number of data centers will be involved in processing the huge volume of data collected from billions of devices in IoT [31], [32].”
A roadmap for security challenges in the Internet of Things (2017, methodology): “In “ The Internet of Things: A Survey from the Data-Centric Perspectiv ” [8], the authors detailed data mining and management in the IoT according to the same visions as [6].”
The Internet of Things—A survey of topics and trends (2014, background): “In terms of application areas, papers focusing on supply chains and social applications received somewhat greater treatment than the other application areas, perhaps because of the established role of IoT technologies such as RFID in supply chain management and the cultural focus on social Table 3 C”
A Survey on Network Methodologies for Real-Time Analytics of Massive IoT Data and Open Research Issues (2017, unknown): “upon it [45].”
Data Mining for Internet of Things: A Survey (2014, background): “This is where knowledge discovery in databases (KDD) and data mining technologies come into play, for these technologies provide possible solutions to find out the information hidden in the data of IoT, which can be used to enhance the performance of the system or to improve the quality of services…”