When Things Matter: A Data-Centric View of the Internet of Things

TL;DR

This paper surveys data-centric techniques for managing the large-scale, noisy, and continuous data generated by IoT devices, highlighting current methods and open challenges in the field.

Contribution

It provides a comprehensive overview of state-of-the-art data management techniques for IoT, emphasizing data stream processing, storage models, and event processing, and discusses open research issues.

Findings

Overview of data stream processing techniques

Analysis of IoT data storage models

Identification of open research challenges

Abstract

With the recent advances in radio-frequency identification (RFID), low-cost wireless sensor devices, and Web technologies, the Internet of Things (IoT) approach has gained momentum in connecting everyday objects to the Internet and facilitating machine-to-human and machine-to-machine communication with the physical world. While IoT offers the capability to connect and integrate both digital and physical entities, enabling a whole new class of applications and services, several significant challenges need to be addressed before these applications and services can be fully realized. A fundamental challenge centers around managing IoT data, typically produced in dynamic and volatile environments, which is not only extremely large in scale and volume, but also noisy, and continuous. This article surveys the main techniques and state-of-the-art research efforts in IoT from data-centric perspectives, including data stream processing, data storage models, complex event processing, and searching in IoT. Open research issues for IoT data management are also discussed.