Analytics for the Internet of Things: A Survey

TL;DR

This survey reviews how data analytics is applied within the Internet of Things, highlighting techniques, infrastructure, and future research directions for creating effective IoT applications.

Contribution

It provides a comprehensive categorization of IoT analytics approaches, a layered taxonomy from data to insights, and insights into enabling technologies and future challenges.

Findings

Categorization of analytic approaches for IoT data

Layered taxonomy from data collection to analytics

Identification of tradeoffs and future research directions

Abstract

The Internet of Things (IoT) envisions a world-wide, interconnected network of smart physical entities. These physical entities generate a large amount of data in operation and as the IoT gains momentum in terms of deployment, the combined scale of those data seems destined to continue to grow. Increasingly, applications for the IoT involve analytics. Data analytics is the process of deriving knowledge from data, generating value like actionable insights from them. This article reviews work in the IoT and big data analytics from the perspective of their utility in creating efficient, effective and innovative applications and services for a wide spectrum of domains. We review the broad vision for the IoT as it is shaped in various communities, examine the application of data analytics across IoT domains, provide a categorisation of analytic approaches and propose a layered taxonomy from IoT data to analytics. This taxonomy provides us with insights on the appropriateness of analytical techniques, which in turn shapes a survey of enabling technology and infrastructure for IoT analytics. Finally, we look at some tradeoffs for analytics in the IoT that can shape future research.