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Private and Open Data in Asia: A Regional Guide.
The Internet of Things (IoT) envisions the enablement of connectivity, any time, any place, with anything and anyone – ideally using any path and any service1. Business entities foresee huge IoT economic opportunities across an incredibly broad range of applications, including smart cities, healthcare, retail, home automation, agriculture, industrial control, logistics, and many more.
A 2015 McKinsey report estimates that there will be 25-50 billion IoT devices deployed by 2020, and more than US$40 trillion of new investment in smart city infrastructure by 2035 alone.
Gartner called IoT the most hyped emerging technology of 2014, and again in 2015. But after hype typically comes disillusionment. Considering the majority of today’s IoT solutions and lack of IoT standards, promise and reality are still far apart. This view is also supported by the aforementioned 2015 McKinsey report, which details that most IoT-generated data are often not used or are heavily underused. One example they cite is an oil rig that deploys 30,000 sensors, but only one percent of the collected data are examined. The information used is mainly for detecting and controlling anomalies – not for optimization and prediction, which offer the greatest value.
In this talk, we will first take a look at current IoT standards, solutions, and common challenges that are yet to be adequately addressed. The key ingredient in the success of many IoT applications is context-aware computing. Context-aware computing uses situational and environmental information about people, places, and things to anticipate immediate needs and offer enriched, situation-aware, and usable content, functions, and experiences.2
We’ll also discuss next-generation platforms designed from the ground-up for contextual computing, and outline several concrete, platform-based use cases that enable a wide range of IoT applications.
1 Loosely based on Guillemi et.al.’s 2009 definition of The Internet of Things.
2 Context-aware computing definition by Gartner IT Glossary.
Dr. Markus Kirchberg serves as the Head of Technology Innovation and has responsibility for driving and delivering technology innovation across the Asia Pacific region. Kirchberg has over 20 years experience in research and technology-driven innovation. His career spans across academia, dedicated research centers as well as industrial research and incubation labs.
Prior to joining Deep Labs, Markus headed Visa Labs, Asia Pacific with responsibility for technology innovation and incubation across the Asia Pacific region. Prior to Visa, Kirchberg held the role of Expert at HP Labs Singapore, where he led various innovation initiatives on next generation, cross-domain data analytics platforms; Research Fellow and Principle Investigator at the Institute for Infocomm Research, A*STAR; and Lecturer at Massey University, New Zealand.
Kirchberg’s skill set includes full innovation lifecycle management (ideation — research — incubation — production), automating infrastructure, cloud computing, data management at multi-petabyte scale, data privacy, emerging technologies, Internet-of-Things, large-scale data analytics and extreme transaction processing. Kirchberg has extensive experience across healthcare, logistics, payments analytics & processing and risk management (compromise / fraud detection / prediction).
Kirchberg currently holds an Adjunct Associate Professor position with the Department of Computer Science at the National University of Singapore (NUS) where he teaches Master degree courses focusing on Applied Data Analytics.
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