Breaching Boundaries and Building Frameworks - Tapping into Future IoT Industry Growth with oneM2M
In the earliest days of the Internet of Things (IoT), when the market first began to take shape, solution providers centered their focus on connectivity. This – the intricacies involved in connecting billions of devices and assets to the Internet – was just the beginning.
In 2015, a macro-assessment of IoT carried out by worldwide management consultancy, McKinsey, quantified the market opportunity in nine categories, ranging from home automation, factory, personal monitoring (health and fitness), smart city applications and more. Within these verticals and domains, there can be hundreds, or thousands, of individual applications and so there is vast potential for technology providers and connectivity service businesses to target possible new connected devices.
Fast-forward to today, however, and the impetus is shifting. Increasingly, the primary objective for the industry is now data. Specifically, how to understand, interpret and handle the data that comes from connected assets. Operators, developers, and service providers are aiming to find ways to connect, manage, analyze and, eventually, share data among different organizations. Which will, in turn, will break down boundaries and maximize potential industry growth.
The Value of Data Models
The heterogeneous nature of IoT introduces another layer of complexity, in addition to the issue of large scale and distributed data management. Making data interoperable - considering the different protocols, architecture, and standards used across this ‘system of systems’ - between partners in a supply chain, across application silos or between vendors of interchangeable devices and sensors is a challenge.
A solid framework for a good data model can solve these issues. In other words, IoT data modeling offers an approach which could more efficiently describe, interpret, analyze and share data among heterogeneous IoT applications and devices.
Fortunately, several data models already exist. Many of them have been developed by different Standards Development Organizations. Some of them are for specific IoT vertical applications or domains. For example, Smart Appliances REFerence (SAREF) provides a shared model for home appliances. Data models from the Open Geospatial Consortium (OGC) are more for geosciences and environment domain. The Open Connectivity Foundation (OCF) specifies data models based on vertical industries such as automotive, healthcare, industrial and the smart home. The World Wide Web Consortium (W3C) Thing Description (TD) provides some vocabularies to describe physical things but does not have much focus on data.
In contrast, Schema.org operates as a collaborative community and it aims to provide more general and broad vocabularies and schemas for structured data on the Internet. A collaborative approach to integrate and unify various data models is critical and necessary to work across IoT application and organizational boundaries. It requires cooperative efforts among different industry bodies.
oneM2M’s Role in Data Model Standardization
oneM2M, the global standardization body, was established to create a body of maximally reusable standards to enable IoT applications across different verticals. oneM2M focuses on common services, such as semantics, device management, and security, that are needed in all IoT applications. The oneM2M standard takes the role of a horizontal IoT service layer between applications and the underlying connectivity networks. In doing so, it masks technology complexity for application developers and hardware providers.
In other words, oneM2M supports the capability to interwork to various local/proximal networks. Each of these proximal networks tends to use their own data model. As part of the interworking framework of oneM2M, it provides a layer of abstraction by interworking these data models together with one another via the oneM2M resource and data model. As shown in Figure 1, applications thus can communicate using one data model (i.e. oneM2M resource and data model) and oneM2M handles the translations to the various proximal network data models - simplifying applications.
Figure 1: oneM2M provides interworking and mapping between oneM2M and various vertical data models.
Amongst its various standardization efforts, oneM2M takes a collaborative approach in developing its data model-related specifications. For example, one of its technical specifications, TS-0005 for management data model, is the result of a collaboration between oneM2M and the Open Mobile Alliance (OMA).
Another specification, TS-0023, laid the groundwork for a Smart Devices Template (SDT) that was first applied to create a Home Appliances Information Model and Mapping. In the next release of oneM2M, Release 4, the underlying data model principles will be extended to support information modeling and mapping for vertical industries, such as smart cities.
Tapping into the Potential for IoT Data Models
The impact of IoT is already far-reaching, but the full promise of it is yet to be realized. The renewed focus on frameworks to manage data models across application verticals and domains sets the stage for unlocking the next phase of the IoT industry’s growth.
Harnessing the array of IoT technologies and the data generated by them, recognizing the true value of data-driven analytics, continuing to make gains with standardization and improving cross-company collaboration, means we can create more space for increased innovation, maximal efficiency and to realize the full game-changing potential of the IoT, which is undoubtedly going to help shape our future.
Chonggang Wang (Chonggang.Wang@InterDigital.
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Calendar of Events
2019 IEEE International Conference on Internet of Things and Intelligence System (IoTaIS)
5-7 November 2019
"IoT and the mmWave Frontier", an IEEE IoT Vertical and Topical Summit, co-located with IEEE RWW2020
26-27 January 2020
San Antonio, TX, USA
IEEE 6th World Forum on Internet of Things (WF-IoT)
5-9 April 2020
New Orleans, LA, USA
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Special Issue on Internet of Things for Smart Ocean
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Special Issue on Software Engineering Research and Practices for the Internet of Things
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Special Issue on Deep Reinforcement Learning for Emerging IoT Systems
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