1.1 Internet of Things
Over the last couple of years, the Internet of Things (IoT) has caught the imagination. From smart meters that allow you to monitor your energy consumption to tags that parents can use to monitor the movement of their children and wearables that count your daily steps, the IoT has begun to be widely adopted by individuals. Multiple factors have encouraged the of the IoT by individuals, with some appreciating its ability to save them money while others liking the peace of mind that comes from being able to remotely monitor their child, house or pet.
But what is the IoT? While the genesis of the idea has been variously attributed (Ardito et al. 2018; Wang and Hsieh 2018), it is widely credited to Kevin Ashton in the late-1990s (Lee et al. 2017; Tang et al. 2018). Numerous definitions have subsequently been proposed (Martínez-Caro et al. 2018), and while these vary in their phrasing and exact composition, there is a degree of commonality to them. Technology is embedded in a device, which is connected to a network over which it sends data. This connection is typically, but not exclusively, wireless, of which several technologies exist (OECD 2015).
Through falling costs and technological advances, it is expected that globally the number of IoT connections will be significant, especially when compared to the number of existing mobile subscribers or Internet users. For example:
- Analysys Mason (2020)—5.3 billion by 2028
- Ericsson (2019)—24.9 billion connections by 2025
- Strategy Analytics—38.6 billion by 2025 (Business Wire 2019)
- IDC (2019)—41.4 billion by 2025
- OECD (2015)—50 billion by 2020
- Huawei (2018)—100 billion by 2025
- Cisco (2016)—500 billion connections by 2030
These connections will, in turn, generate revenues. Fortune Business Insight (2019) forecasts global IoT revenues of $1102 billion by 2013, while Global Data (2019) calculated that the IoT will generate revenues of $96 billion in the Asia-Pacific region alone by 2023. Global Data (2019) interestingly divides the forecast revenue into three sources: software and services, devices and connectivity with the first being considerably larger than the other two.
This is not to suggest that the selling of devices will be a small market. Gartner (2019), focusing solely on the enterprise and automotive markets, forecasts revenues of $389 billion in 2020 from what it describes as ‘endpoint’ electronics. More broadly, the wider economic gains associated with the IoT, due to cost reduction, remote monitoring etc., have been forecast to be substantial. The forecasts noted by OECD (2016) range from an estimated gain of $10–15 trillion for global GDP over the next 20 years to $1 trillion from the widespread adoption of smart meters alone. McKinsey Global Institute (2015) draws attention to the sectoral differences that occur regarding the potential value that could be created by the IoT by 2025. The application of IoT within the home environment could generate $350 billion in value, whereas the potential value from its application in the retail sector was forecast to be up to $1.2 trillion. The application of IoT to vehicles could generate $740 billion in value (McKinsey Global Institute 2015).
It is, however, worth offering a word of caution. Strategy Analytics, cited by Business Wire (2019), state that many companies are assuming that revenues will automatically flow from the devices being installed but this may not be the case. In other words, there is a need to develop business models that monetise the IoT devices that are being installed.
IoT connections will generate vast amounts of data. IDC (2019), for example, forecast that by 2025 IoT will generate just under 80 zettabytes of data. This presents both an opportunity as well as a challenge. Through the analysis of this data, detailed insights will emerge—these insights are wide-ranging, occurring wherever IoT is adopted and reflect the granularity of the data that is generated and collected. Moreover, these insights will occur at different levels of aggregation—individuals, households, city etc.—but can also be constantly updated to reflect the real-time collection and analysis of IoT generated data.
But there are also challenges with analysing and then utilising such large amounts of data. Not only must techniques be developed that can handle the large volume of data associated with the IoT, but these techniques must also be able to accommodate its rapid accumulation as well as be able to identify relevant insights that allow a company, doctor etc. to take whatever appropriate action is necessary (Deetjan et al. 2015; OECD 2013). The ability of these techniques to generate insights, and thus value, is also shaped by context (Greengard 2015). Context means that the device is able to unders...