The Internet of Things promises to change everything, but with each passing day, there seems to be growing cynicism, even hostility, to widespread adoption. In this blog, Amit Sachdeva argues that businesses need to better demonstrate how IoT and affiliated technologies will transform the world for the better.\u00a0<\/strong><\/span><\/h2>\n\u00a0<\/p>\n
![\"\"](\"https:\/\/www.tatacommunications.com\/blog\/wp-content\/uploads\/2020\/03\/AdobeStock_207053065-300x200.jpeg\")
<\/a>For many businesses, successfully launching an Internet of Things (IoT) enabled service or application would seem like a reason to celebrate in itself. Yet, the real value of IoT comes from the data-led insights that can be derived from connected devices.<\/p>\nThe best way to manage and drive value from IoT data is with the help of artificial intelligence (AI), which is able to detect data patterns that might not be obvious to the human eye. Add in the strong degree of trust that distributed ledger technologies (DLT) like blockchain can bring to IoT \u2013 and you\u2019ve got a powerful combination of three technologies that could have a truly transformational impact on how businesses operate and interact with their customers, and how people engage with the world around them.<\/p>\n
The two ways to leverage IoT data <\/strong><\/p>\n![\"\"](\"https:\/\/www.tatacommunications.com\/blog\/wp-content\/uploads\/2020\/03\/AdobeStock_228665655-300x169.jpeg\")
<\/a>Imagine you\u2019re a business with a fleet of cars, trucks, airplanes or freight containers. Using the IoT, you\u2019re able to capture two types of information that can be collected from them: primary operational information \u2013 such as location, engine condition and temperature data \u2013 to support the day-to-day operations of your business; and aggregated big data \u2013 which you can use to analyse a much broader range of factors about your services and enhance them through R&D, for example.<\/p>\nOrganisations may already use some form of analytics to manage both types of information. However, the enhanced capability of AI platforms to capture patterns of behaviour in both people and \u2018things\u2019, combined with the strategic insights these platforms can offer, could take a business to the next step.<\/p>\n
\n\u201cIncreasingly, there is a need for IoT data to be shared across a wider IoT ecosystem too in order to maximise its benefits.\u201d<\/span><\/em><\/h3>\n<\/blockquote>\nThat\u2019s where DLTs such as blockchain come in, allowing IoT data to be shared and stored securely across that ecosystem and value chain.<\/p>\n
Power of IoT, AI and DLTs in connected cars<\/strong><\/p>\n![\"\"](\"https:\/\/www.tatacommunications.com\/blog\/wp-content\/uploads\/2020\/03\/AdobeStock_94201350-300x200.jpeg\")
<\/a>The connected car ecosystem is the perfect example of how AI, IoT and DLTs interact. As a car travels along, it generates and receives information as part of a wider IoT ecosystem, or vehicle-to-anything (V2X), environment. So, when it passes through an automated toll booth a transaction occurs that captures information about the vehicle, but it also requires access to a payment ecosystem that enables the secure, automated transmission of funds from the owner to the toll operator. This could be done in a variety of ways, including e-wallet technology, banking, charge card, etc.<\/p>\n\n\u201cPerhaps more significantly than that though, each transaction becomes part of potentially millions of transactions that can be analysed by a big data AI engine.\u201d<\/em><\/span><\/h3>\n<\/blockquote>\nThis could determine characteristics about the volume of cars going through the toll system, the time of day, type of vehicle, type of payment, direction, and so on, helping different players in the connected car ecosystem to understand more about their customers, vehicles and road usage. This data can then be used to determine not just how many toll booths need to be open at different times of the day, or when to close them for maintenance \u2013 but also if there might be market potential for opening a new supermarket or service station targeting commuters, for example.<\/p>\n
The challenges with IoT, AI and DLTs<\/strong><\/p>\nThe architecture and hardware\/software environments associated with IoT, AI and DLTs are different, which can make it challenging for businesses to bring them together in an-easy-to-operate solution.<\/p>\n
\n\u201cEach of the three technologies also has its own fragmented systems and standards, with an array of security, data privacy, legal, and ethical issues.\u201d<\/span><\/em><\/h3>\n<\/blockquote>\n
![\"\"](\"https:\/\/www.tatacommunications.com\/blog\/wp-content\/uploads\/2020\/03\/AdobeStock_207053065.jpeg\")
<\/a>For many businesses, successfully launching an Internet of Things (IoT) enabled service or application would seem like a reason to celebrate in itself. Yet, the real value of IoT comes from the data-led insights that can be derived from connected devices.<\/p>\nThe best way to manage and drive value from IoT data is with the help of artificial intelligence (AI), which is able to detect data patterns that might not be obvious to the human eye. Add in the strong degree of trust that distributed ledger technologies (DLT) like blockchain can bring to IoT \u2013 and you\u2019ve got a powerful combination of three technologies that could have a truly transformational impact on how businesses operate and interact with their customers, and how people engage with the world around them.<\/p>\n
The two ways to leverage IoT data <\/strong><\/p>\n Organisations may already use some form of analytics to manage both types of information. However, the enhanced capability of AI platforms to capture patterns of behaviour in both people and \u2018things\u2019, combined with the strategic insights these platforms can offer, could take a business to the next step.<\/p>\n That\u2019s where DLTs such as blockchain come in, allowing IoT data to be shared and stored securely across that ecosystem and value chain.<\/p>\n Power of IoT, AI and DLTs in connected cars<\/strong><\/p>\n This could determine characteristics about the volume of cars going through the toll system, the time of day, type of vehicle, type of payment, direction, and so on, helping different players in the connected car ecosystem to understand more about their customers, vehicles and road usage. This data can then be used to determine not just how many toll booths need to be open at different times of the day, or when to close them for maintenance \u2013 but also if there might be market potential for opening a new supermarket or service station targeting commuters, for example.<\/p>\n The challenges with IoT, AI and DLTs<\/strong><\/p>\n The architecture and hardware\/software environments associated with IoT, AI and DLTs are different, which can make it challenging for businesses to bring them together in an-easy-to-operate solution.<\/p>\n<\/a>Imagine you\u2019re a business with a fleet of cars, trucks, airplanes or freight containers. Using the IoT, you\u2019re able to capture two types of information that can be collected from them: primary operational information \u2013 such as location, engine condition and temperature data \u2013 to support the day-to-day operations of your business; and aggregated big data \u2013 which you can use to analyse a much broader range of factors about your services and enhance them through R&D, for example.<\/p>\n\n
\u201cIncreasingly, there is a need for IoT data to be shared across a wider IoT ecosystem too in order to maximise its benefits.\u201d<\/span><\/em><\/h3>\n<\/blockquote>\n
<\/a>The connected car ecosystem is the perfect example of how AI, IoT and DLTs interact. As a car travels along, it generates and receives information as part of a wider IoT ecosystem, or vehicle-to-anything (V2X), environment. So, when it passes through an automated toll booth a transaction occurs that captures information about the vehicle, but it also requires access to a payment ecosystem that enables the secure, automated transmission of funds from the owner to the toll operator. This could be done in a variety of ways, including e-wallet technology, banking, charge card, etc.<\/p>\n\n
\u201cPerhaps more significantly than that though, each transaction becomes part of potentially millions of transactions that can be analysed by a big data AI engine.\u201d<\/em><\/span><\/h3>\n<\/blockquote>\n
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\u201cEach of the three technologies also has its own fragmented systems and standards, with an array of security, data privacy, legal, and ethical issues.\u201d<\/span><\/em><\/h3>\n<\/blockquote>\n