3 Potential Applications
3.2 Potential IoT Applications by Archetype
At Taraxa, we identified three distinct categories of IoT-relevant blockchain applica- tions. Because this is a nascent and rapidly evolving space, this only represents our latest thinking at the time of writing.
3.2.1 IoT Data Anchoring
IoT devices generate a great deal of data, and when that data is shared across entities it needs to be trusted. One way to augment trust is to establish unique identities for each data-generating device and have the devices anchor the data they have collected onto the blockchain.
The anchoring process involves placing a hash (a function that maps data of arbitrary size into data of fixed size in a way that minimizes collisions—different pieces of data cannot map into the same hash) of a data set collected by the device along with its signature into say a smart contract as a record of the provenance of the data as well as proof that the data has not been tampered with. Only the hash of the data set should be stored on-chain, and not the full data set, because we want to minimize the load and cost of using the blockchain, and the signature guarantees that the data came from the device. Of course, this would also require that the device manufacturer publish the public keys embedded (preferably via secure hardware) into their devices.
Examples:
• Cold chain logistics: a supermarket that is taking delivery of milk shipped via cold chain would like to have guarantees that the milk has been properly refrigerated throughout its route. If the refrigeration units were turned off during shipping for a few hours and turned back on, the supermarket would not be able to tell the difference until the milk started to spoil much earlier than expected. Location and temperature sensors could be installed on each refrigerated truck. These would intermittently upload data as well as anchor that data onto the blockchain, ensuring that the shipping company has not tampered with the data after the event.
• Public infrastructure monitoring: with the advent of public–private partner- ships, many local governments are increasingly outsourcing the management and maintenance of public infrastructure such as bridges, roads, and tunnels to pri- vate companies. Once outsourced, the government has a responsibility to ensure that public infrastructure is being well maintained and that the data reported are accurate (e.g., toll income, maintenance expenditure). Sensors installed on such public infrastructure (e.g., cameras, strain gauges, moisture) will also anchor the data they collect onto the blockchain to prove to local governments that the data has not been tampered with.
3.2.2 Machine Monetization and Eventual Tokenization
Many assets being monitored by sensors are revenue-generating machines that require significant upfront capital outlay to deploy. For many new ventures, obtaining funding or loans through traditional financing channels may be challenging.
With proper data anchoring, the earning capabilities of such machines can be tracked in real time on the blockchain, providing proof and generating expectations for future income. Businesses can then solicit potential customers to invest in shares
of these machines by issuing digital tokens on the blockchain. This has the dual benefit of not only raising funds from a much wider pool of potential investors, but also that the tokens are likely to end up in the hands of customers who have interest in purchasing the services of the machine in the future.
Examples:
• Shared vehicles: require the operator to not only make large upfront investments to purchase a fleet of cars but also run continuous and aggressive marketing cam- paigns to generate awareness. If each vehicle’s location, movement, mileage, and, most importantly, income data were to be released to the public (anonymized to protect driver privacy), each vehicle could be tokenized and those who purchase the tokens would receive a heavy discount when renting the shared vehicle. Not only does this alleviate the pressure of extremely large upfront investments, it also ties customers into an ecosystem that they now have a stake in and financial incentives into drive the service’s adoption, elegantly killing two birds with one stone.
• Smart vending machines: vending machines are becoming increasingly popular around the world as a low-cost and highly convenient alternative to manned store- fronts, but their value remains underutilized. With so many machines deployed on every street corner, they could easily be outfitted with additional IoT infrastructure to enable them to collect data of their surroundings (e.g., foot and vehicle traffic, localized weather) which could be sold, become distribution points for humanitar- ian aid (e.g., disaster relief, charitable giving), or even serve up a far more accurate alternative geo-location service (e.g., via WiFi triangulation) in an urban environ- ment to GPS. All of which could not only help further monetize these vending machines but also provide social services far beyond their original design goals.
Machine to Machine Economy
The ultimate application is to enable machines to trade with one another autonomously. This of course would require a very high level of intelligence and autonomy on the part of the machines, but a variety of mechanisms could be designed where machines can discover and purchase resources they require to optimally complete their stated objectives.
One of the most common mechanisms is a marketplace; here we look at two potential applications.
• Agricultural dronescould make real-time decisions on plant protection (e.g., insecticide) deployment across a field based on its internal models and by pur- chasing data from the surrounding sensors. It could purchase or trade data with cameras in the surrounding fields for analytics on pest detection, from local weather sensors to predict chances of rain so that the chemicals will not be washed off min- utes after deployment, from local soil moisture sensors to customize the level of dilution, etc. Once machines are automated by AI and empowered by blockchain, they can operate and interact with one another with minimal intervention.
• Traffic routingfor autonomous vehicles may be very different than for vehicles operated by humans. Vehicles could, in real time, communicate their intended destination, urgency, and willingness to pay surrounding vehicles so that traffic could be routed and reshaped in real time according to supply and demand. Usual visual and audio cues created for human drivers would not be necessary (e.g., traffic lights, turn signals), and in their place would be a dynamic real time bidding market for road space as a commodity.
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Steven Puis an entrepreneur and strategy consultant. Prior to founding Taraxa, he co-founded multiple venture-funded startups in wireless mesh networks, mobile health, and healthcare ser- vices. He was also a director at Monitor Deloitte’s China office, spearheading the firm’s digital strategy practice, advising senior executives of multinational corporations on how large organiza- tions could effectively capture innovation and respond quickly to market disruptions.
Mr. Pu holds undergraduate and master’s degrees in electrical engineering from Stanford University.
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