Combining Blockchain with AI and IoT

We are witnessing the 4th Industrial Revolution unfolding. The emergence of exponential technologies like Blockchain, Internet-of-Things (IoT), Artificial Intelligence (AI), Quantum Computing, Big Data is happening. The convergence of such technologies is aiming to enhance efficiency, cost-saving procedures and in many cases eliminate the need for middlemen.

The combination of Blockchain, AI and IoT is being tested and/or implemented with a significant number of use cases. The ability of a blockchain network to provide tamper-proof, transparent recording of information is a great match for many industries. Its integration with data derived from AI and IoT procedures, aims to achieve efficient and innovative procedures across different sectors.

The fundamentals of these 3 technologies allow for multiple uses and innovative applications within many industries. The interaction between exponential technologies is an essential step towards economic growth through digital transformation. This blog post digs a bit deeper into the structure of AI and IoT, and demonstrates notable use cases upon their convergence with blockchain technology.

Artificial Inteligence (AI)

AI, refers to the ability of digital computers and robots to perform intelligent tasks without requiring human intervention. Among others, AI enables services like chess games and digital assistants, with the most notable one being Siri. Siri uses data gathered from Speech Recognition, Natural Language Processing, Machine Learning and through algorithms it determines and executes the requests of users.

Siri has been a crucial service which currently positions Apple as the leader on AI adoption ahead of other tech giants like Google, Facebook and Amazon.

Furthermore, AI use cases facilitate automatic flow of procedures through artificial creativity and image recognition. Translation of texts which is now essential in many online services also involves AI on the backend. In addition, AI experts focus on the industry of autonomous vehicles and robots which can execute intelligent actions.

Currently, AI procedures mostly perform relatively simple tasks like those mentioned above. The inclusion of Machine Learning allows for automated actions based on pre-determined rules and memory. The degree to which AI services can widely incorporate human decision-making intelligence is to be seen.

Theoretically, AI applications will be able to improve their intelligence through repetition of tasks over time. Thus, these services could vastly replace the need for human intervention. In an ideal scenario, machines would execute actions in a faster, more efficient and smarter way than humans.

Internet of Things (IoT)

IoT refers to physical objects and their integration with the internet and sensors.

Smart devices enable the gathering of data through sensors, which leads to internal processing and transmission of information to other internet-connected devices. According to Statista, approximately 22 billion IoT were connected globally in 2018. By 2030, the research expects this number to increase towards 50 billion.

Therefore, the IoT ecosystem will cover a variety of services “from smartphones to kitchen appliances”. Specifically, the combination of AI and IoT is one of the most significant milestones towards achieving the global adoption of autonomous vehicles.

Embedded IoT sensors that detect body signals are useful within the health industry, as they can transmit data and through a connected application monitor, predict and suggest actions regarding the treatment of patients.

The same applies to other industries that require an accurate prediction mechanism such as agriculture. IoT sensors can gather real-time data like temperature and light measurement.

IoT is embedded into gadgets that we can buy from our local stores. These include smart watches, smart house lighting and intelligent security applications.

For example, smart watches gather information from a user’s body i.e. temperature and heart rate to monitor sports performance and suggest workout routines.

Integration of Blockchain Technology

Blockchain networks act as secure mechanisms where transactions are stored without a central point of failure. For example, a transaction that blockchains can store is the temperature of a shipment package tracked by an IoT device. Additionally, for private blockchain networks, health data of patients gathered by body signals can be recorded and monitored.

This enhances transparency and reliability on patients’ information across health institutions, eliminating time-consuming tasks like retrieving medical history and loss of data. Furthermore, the integration of AI procedures could trigger actions like notifying the doctor and the patient for possible medical implications according to identified patterns. The blockchain can record all this information transparently.

Smarts contracts technology can enable self-executing procedures, which means that blockchain will enhance an additional tool besides a transparent, shared ledger.

In the example above with the shipment package, if the temperature of the package would fall below a set limit, smart contracts could apply a penalty to the default party e.g. the truck driver. Parties can pay this penalty (or earn rewards upon successful delivery of services) with blockchain tokens. IBM and Maersk’s TradeLens Shipping Solution, applied a similar supply chain concept in recent pilots.

All 3 technologies contribute from their own end, but to achieve maximum efficiency and security, the integration is inevitable. Even when intelligent data are gathered by AI algorithms and are combined with reliable data transmitted through IoT sensors, security is questionable. This is where blockchain comes into the equation as a decentralized, transparent transaction ledger.

The identification of parties is possible through self-sovereignty where users can control what data they share and who has access to them. This feature is essential for ecosystems requiring digital identities and security of sensitive data like the health industry.

Challenges on Blockchain Integration

Blockchains can only store and validate data they receive. Therefore, the accuracy of information on the blockchain depends on the reliability of its sources. In our case, it depends on the validity of the data coming from the AI and IoT integrations. Digitization means that data are subject to attacks and hacks. For example, a malicious party could try to alter temperature metrics measured from an IoT device.

Let’s imagine that a farmer is into an insurance contract with a company that tracks rainfall to compensate clients in case of flood. The recording is on the blockchain. The insurance company compensates the farmer in case of rainfall exceeding an X amount of millimetres.

What if the insurance company or the farmer himself attempt to hack the IoT sensor and alter its metrics on their benefit? The blockchain network would consider data from the IoT device as legit, even if someone would compromise the measurement.

Even if blockchain records accurate data, there are still some challenges to enable an optimal procedure. The scalability and interoperability of blockchain networks are still questionable and subject to discussion by the community.

With current inadequate scaling solutions, loading blockchains with additional data regarding real-world metrics, could make the procedures unsustainable.

The 4th Industrial Revolution is rapidly changing our society. The degree to which an integration of exponential technologies including Blockchain, AI and IoT is efficient, is still questionable. Several use cases are currently on a testing or even on the implementation phase in various sectors.

If we manage to solve the current integration challenges, a bright digital future may be ahead of us!