Three opportunities of Digital Transformation: AI, IoT and Blockchain

The emerging technologies of artificial intelligence (AI), the Internet of Things (IoT), and blockchain represent an exponential power of three opportunities for private enterprise as well as the public sector. Enterprises capable of exploiting these technologies will use them to optimize and enhance existing processes, create new business processing models, and develop innovative products and services for a new generation of consumers/users. They do not represent a technology-enabled future that is decades away; these technologies are available today to build the businesses of tomorrow, based upon how fast the technologies and their development environments mature and become interoperable.

Figure 1–1: AI, IoT, and blockchain: connected trustful insights
Figure 1–2: IoT connected to the cloud
Figure 1–2: IoT connected to the cloud
Figure 1–3: Blockchain, the trusted centre of the new Internet Web3, integrates processing with the IoT and AI
Figure 1–4: The progress of emerging technologies
  • The risk of bias and discrimination
  • The potential for intrusions of privacy
  • Mass surveillance that may encroach on democratic freedom
  • Secure key infrastructure and governmental operations from all adversaries Data is, of course, the lifeblood of AI, the IoT, and blockchain.
Figure 1–5: AI, the IoT, and warfare: today’s military environment
  • Planning capability to locate sensors and weapons systems optimally to counter identified threats
  • Situational awareness of the evolving battle and status of defensive as- sets at all leadership levels
  • Battle management to pair sensors and shooters optimally for effective defense against multiple threats and efficient asset utilization and engagement
  • Sensor netting to detect, identify, track, and discriminate threats
  • Global engagement management to enable war fighters to adjust defences to the emerging battle
  • Global communications networks to manage and distribute essential data efficiently

The Confluence of the Three Technologies

Why is this new “power of three” technology confluence possible? It is because of major upgrades in computational power, aligned with the petabyte amounts of data being created. (A petabyte is 250 bytes; 1024 terabytes, or 1 million gigabytes. A gigabyte is about the size of a two-hour streaming digital movie.) Consider the enterprise Trimble as an example. The transportation software giant is combining big data, the IoT, AI, and blockchain technologies to reduce costs and increase efficiencies. Large amounts of data are collected and imported from internal systems and various transportation devices; AI ML models are drawn up from the data and insights are recorded. The data is stored on a blockchain platform (see https://hortonworks.com/blog/big-data-powering-blockchain-machine-learning-revolutionize-transportation-logistics-industry/). Let’s look at three of the technology laws that predicted this confluence: Moore’s law, Koomey’s law, and Metcalfe’s law.

Figure 1–6: Moore’s law: The number of components on an integrated circuit doubles every year.
Figure 1–7: Koomey’s law: The energy efficiency of computation doubles roughly every one and a half years.
Figure 1–8: Metcalf’s law: The value of a network increases exponentially with regard to the number of its nodes.

How We Will Interconnect the Three Technologies

As the required technology and network connectivity are fast becoming available to support this trinity, how do we engineer these components to work together? To understand how IoT, AI, and blockchain work together, you can think of them as being like interconnected organic processes, analogous to human body processes (see Figure 1–9).

Figure 1–9: AI, the IoT, and blockchain work together in a system, similar to how interconnected organic processes control the human body’s function as a whole.

AI: The Brain

As mentioned in our analogy, AI is like a brain that provides logic and communication. AI is an area of computing science that emphasizes the creation of intelligent software and hardware that works and reacts like human brains. An AI neural network is designed to simulate the network of neurons that make up a brain (see Figure 1–9), so that the computer will be able to learn things and make decisions in a humanlike manner. Some of the activities AI is currently designed for include speech recognition and some degree of learning, planning, and problem-solving. AI is a big idea with an equally big opportunity for businesses and developers. AI is slated to add $15.7 trillion to global gross domestic product (GDP) by 2030, according to research by PwC (see https://press.pwc.com/News-releases/ai-to-drive-gdp-gains-of--15.7-trillion-withproductivity--personalisation-improvements/s/3cc702e4-9cac-4a17-85b9-71769fba82a6). AI is already transforming how companies process vast amounts of information. As you might expect, a good deal of this processing is being done in the cloud. The cloud is a network of remote servers hosted on the Internet to store, manage, and process data. Microsoft offers Cognitive Services to developers and companies using its Azure cloud computing platform. These services include data analysis, image recognition, and language processing, which require various forms of AI to complete tasks. Microsoft’s Azure Machine Learning Studio (see https://studio.azureml.net) is an AI development tool that analyses big data quickly and efficiently. If you use Google’s new Photos app, Microsoft Cortana, or Skype’s new translation function, you’re using a form of AI on a daily basis. Autonomous driving is the most daunting example that gives cars the ability to see, analyse, learn, and navigate a nearly infinite range of driving scenarios. AI enables cars to learn how to drive on their own — which could bring about a $7 trillion to the autonomous driving economy over the next three decades (see https://www.wired.com/story/guide-self-driving-cars/). In Phoenix, Arizona, Alphabet has launched Waymo One, the first commercial autonomous vehicle ride-hailing service. Waymo will license its AI autonomous driving technology to automakers and use it for package delivery ser- vices and semi-truck transportation as well. Sceptics may assume that AI is just a marketing buzzword for tech companies. But the big technology companies have been heavily investing in AI and ML for years, and the products and services listed here are tangible evidence that these tech giants are already making money from AI. When it comes to advancing AI, hardware may provide answers. Specialized GPU chips enable companies to process complex data and visual information quickly, which has made them ideal for AI cloud computing. GPU-accelerated computing is the employment of a graphics processing unit (GPU) along with a computer processing unit (CPU) to facilitate processing-intensive operations such as ML, analytics, and engineering applications (see https://www.nvidia.com/en-us/about-nvidia/ai-computing/). That said, as AI matures, we should always choose artificial intelligence over natural stupidity.

Advancements in AI

The point at which AI-assisted machines surpass human intelligence is predicted by visionaries such as Ray Kurzweil to arrive by 2045. MIT’s Patrick Winston puts the date at 2040. In a recent survey conducted with scientists and computer techs, respondents were quite positive that this will be achieved even sooner, with 73 percent of tech execs saying this moment will arrive within a decade and nearly half of tech execs believe this will occur within five years. Perhaps the earlier prediction is a result of the impressive pace of technology development that could cause people to overestimate technological capabilities or achievements (see https://www.edelman.com/sites/g/files/aatuss191/files/2019-03/2019_Edelman_AI_Survey_Whitepaper.pdf). Famous computer scientist Alan Turing, who is regarded by some as the originator of AI, devised the “Turing test.” To pass this test, a computer or robot is required to interact with a human in such a way that the human cannot tell it apart from another human. Chatbots, which mainly use text communication, and voice-only AI systems such as Siri, Cortana, and so on, have come a long way toward being more humanlike in their conversation. But by most ac- counts, they have not as yet passed the Turing test. That said, another concept that is probably more appropriate here is the uncanny valley, which refers to the point at which a computer or robot displays some humanlike features but is, ultimately, recognizable as a machine. Although Siri and other chatbots may be impressive residents of the uncanny valley, one of the most telling examples of AI are the robots made by Boston Dynamics (see https://www.bostondynamics.com/). We can see them walking, running, opening doors, and performing other tasks in uncannily humanlike ways, while Siri is just a disembodied voice. This area of technology is progressing so quickly that many are revising their forecasts regarding when AI systems will not only leave their uncanny valley abodes but also pass the Turing test. Consider a relatively new area of research, artificial consciousness, also known as machine consciousness or synthetic consciousness. These terms tend to refer both to AI and robotics, or cognitive robotics, which just means a robot that learns. As mentioned, this area of research is a hotbed owing to the disruptive forces causing advances in computing both in terms of storage capacity and processing capability. Advancements in cloud computing also offer viable and efficient tools for the development work. Both computing hardware and software are available to facilitate the development of AI solutions. AI methods such as machine learning and deep learning can give software and the specially made hardware that it runs on the ability to learn from vast amounts of data it collects. It can then use what it has learned to behave and make decisions in humanlike ways with- out getting too concerned with the definition of consciousness, which is many years away.

AI and Machine Learning

Within the broad field of AI, ML will have the most immediate impact. It has the potential to enable intelligent decision-making either in support of human intelligence or in place of it. Businesses will use ML to perform tasks to achieve a level of accuracy and efficiency beyond the capabilities of human workers. But putting decisions in the hands of intelligent machines has pro- found ethical and legal implications. (We will explore the legal implications in Chapter 7.) Although AI/ ML already make intelligent interventions on behalf of humans (such as voice-activated personal assistants), there is obviously much AI work to be done before machines are given full agency. The insights generated by ML will help businesses better understand customer expectations and market trends, enabling automated, personalized engagements. AI/ML will help in the creation of new goods and services, de- signed to meet the demands of modern consumers. AI/ML will empower business operations through analysis and strategic input. In the automotive industry, ML is the driving force behind autonomous vehicles. It can help the telecommunications industry identify and address network faults and enable financial services institutions to profile consumers more accurately. AI/ML will control customer service chatbots, provide marketing insights, identify cybersecurity vulnerabilities, enable personalized products and services, and facilitate an attorney’s ability to implement smart contracts. As we shall see, AI/ML combined with the IoT and blockchain provides significant potential for a historical transformation. There’s little doubt that the impact of AI on the business enterprise will be profound. So why aren’t we seeing more ground-breaking ML-powered products, services, and business models hit the market today? The answer, as with the IoT, is that maximizing the business benefits of ML is more challenging than it seems. To get from proof of concept (PoC) to full-scale production implementations will take years. SQL, the most popular data store to date, surfaced in the early 1980s but took nearly 10 years to become the data store of choice. It has remained so for more than 30 years. As with all new technologies, it represents an incremental process. To exploit the true value of AI/ML in the real world, the enterprise must do the following:

  • Recognize opportunities for AI combined with the IoT and blockchain and off chain data (such as supply chain as well as other applications), which will yield a strong enough potential return on investment to spend on initial development efforts.
  • Attract, develop, and retain talented multidisciplined developers to build platforms and applications that integrate AI and ML with the IoT and blockchain.
  • Foster AI, begin to accumulate and store data both internal and external, and structured and unstructured, and integrate it with IoT sources and blockchain implementations.
  • Put aside some budget to develop PoC’s for new and applicable use cases.
  • When mature and where applicable, apply AI combined with IoT and blockchain to existing infrastructure and capabilities.
  • Build a team of tech-savvy attorneys and financial staff to understand the emerging global legislation and regulation around AI.
  • Consider the ethical and legal issues regarding implementations of AI combined with the IoT and blockchain before releasing it for public consumption.

IoT: The Neurons and Senses

In our analogy, IoT is the human nervous system. The human brain supported by the nervous system comprises billions of connected neurons. IoT consists of billions of connected physical devices. With respect to IoT, these devices are connected to the Internet and they collect and share data. Pretty much any physical object can be transformed into an IoT device if it can be connected to the Internet and controlled that way. The central nervous system has a protocol network similar to the IoT network, which sends prompts to and from itself using neurons called dendrites and axons. Dendrites, as shown in Figure 1–10, bring information to the cell body, like a message from AI or smart contracts application. Axons take information away from the cell body, like the IoT device formulates a response to AI or smart contracts application. Information from one neuron flows to another neuron across the synapse. Our IoT devices are like components of the peripheral nervous system. They relay information like nervous system axons to and from the IoT server. By delivering messages via the IoT network, they provide us AI with status updates about our devices and their states. In a biological neuron, the dendrites receive inputs, which are summed in the cell body and passed on to the next biological neuron via the axon, as shown in the figure. Similarly, IoT platforms receive multiple inputs, apply various transformations and functions, and provide output.

Figure 1–10: The IoT and the central nervous system

IoT Components

Every tech revolution is propelled by the emergence of a new class of enabling components. The progress in compute-communicate energy efficiency during the past 50 years is unprecedented. That said, we need additional gains in power efficiency for logic and data exchanging wireless connections to pro- vide coverage so that the edges of the networks can be attached to individual components inside any machine to the any components of the real world around us.

IoT Networks

The architecture of the IoT ecosystem requires networks to transport data as information back and forth to remote users and devices using the cloud (see Figure 1–11). We need new API standards, protocols, and designs to operate within the current and perhaps newly designed cellular 5G networks. As these new IoT designs and standards emerge for transporting data, they will be developed to optimize resources to use a small fraction of the standard cell bandwidth. These new low-power standards will require lower speeds and power chips. Entirely new networks will emerge to be optimized specifically for the different characteristics of IoT connections — low power, low bandwidth, and use of frequencies with immunity to interruptions and tampering. This is very important for gathering information from and controlling real-time activities in the physical world (such as for autonomous vehicles), where there can be no tolerance for dropped connections.

Figure 1–11: The flow of IoT data using the cloud

IoT Devices: Sensors and Semiconductors

IoT devices can also actively measure attributes and states of a thing or person, such as temperature, vibration, velocity, location, personal health metrics, and so on. Today’s sensors are small, relatively inexpensive, and able to detect and measure using very little energy. The accelerometer in a mobile phone, for example, detects when we tilt the device. The opportunities for real- time monitoring and diagnostics are critical to emerging real-world industries, especially supply chain applications. These embedded chips can obtain power from the surrounding environment: vibration, noise, light, heat, and even ambient RF fields create energy in our environment. The sensors and semiconductor logic are already here, are relatively inexpensive, and are deployed to make connected cities, homes, and vehicles a reality. Semiconductor sales into IoT applications are running at $18 billion a year and growing at a rate of 20 percent annually (see https://www.eetimes.com/document.asp?doc_id=1330422).

IoT Integrated Circuits

The core of the IoT requires a near-zero-power integrated circuit (IC) and near-zero-power RFID chip. The chip is enabled by other devices that wirelessly send it power. The power is initiated from an external source, or reader, that beams radiofrequency energy at an unpowered chip, which then animates the chip so it can send a radio signal back to the reader. An RFID chip needs no battery, wires, or energy harvester on board. (For example, consider the card-enabled toll system used in vehicles.) RFID chip technology is well-suited for determining a thing’s identity, location, and authenticity. RFID is useful for tracking and monitoring food spoilage and safety and for tracking boxes in warehouses or trucks in the world’s supply chains. To this end, companies such as Impinj manufacture next-generation ultra-efficient RFID chips, the associated readers, as was well as cloud-based software and analytics to facilitate the utility of the associated data flood. Other RFID companies include NXP Semiconductors, Alien Technology, AMS, Phychips, and Zebra Technologies.

IoT Message Protocols

IoT devices work by pulling data from users either through input devices such as touch screens or sensors used for motion detection, temperature, humidity, pressure, and so on. This data is sent to the data servers for storage and processing, and the resulting information is provided to the end user de- vices for analysis and control. IoT and connected devices use different communication and messaging protocols at different layers (see Figure 1–11). The selection of the protocol used during development of an IoT device depends on the type, layer, and function to be performed by the device. Message Queuing Telemetry Transport (MQTT) and Constrained Application Protocol (CoAP) are two of the widely used communication protocols for the IoT application layer. MQTT is an M2M publish-subscribe–based messaging protocol that is used to communicate device data to the servers. Its main purpose is to man- age IoT devices remotely, especially when a huge network of small devices needs to be monitored or managed via the Internet, such as parking sensors, underwater lines, energy grids, and so on. MQTT messages are sent asynchronously through publish-subscribe architecture. The messages are encapsulated in several defined control packets, which are designed to minimize the network footprint. MQTT features a very low bandwidth using TCP/IP with no metadata and three quality of service (QoS) layers depending on the importance of the data being transferred. CoAP uses the familiar REST (Representational State Transfer) design pat- tern, which helped make the web so successful. Using REST, servers make re- sources available at uniform resource identifier (URI), the string of characters that unambiguously identifies a particular resource — the https://www.whatever. Clients access the resources using methods including GET, PUT, POST, and DELETE. CoAP features a REST client-server document transfer using HTTP for web integration and metadata to differentiate document types. CoAP uses the User Datagram Protocol (UDP), an alternative communications protocol to Transmission Control Protocol (TCP), to establish low-latency and loss-tolerating connections. CoAp uses Datagram Transport Layer Security (DTLS) to provide security for UDP applications, which enables them to communicate while preventing eavesdropping, tampering, or message forgery.

IoT Servers and Data Stores

Network infrastructure expansion emerges from and is driven by data traffic. M2M connections are the fastest growing contributors to total overall Inter- net traffic. By 2020, machine-related data is anticipated to grow 600 percent (see https://www.itu.int/dms_pub/itu-r/opb/rep/r-rep-m.2370-2015-pdf-e.pdf). Growth of the IoT will require new enterprise-class datacenters (see http://datacenterfrontier.com/internet-things-may-create-new-breed-data-centers/). The IoT requires its own servers and accompanying operating system (OS) — that is, an OS optimized monitoring events and attributes of things such as packages in warehouses or drones. The IoT server OS (see Figure 1–12) will require software for security to authenticate and authorize users and devices that access and provide data. IoT servers will contain rules engines that provide the ability to build intelligence in the IoT network. The server facilitates the ability of a process broker to send different commands, alerts, or data to the different devices and user clients based on the messages received by the broker. The rules can be defined based on type and topic of message or timer rules to send commands, alerts, or data messages to the topic based on a given date and time. The IoT server will provide systems management for the management of shadow data stores, registration of devices, and device data analytics. The M2M Internet will require security solutions embedded in the chips at the edges of networks using end-to-end encryption inside the IoT chip and synchronized with its network via the IoT server.

Figure 1–12: IoT server data flow

IoT Trends

As these components mature and become cost effective, the IoT will transform a world of things into a world of data about things. The data will be aggregated and securely stored on the blockchain. As discussed, practically anything can be equipped with a sensor and made smart. The public sector, manufacturing, transportation, automotive, consumer goods, and even health- care will never be the same once the IoT augmented by AI and BC takes hold. The combination of these technologies provides opportunities to extract new data and improve existing business processes, bring innovative products and services to market faster, and gather new information on consumer trends and preferences. A host of IoT smart household appliances and personal electronic devices will transform the consumer goods industry to create a new user experience and provide retailers with a massive amount of useful data. But the impact of the IoT will be felt far beyond the home.

  • Create an ecosystem of IoT-enabled devices.
  • Use data science to source, store, and manage all the potentially relevant data.
  • Develop analytics and ML capabilities.
  • Create new IoT applications that exploit the data collected using analytics and ML.
  • Integrate the IoT into existing applications and workflows.
  • Deploy end-to-end security to avoid tampering and interruption of data flow.
  • Monitor, manage, and iteratively adjust the entire value chain.

Blockchain: The Memory

IoT feels, and AI thinks. Blockchain, meanwhile, remembers. Blockchain is known as the technology that underpins bitcoin and other cryptocurrencies. In fact, it provides much more functionality and value than that. As mentioned, blockchain is a digitized, encrypted, decentralized database/ledger of trans- actions. The transactions are replicated across multiple computers and linked to one another to make any tampering with records virtually impossible. This immutable way of managing records eliminates the need for any central entity managing the transactions. Think of blockchain as the foundation of high-trust computing; it brings reliability, transparency, and security to all manner of data exchanges whether financial transactions, contractual and legal agreements, or changes of ownership. A blockchain uses a distributed peer-to-peer (P2P) net- work to keep an unalterable record of every exchange, removing the need for trusted, third-party intermediaries in digital transactions. The resulting value is faster processes, real-time transaction visibility, and reduced costs across every industry. From a technical point of view, blockchain is a distributed, thrustless, transparent, immutable, consensus validated, secured, cost reducing technology (see Figure 1–13). The blockchain is distributed because a complete copy lives on as many nodes as exist in the system. The blockchain is immutable because none of the transactions can be changed. The blockchain is consensus validated (for example, in the bitcoin space) by the miners who are compensated for building the next secure block or validated by a consensus algorithm, which is described in Chapter 2.

Figure 1–13: Blockchain is a distributed trusted information technology

Blockchain Types

There are three primary types of blockchains that serve different purposes and provide unique benefits: public blockchains, consortium blockchains, and private blockchains.

Public Blockchains

Public blockchain creators envisioned a blockchain available to all, where transactions are included if and only if they are valid, and where everyone can contribute to the consensus process. The consensus process determines what blocks get added to the chain and what the current state is. On public blockchains, instead of using a central server to store data, the blockchain is secured by cryptographic verification supported by incentives for the verifiers (miners). Anyone can be a miner to aggregate and publish those transactions. In the public blockchain, because no user is implicitly trusted to verify trans- actions, all users follow an algorithm that verifies transactions by committing software and hardware resources to solving a problem by sheer force — that is, by solving the cryptographic puzzle to find the next block. The miner who reaches the solution first is rewarded, and each new solution, along with the transactions that were used to verify it, forms the basis for the next problem to be solved. In 2019, proof of work (PoW) and proof of stake (PoS) were the most commonly used verification concepts.

Consortium Blockchains

A consortium blockchain, such as Corda or Hyperledger Fabric, is a distributed ledger in which the consensus process is controlled by a preselected set of nodes — for example, a consortium of nine financial institutions, each of which operates a node, and of which five (as with the U.S. Supreme Court) must sign every block in order for the block to be valid. (See https:// docs.corda.net/releases/release-M8.2/key-concepts-consensus-notaries.html for more on the consensus.) The right to read the blockchain may be public or restricted to the participants, and hybrid routes may exist, such as the root hashes of the blocks being public together with an API that enables members of the public to make a limited number of queries and get back cryptographic proofs of some parts of the blockchain state. These sorts of blockchains are distributed ledgers that may be considered partially decentralized.

Private Blockchains

The types of openness and pseudonymise that exist on a public blockchain usually aren’t suitable for transactions among business entities. For numerous reasons, including regulatory and security concerns, most organizations need to know who they’re dealing with, and they must also ensure that unauthorized participants cannot gain access to transaction data, which could contain sensitive corporate information. Even within the world of private blockchains, it is important to consider which degree of privacy is necessary and useful. For example, a strictly private blockchain run and maintained by a single entity within a single organization has limited use. Blockchain networks become more valuable when more organizations participate to share and transact data. But these organizations can participate only when they have been granted permission to do so. This type of permissioned network among a known set of participants is a private blockchain. In a private blockchain, consensus is usually achieved through a process called selective endorsement. It is based on the concept that network participants have gained permission to be there and that the participants involved in a transaction are able to confirm it. A blockchain using this type of consensus can be built with a more modular architecture, and it can allow for greater transaction volume at faster speeds. Endorsers are determined by the governance and operating rules for the network. As with all blockchains, private blockchains employ the recording mechanism of grouping transactions into blocks and linking blocks together into an immutable chain. But in a business context, when it is necessary to protect sensitive corporate information and customer data, it is important to secure the blockchain with additional measures. Private blockchain networks do the following:

  • Ensure separation between entities, providing horizontal protection.
  • Prevent attacks through privileged user accounts, providing vertical protection.
  • Protect encrypted data by securing the cryptographic keys.

Comparing Blockchain Types

It is important to draw a distinction between public, consortium, and private blockchains. Even “old school” distributed ledger adoptions that prefer a traditional centralized system can get the addition of cryptographic auditability attached. As compared to public blockchains, private blockchains have a number of advantages: The private blockchain operator can change the rules of blockchain. If it is a blockchain among financial partners, if errors are discovered, they will be able to change transactions. Likewise, they will be able to modify balances and generally undo anything, because there is an audit trail of all transactions. In some cases, this functionality is necessary — for example, with a property registry if a mistaken transaction is issued or some nefarious person has gained access and made himself the new owner. On a private block-chain, transactions are less expensive, because they need to be verified only by a few nodes that can be trusted to have very high processing power. Public blockchains tend to have more expensive transaction fees, but this will change as scaling technologies emerge and public-blockchain costs decrease to create an efficient blockchain system. Nodes can be trusted to be very well connected, and faults can quickly be fixed by manual intervention, allowing the use of consensus algorithms that offer finality after much shorter block times. Improvements in public blockchain technology, such as Ethereum PoS, will bring public blockchains much closer to the “instant confirmation” ideal. The latency difference will never disappear, because, unfortunately, the speed of light does not double every two years as posted by Moore’s law, which we reviewed earlier in the chapter. If read permissions are restricted, private blockchains can provide a greater level of privacy. Given all of this, it may seem like private blockchains are unquestionably a better choice for institutions. However, even in an institutional context, public blockchains still have a lot of value. In fact, this value lies to a substantial degree in the philosophical virtues that advocates of public blockchains have been promoting all along, among the chief of which are neutrality and open- ness. Public blockchains are open, and therefore they are used by many entities, and this provides networking effects. If we have asset-holding systems on a blockchain and a currency on the same blockchain, we can cut costs to near-zero with a smart contract: Party A can send the asset to a program that immediately sends it to Party B, which sends the program money, and the pro- gram is trusted because it runs on a public blockchain. Note that in order for this to work efficiently, two completely heterogeneous asset classes from completely different industries must be on the same database. This can also be used by other asset holders such as land registries and title insurance companies.

Blockchains and Smart Contracts

The term “smart contract” was first coined in 1994 by lawyer and cryptography researcher Nick Szabo, as he theorized on the future of e-commerce in the light of the newly born Internet. Szabo argued that contracts and legal agreements follow Aristotelian syllogisms — that is, if some condition is true then we can perform some function. So, for example, “if Buyer fulfils such and such conditions, then Seller is obliged to transfer the asset,” in paper con- tracts, could be replaced with computer programs that automatically execute the terms of an agreement. Computer code is precise and freed from fallible human interpretation. Moreover, it is tested and validated before it is implemented to a production status. In contrast, traditional contracts may contain flaws that cause disputes, resulting in lost time and money. Furthermore, contracts and agreements are mere words on paper; without an authority willing to enforce them, they’re rendered useless. Code, on the other hand, makes our modern world operate efficiently. Pre-programmed instructions can move money around, lock doors, and forfeiture payments in escrow, without one single policeman or bureaucrat signing an order or threatening punishment. The problem with code, however, is that it can be hacked. If we want a self-enforcing code contract to govern funds and property, this code would have to be stored on a computing platform that exclusively controls these assets. These contracts could never be trusted to be free from unauthorized alteration or attacks. For this reason, as time progressed and e-commerce became a common place practice, Szabo’s smart contracts remained an interesting but infeasible concept, reserved for future generations to iterate on. The blockchain, however, solves a problem raised by Szabo and his peers. Code stored in a blockchain system is freed from the need to physically reside in one single location, and hence it is not under the influence of the owner of said location. Furthermore, thanks to the public nature of blockchains and their consensus mechanisms, unauthorized alteration of such code is close to impossible. The term “smart contract” as it is used in the context of Ethereum, Qtum, and other Turing-complete blockchain platforms, is acceptable but slightly mis-leading. Ethereum’s smart contracts, written in a language called Solidity, are essentially code, not dissimilar from other programming languages. Although it is true that smart contract code mainly deals with transactions between agents in a blockchain system, and hence is able to describe and execute some terms of an agreement between such agents, smart contracts themselves are not actual contracts. A contract is a voluntary arrangement between two or more parties that is enforceable by law as a binding legal agreement. A bound contract generally re- quires an offer, an acceptance, consideration, and a mutual intent. Smart con- tract code doesn’t meet most of these criteria, especially the requirement to state an offer clearly, nor does it express acceptance and willingness to be bound by law, so it is not enforceable in any legal context. Third parties, es- crow services, and oracles (off-chain functionalities and sensors) could of course be brought into the picture, but they would have to be orchestrated in a way that enables users to interact with them easily and understanding what they’re doing. At the moment, smart contracts alone, as they are introduced with real-existing blockchain platforms, do not meet these requirements. Nevertheless, despite the relative crudity of the technology, smart con- tracts are by all means a ground-breaking innovation and will most probably serve as the cornerstone of future digitized commerce. With the advent of Turing complete blockchains and the IoT, smart contracts can safely and swiftly move assets around, interact with physical objects, and lead to the automation of many business-related processes that currently demand vast human re- sources and time. But to serve as a substitute for traditional paper contracts and the legal relations they dictate, these automated processes have to be orchestrated intelligently and flexibly, and they must be embedded in an interface that enables humans to make sense of them.

Benefits of Using Blockchain with the IoT

To summarize, the benefits of using blockchain with the IoT are trust, traceability, and security. Blockchain’s decentralized, open, and cryptographic nature enables people or entities to trust one another and transact P2P. As autonomous systems and devices interact with one another, the IoT transactions are exposed to potential security risks. Blockchain technology provides a simple, cost-effective, and permanent record of decisions made and communicated. Data transactions take place between multiple networks owned and administered by multiple organizations. Blockchain can provide a permanent, immutable record so that custodianship can be tracked when data or physical goods move between points in the value chain. Blockchain records are by their very nature transparent — activities can be tracked and analysed by anyone authorized to connect to the network. Security is of critical importance for IoT networks. Imagine a scenario where the hackers are able to attack the smart city network, thereby not only bringing down all the interconnected processes but also exposing personal data. If the data is exchanged over blockchain network, the overall security of the IoT network is greatly enhanced. Finally, blockchain or DLT systems use smart contracts or applications that are automatically executed when the conditions are fulfilled. Using smart contracts, the actions can be executed across various entities in the supply chain automatically, in an immutable manner without worrying about the disputes.

Use Cases for the IoT with Blockchain

The use of the IoT with blockchain is already gaining momentum. For example, IBM’s Watson IoT platform integrates well with the blockchain backed by Hyperledger Fabric (see https://www.hyperledger.org), effectively combining the two technologies. Similarly, Azure IoT also offers good integration with Ethereum, Corda, and Hyperledger Fabric. A number of technologies use blockchain in conjunction with the IoT. Blockchain can be used to record and timestamp sensor data. This way, the data from the sensors cannot be manipulated and can be trusted by all the parties parties in the transaction. In smart cities, multiple entities are collecting and acting on data, which can be trusted easily by use of blockchains. Use of blockchain in smart cities can even enable citizens or entities to sell data and get paid via bitcoins. The IoT comprises multiple devices, and these devices are authenticated based on digital certificates, which render the devices vulnerable to security breaches. Blockchain can create the digital identity of the devices so that they cannot be manipulated. Also, information about the devices can be dynamically updated, leading to higher scalability. Provenance of a product can be established using the IoT and blockchain. IoT sensors can be placed on medicine packets to trace and record information as the packets move from the factory, to the distributor, to the retailers using blockchain’s distributed ledger. IoT sensors with blockchain can go a long way toward avoiding the problem of fake medicines in many emerging markets. Gartner estimates that blockchain could create $176 billion of value-added revenue by 2025, thereby revolutionizing the supply chain, enabling new business models, and disrupting existing ones. Blockchain will prove to be a game- changer in numerous industries and sectors, such as financial services, insurance, e-commerce, healthcare, and human resources — essentially, anywhere digital information is exchanged. In the consumer goods sector, blockchain coupled with IoT sensors will provide transparency across the supply chain through asset tracking to enhance accountability, streamline product recalls, and improve consumer trust. In education and research, it will help to ensure that intellectual property rights are recorded and upheld. In finance, blockchain is the fuel powering the financial technology (fintech) revolution. As with IoT and AI, you’d expect blockchain to have been adopted and implemented more widely by now, particularly considering the media hype regarding blockchain and cryptocurrencies. But beyond those agile fintech start-ups, it’s still a comparative rarity.

Barriers to Blockchain Adoption

The problem is familiar: perceived risk and complexity stand in the way of widespread adoption. Barriers to blockchain adoption include the following:

  • The cost and availability of compute resources affect its widespread use, though advances in cloud computing will solve this.
  • The cost of blockchain miners affects its widespread use. The development of new consensus algorithms will correct and ultimately eliminate the miners.
  • Blockchain contracts are currently untested in court. Changes and new curriculum in law schools will foster the use and ultimate testing of these smart contracts.
  • Blockchain must be integrated with existing off-chain data stores and systems of record. Blockchain is undoubtedly transformative. In fact, much of its impact has yet to be explored, even on a theoretical level. But before the enterprise can discover the outer reaches of blockchain’s potential, these stumbling blocks must be overcome.

The Confluence of AI, IoT, and Blockchain Is Real

Today, even the most advanced technologies are usually reactive rather than proactive. Think of virtual assistants such as Siri, Alexa, and Cortana. Give them a command and they’ll respond to it, by playing a tune, ordering a product you’ve requested, or placing a call on your behalf. But when powered by transformational technology, these virtual assistants will become much more proactive. In the near future, your virtual assistant might observe that you’re running low on a particular product and suggest that it place an order for you, or it could tell you how you can find the best value by adjusting your purchase habits. IoT data from your refrigerator will determine that your almond milk is running low. ML will work out which retailers sell your preferred brand. And blockchain will ensure that the transaction is processed securely, and you get exactly what you paid for. Consider U.S. healthcare in its current form, whereby the patient decides when she needs to see her doctor. This happens generally only when visible symptoms have appeared, or an accident has occurred. The patient must schedule an appointment, and then remember the pertinent details of their medical history during the visit. Human beings are, of course, fallible and forgetful. So, imagine a nation of sensor-equipped patients, in which ML monitors IoT sensor data and can determine, at an early stage, when something has gone wrong. The patient’s virtual assistant can cross-reference her calendar with her doctor’s calendar and schedule an appointment automatically. And when the patient arrives, blockchain will ensure that she has a secure, accurate, digital medical history for the doctor’s reference. With respect to cybersecurity, new, more-stringent regulations such as the GDPR in Europe, the mutating threat of cybercrime, and the increasing value and proliferation of consumer data has made cybersecurity a universally pressing concern. But even here, the IoT, AI, and blockchain can have a transformational effect. These technologies largely remove the human element from cybersecurity and similar processes. When practically everything is sensor-equipped, log and audit data can be collected in a centralized repository. ML can analyse this data far more quickly and accurately than any human could, make logical decisions, and take autonomous action. And any and all critical evidence is securely recorded via blockchain. This system effectively bypasses the most common causes of data breaches — carelessness, human error, and malicious intervention.

In Conclusions

These three transformational technologies will bring change in our professional and personal lives, in the companies we work for, and in society as a whole. But their impact doesn’t belong to a vague and distant future; as we have hopefully demonstrated, most of these capabilities are available today. The IoT, AI, and blockchain complement one another well and can potentially remove some of the drawbacks of these technologies when they are implemented in isolation. The idea of these technologies working in tandem is not new, but it still needs time and effort to mature, with efforts driven by the passion of a new wave of developers. We have yet to envision the full impact of he confluence of these technologies. Now is the time for visionaries to wake up to the potential of this trinity and start to look at creative ways of using them for appropriate solutions, which are not limited by technology solutions, but only by our imaginations. In summary, here are items to review and research when preparing for transformational technologies:

  • Focus on the trinity: the IoT, AI, and blockchain.
  • Understand that these technologies need and use increasing amounts of data to operate efficiently and accurately.
  • Embrace transformational technologies and realize that attaining full potential will require a change in business processes or models.
  • Integrate transformational technologies across the entire enterprise, giving your organization the efficiency and agility, it needs to compete.

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Founder and Chief Executive Officer (CEO) of SkyDataSol

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