Research Summary: SoK: Applying Blockchain Technology in Industrial Internet of Things

On the bright side, innovations in blockchain computation efficiency come out gradually. It’s not standing still. Researchers and engineers are working hard to reduce energy consumption. Ethereum even promised to reduce energy consumption by 99 percent on Ethereum 2.0.
If it’s going faster every day, maybe we can get lucky, and see blockchain overcome this barricade in the future.

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Hi @rlj: Thanks for an excellent summary of a paper which reveals many of the contradictions presented by the history of IIoT, as well as the possibility of mitigating those contradictions with blockchain—if industry can bring itself to adopt the necessary new “mindset” around the issues at hand.

Here are a few key points that leapt out at me:

This highlights a key failing across most industries and OEMs: Attempting to achieve customer “lock-in” with core proprietary protocols and technologies rather than adopting common-sense standards and then differentiating products on top of those standards.

The assumption that all data is equally valuable, to be preserved forever, is simply in error. A huge percentage of industrial data can be collected and processed at the edge, acted upon appropriately, and then discarded.

The paper characterizes “edge” devices correctly…

…but it doesn’t say that if we adopted a fully networked, P2P, decentralized state of mind, the limited computing power, storage, etc. of IIoT devices might be seen as an opportunity not a constraint.

For example, the paper notes:

But then it goes on to say:

Why can’t these “robust encryption primitives” be implemented on edge devices themselves?

Finally, the paper makes an intriguing point:

This is a fascinating area. Can you say a little more about it?

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@GanouTeikun welcome to the forum! With a mechanical engineering background, I was thinking that this post may interest you.

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@Gearlad how does this overlap with some of the work you and @Sean1992076 have been working on? @rlombreglia mentions embedding primitives in IoT devices, can you talk about some of the work your lab has done with turning IoT into lightweight ETH nodes and routers – would that be a solution ot some of the issues he’s brought up?

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Having worked in this field, I had an experience that using blockchain for storing all the data is quite a terrible idea (from an implementational perspective). Instead signed Hash Tables can be the preferred way since we had like more than 50 GBs of data only from one industrial site per day. Imagine storing it as multiple copies stored on all participating nodes that too through consensus!

And for immutability of data (partial?), as long as at least one party can provide full copy of data along with the corresponding signed hash table from blockchain, we are good :slight_smile:

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@kanad really good to have you on the forum again! Can you tell us a little more about the challenges of combining IoT and the blockchain – do you agree with the article’s basic premises about the increasing complexity of the networks requiring some kind of decentralized structure? Or are there other solutions that might work temporarily (especially in an industrial environment)?

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First of all, I admit that I’m still a newbie in blockchain and I’m still familiarizing myself with IoT and blockchain and their implications in Industry 4.0.
I was having an interesting discussion with @Gearlad in my lab in the Mechanical Engineering department at National Taiwan University. Industry 4.0 is all about leveraging IIoT technologies to allow for cities to become smart cities and for factories to become smart factories. Let’s just say this: our lab plans to become a smart lab.

We collect all kinds of experimental data including temperature, pressure, size of material, ratio of different elements in an alloy, current (Ampere) density of materials, performance as an electrocatalyst for C02 reduction, so on and so forth. Currently, not all of the data that we collect is done with the help of machines, and some of it is based on human observation. Moreover, for the data we do collect, we store it on two mediums: Google drive and an external hard disk. An external drive is safer than Google drive in terms of privacy but nonetheless is still prone to damage and is less accessible than cloud storage. Unfortunately, Google is planning to shut down its unlimited storage plan for institutions and enterprises. What we need is a new form of data storage that is both secure and easily accessible, and blockchain is promising in overcoming these limitations.

Currently our lab has more traditional equipment that is not yet connected to the Internet. However, we have begun modernizing the lab with the application of AI and IoT. The purpose of this is to autonomously sort all of the data that we collect and to find different trends in this data. By using these trends we can predict the change in parameters such as size of ligaments, morphology structure, and electro-catalytic performance in porous metals. We use a Scanning Electron Microscope (SEM) to observe the morphology of porous metals; however, due to the limitation of the equipment, we need to adjust the focus of the images manually. Anothing thing worth mentioning is the facility is unable to generate mappings of elements on the porous metal, unless a better SEM machine is purchased (very expensive!). By applying IIoT, we have been planning to further upgrade our lab’s furnace so that it can monitor and control fluctuations in temperature more efficiently. In addition, automation can also increase safety as we’ve been relying on human observations to stop the furnace when the overshoot of the temperature in the furnace rises too high. Also, since we heat up materials in vacuum chambers, IoT may warn when the pressure of the chamber rises above the acceptable level. The examples above are also connected to the Industrial Control System (ICS) since we are aiming to create a system where all devices are able to do feedback control when some abnormalities arise.

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Thank you so much for this contribution! In the summary, it is mentioned that edge devices may be utilized to consolidate transactions so a node can validate without having to be onboard each IIoT device. I am not sure if the author made any suggestions how to get to this point of standardization without regulation or industry cooperation? There seems to be an assumption of a common goal driving the theoretical IIoT blockchain layer; but in reality, what would compel a private organization to join a non-government regulated public network as a means of monitoring their devices if they did not have at least SOME influence over that network layer?

While the proposed IIoT layer does sound useful in theory, has there been any indication that any private organizations have a desire to add a non-proprietary edge layer to their IoT devices, or has that notion mainly been coming out of the blockchain theoretical space?

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Great post, really enjoyed reading it.
I thought @rlombreglia replies were quite interesting about embracing constrains as opportunities.

Decentralized disjointed layers can help organize data for some type of central processing. First, you would have to characterize your industry/orgnization into nodes/layers and have extreme understand of how they communicate with one another. Local nodes can have set data standardization among the locale to improve scalability. Only using the distilled data from the points you reduce bottlenecks.

The ground level of one organization will most likely never look like another- so why even try to solve that problem? Differences are what makes organizations/sectors special; the overarching overlaps are where leaps and bounds will happen: getting product from A to B is ubiquitous. Though, I do not think a solution will make itself present until tested.

Showing decentralizing your business to align with the blockchain structure is where improvement using blockchain starts being obvious.

It makes sense to me if the benefit of the blockchain is to be decentralized, so why not decentralize your industry? This will probably be backwards for a lot of current industries but newer ones for example with AI- helping managing datasets as smaller pieces of a whole could possibly improve transparency.

An example that shows this is mentioned in the Smart Grid section about how centralized energy management systems (EMS) are less efficient than decentralized blockchain EMS.

The more decentralized the system the better a decentralized technology like blockchain benefits it.

I would like to learn more about this topic as it has huge implications, thanks for writing it!

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This was an exciting read which shines a light on the Industrial Internet of Things as one of the key components of Industry 4.0. I really love how this summary highlighted some of the issues and challenges of IIOT and this includes the heterogeneous nature of IIoT, the complexity of the network, the poor interoperability system, the massive data management, and the risk of being vulnerable to a single point of failure. There seems to be an assumption that the application of blockchain technology could help improve the efficiency of IIoT. I am not sure if the author answered the question on how Blockchain technology will solve the challenges of IIoT and thus, drive Industry 4.0.

Reading the article [1], the author opines that the decentralized nature of blockchain could eliminate a single point of failure, save operational costs, and enhance trustworthiness, and the immutability nature of Blockchain could make the data of IIOT difficult to be tampered with. However, we all know that Blockchain technology has a scalability issue, and also, the Storage issue, which is interconnected with the scalability issue because as the size of the chain grows, nodes require more and more resources, thus decreasing the system’s capacity scale. Is the trade off with integrating blockchain technology to IIoT really worth it?

I know there is a Current Use of Blockchain, the hyperledger Fabric, in the supply chain industry. for instance, Walmart used Hyperledger fabric to trace a batch of mangoes in 2.2 seconds, something that typically takes 7 days to do [2]. My last Question is thus, if Walmart were able to improve its supply chain efficiency with Blockchain, how, then, is latency a challenge blockchain needs to improve upon?

  • [1] G. Wang, “SoK: Applying Blockchain Technology in Industrial Internet of Things”, Cryptology ePrint Archive, 2021. [Online]. Available: https://eprint.iacr.org/2021/776.pdf . [Accessed Sept. 18, 2021]

  • [2] Tan B., Yan J., Chen S., Liu X. (2018) “The Impact of Blockchain on Food Supply Chain: The Case of Walmart”. In: Qiu M. (eds) Smart Blockchain. SmartBlock 2018. Lecture Notes in Computer Science, vol 11373. Springer, Cham. https://doi.org/10.1007/978-3-030-05764-0_18

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This was an interesting read which gives a take on the core implications and potential of Blockchain as a service being the cornerstone to large-scale automative architectures. I really love how this summary gives me further perspective on the scaling trilemma of scalability, maintainability, and ease of use problem that Blockchain is facing! This may be a recurring theme of blockchain and the core hurdle for integration and adoption of blockchain for conventional commercial activity.

Ensuring decentralization over a vast perimeter of automation may hinder scalability and may introduce further layers of abstraction that make such automation unnecessarily difficult to operate. Ensuring Scalability inevitably makes tradeoffs of blockchains decentralization and makes maintenance of said blockchain possibly irreconcilable. This goes the same for ensuring ease of use. Conveniences and an emphasis on good user experiences may as well make the entire application architecture so simple that blockchain may be the last technology any developer would want to use in their application.

In terms of the scalability issue of data management, would you think the concept of a “blockchain database” be appropriate to facilitate further development of Blockchain as a service oriented automative applications?

Blockquote El-Hindi et al. introduced the concept of Blockchain DB with the aim of coming up with a shared database on blockchains [2]. Blockchain DB introduces a database layer on top of the existing blockchain framework that extends the blockchains by classical data management techniques. The aim of blockchain DB is to increase the performance and scalability of blockchains for data sharing but also decrease complexities for organizations intending to use blockchains as DB.

An overview of blockchain scalability, interoperability and sustainability
2. El-Hindi et al. (2019). “BlockchainDB - Towards a Shared Database on Blockchains.”

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Thanks for your comment @jmcgirk.

Industry 4.0

The fourth Industrial Revolution (Industry 4.0) is driven by the goal of complete industrial automation with minimal third-party interference. Industry 4.0 has emerged through the convergence of Cyber-Physical Systems, the Internet of Things (IoT), and the digital technologies outlined in Fig. 1. In this paper, the author emphasizes that blockchain will have a profound impact on Industry 4.0, which may be attributed to underlying principles like better efficiency and trustworthiness. Blockchain-enabled Industrial Internet of Things (IIoT) platforms may enhance the main features of Industry 4.0 illustrated in Fig. 1. This is discussed with more depth as follows:

Decentralization

As a decentralized ledger technology, blockchain enables self-regulating capabilities and eliminates reliance on centralized authorities. In blockchain networks, smart contracts are real-time auditors that perform self-executing functions once specific conditions are met. Integrating blockchain networks with IIoT platforms may advance the autonomy of smart machines in industrial manufacturing by securing machine-to-machine communication. Further, removing third-party actors reduces overhead costs and risks associated with intermediaries and centralization, such as single-points-of-failure attacks. Considering that Industry 4.0 seeks to achieve full industrial automation, implementing a decentralized system may support its intent. This makes blockchain infrastructure and its autonomous capacities an appealing prospect for Industry 4.0.

Trustworthiness

The use of blockchain may enhance data transparency and increase trustworthiness in Industry 4.0. In this paper, the author emphasizes that having autonomous agents communicate directly provides several key advantages: greater efficiency, lower expenses, and fewer risks involved. However, in centralized industrial systems, the trustworthiness of the agents is an open issue [1]. Blockchain can address this concern. With all data on a blockchain stored in an immutable and transparent manner, participating entities can monitor and verify transactions across multiple industrial sectors and organizations. Additionally, consensus mechanisms ensure that all participating agents within a network share the same account of the truth while smart contracts running on blockchain regulate data provenance, ownership, and user access controls. Since data stored on the blockchain is visible to the authorized users of a network, it is resistant to manipulation, and tampering is more readily detected. With more reliable data, the technologies deployed in Industry 4.0 are better equipped to “extract value” to improve learning patterns and autonomy within smart factories.

Digitization

As “​​the backbone of any industrial sector”, the supply chain plays a critical role within industrial processes. Blockchain technologies can be implemented for quality control and more efficient supply chain management. One example of this may be found within the aerospace industry, where blockchain will potentially increase revenue by up to $40 billion. With a single aircraft consisting of up to millions of individual parts, blockchain technologies can assign each one a digital identity and retain its “digital twin”—a complete record that includes its provenance, service maintenance history, configuration, etc. This record is continually updated and available in real-time, making problems easier to diagnose and thereby reducing the frequency of manual testing and inspection. For instance, Airbus was able to take a preemptive maintenance approach by embedding sensors into its machinery. Blockchain’s utility within the aerospace industry can generally be applied to industrial ecosystems. Industrial assets like smart devices and machinery may be digitized and tracked. With parts originating from many different suppliers, traditional systems face challenges with counterfeits, communication barriers, and manual record-keeping. In Industry 4.0, blockchain can help to overcome drawbacks such as these by providing a secure digital accounting system that allows for greater traceability and tracking.

Conclusion

The topics discussed here only touch on a very small portion of blockchain’s utility within Industry 4.0. Anyone interested in further exploring blockchain’s applicability in Industry 4.0 should read “Blockchain technology applications for Industry 4.0: A literature-based review.” With regards to this response, the key takeaways of blockchain within Industry 4.0 are: decentralization that allows for greater automation of industrial processes, trust through transparency and immutability of industrial data, and digitization that allows for traceability and tracking of industrial components.

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@Twan, I’m interested to learn how Ethereum 2.0 will reduce energy consumption by 99%. What measures are being taken to achieve this level of efficiency?

@kanad Thanks for sharing. The author of this paper mentions that the use of hash tables is a possible storage solution for resource-constrained IIoT devices. Can you elaborate more on how distributed hash tables are beneficial for industrial applications and end devices based on your experience? Also, I’m curious to hear your thoughts on whether you think one of the author’s proposed models of integration or BaaS is appropriate for industrial uses at this point in time.

@GanouTeikun Thanks for your comment. As the lab integrates more technologies that introduce connectivity to traditional equipment, what do you see as possible challenges or risks posed by this scenario? How might blockchain advance automation in the lab? What are some potential drawbacks that blockchain may present? Lastly, can you provide some insight on the uses of blockchain and AI convergence in the lab?

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@rlombreglia Thank you for your thoughtful additions to this conversation. While this paper has limited discussion on edge computing, it most certainly is an area that deserves attention given its tremendous growth, relevance to time-sensitive applications, and potential to accelerate industrial performance.

Edge computing processes and transmits local data, meaning that the bandwidth requirements are lower in comparison to dispatching all data to a centralized data center. Edge computing can reduce cloud storage requirements by filtering out “excess data”, and sending the essential data to the cloud. Limiting the data processed by the cloud mitigates latency, making edge computing an emerging solution for the timing requirements of industrial operations. Gartner estimates that around 75% of “enterprise-generated data” will be “created and processed outside a traditional centralized data center or cloud” by 2025. As industrial applications continue to incorporate more smart devices into their networks, finding an efficient and secure means to manage the massive data that they generate is increasingly imperative. Edge computing shows promise in addressing scalability challenges and lowering operational overhead in Industrial Internet of Things (IIoT) platforms, yet the inherent risks of centralized systems prevail.

Blockchain technology may be implemented on the IIoT edge to secure communication and minimize networking and computational overhead. This is observed in the following use cases:

  • Guo et. al tested a “distributed and trusted authentication” model that deployed a Practical Byzantine Fault Tolerance consensus algorithm to record and authenticate data on a consortium blockchain. In the experiment, edge authentication services were provided via smart contracts, an asymmetric cryptographic framework was applied to secure the edge nodes and terminals, and an edge-based caching scheme was used to accelerate the hit ratio. The results suggested that the proposed model outperformed current edge computing models by reducing delay, improving hit ratio, and providing a more cost-efficient communication and computational mechanism.

  • Jangirala et. al developed a simulation that used blockchain identification technologies to verify the identity of reader tags within supply chains. By applying a “lightweight blockchain-enabled RFID-based authentication protocol” within a “5G mobile edge computing environment” with bitwise rotation, bitwise exclusive-or, and linear cryptographic hashes, the researchers observed enhanced security and improved communication and computational overhead compared to existing models.

  • Nkenyereye et. al proposed a private blockchain-based “lightweight multi-receiver signcryption scheme” for emergency driven messages (EDM) in 5G vehicular edge computing. By implementing blockchain into edge nodes, EDMs were securely transmitted to edge servers within close proximity to reduce response time. The findings of the study indicate that the edge nodes were shielded from various attacks and that the model reduced communication overhead.

To reiterate the material discussed in the summary, blockchain removes centralized solutions used to increase throughput, i.e. resorting to expensive networking equipment, and it eliminates fees associated with third parties. Though the author of the original article does not specify as to why blockchain may lower these overhead expenses, generally it seems that incorporating blockchain into edge nodes, adopting lightweight protocols, and using blockchain as an overlay for heterogeneous devices may allow for communication to become more efficient, secure, and cost-effective.

At the edge layer, poorly-secured edge devices are an entry point for attackers. Moreover, resource-constrained edge-IoT devices in centralized systems face challenges with implementing “fine-grained access controls” and “encryption-based data protection.” Integrating blockchain into the Edge of Things (EoT) networks enables security services such as “access authentication, data privacy preservation, attack detection, and trust management”. The convergence of blockchain, edge computing, and the internet of things creates the novel paradigm coined the blockchain edge of things (BEoT). In the BEoT, data may be secured through encryption algorithms, yet one thing that remains unclear to me is if the “robust encryption primitives” are actually integrated into the edge devices themselves. From what I gather, encryption mechanisms are carried out in the network layer after the blockchain authenticates the devices.

Per conversations with @Gearlad, Francis has experience with edge computing. @fmendoz7, is there any insight that you can provide on this topic?

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Hi @rlj, thank you for the thoughtful response. Our lab lacks personnel with a strong IT background. In addition, it’s a fairly new lab and we are still in the process of acquiring new resources.

Our research is more related to material science; we’ve just started getting into applying automation and AI technologies to our research recently. Some of the softwares we use include OriginPro, Jade (for XRD), CHI6273D electrochemical analyzer, AutoLab, etc. There are many cases where the application of AI is beneficial to all parties involved, and there are ways that it could optimize the softwares we use. With that being said, there is always an inherent risk with using AI and taking humans out of the equation.

Risks involved with humans operating the lab

  • Human error in measurements
  • Human safety hazards: burns (acids, bases, fires, etc.), toxic gas, cuts (especially when using drills or cutters)
  • Causing damage to equipment
  • Incorrect operational procedures

Risks involved with automation of lab processes

  • Variables not programmed to be factored in by AI - these cases need human intervention
    • Dulling of blade, malfunctioning parts, etc.
    • Introducing machine to dynamic or rapidly changing environment
  • As you mentioned in your summary, end devices can be attacked or hacked as they lack sufficient security mechanisms.

Blockchain can be used for material sciences in the context of supply chain management. Quality assurance and ensuring that data is tamperproof is something that the blockchain can provide; blockchain has the potential of tracing the usage of materials from source (manufacturers, miners - of metals, not of hash values, etc.) to consumer (researchers and different industries).

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@Larry_Bates Thanks for your comment.

In this paper, the author’s position on standardization is based on the notion that failure to conform to open standards will eventually result in incompatibility. However, one of the arguments is that on a fundamental level, standards will reduce overhead, mitigate risks, and reduce the complexity of networks. Especially within the industrial ecosystem, where there is an urgent need for solutions that “support diverse use cases” to enhance interoperability, fuel competitive advantage, and optimize performance, “open standards” could promote transparency, trustworthiness, and better security. Due to the interdependent nature of industrial organizations, which entail “multiple stakeholders”, public and private enterprises may be inclined to adopt generic standards rather than deploy case-specific solutions to overcome barriers to varied trade and partnership opportunities [1]. Moreover, the International Organization for Standardization emphasizes that “the era of proprietary solutions is over and replaced by the new era of shared economy.” Bearing in mind the shift toward Industry 4.0, standardization may “facilitate automation” essentially by simplifying industrial architectures, improving auditability, and easing the integration of “communication technologies.” That being said, though many efforts for standardization are underway, these developments are still in a nascent stage and it seems fair to assume that many of the ideas surrounding the IIoT-blockchain layer are indeed based on theoretical inferences at this point in time.

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Hi @rlj , thank you for sharing this interesting and useful summary. Recently, the development of TinyML has been in place gradually, and it sounds like a key development field. I’d love to know how do you think about how this trend will affect the integration of IIoT and blockchain. Thanks!

Applying a blockchain database service layer shows potential for improving scalability and reducing complexity for conventional Industrial Internet of Things systems. As an abstraction of the underlying layers, the blockchain database layer can leverage cloud-based platforms, enabling the deployment of essential blockchain services without modifying existing infrastructures. While blockchain databases such as BigchainDB, ChainfyDB, Cassandra, and Modex claim to improve performance, it seems as though the appropriateness of the blockchain database layer depends on the parameters of specific applications and the scheme employed. In the case study you referenced, BlockchainDB allows for data sharing and replication to be defined by the application, whereas direct blockchain-IIoT integration requires all nodes to distribute and record all transactions. With that in mind, scalability may be improved by allowing for more efficient data management. On the other hand, by implementing blockchain-as-a-service, smart contracts can further automation in industrial environments. Overall, I would say that the overarching draw of adding a blockchain database service layer is the ease of use, fewer complexities, less investment, and user-defined blockchain features. Regardless of better scalability, incorporating the database service layer still raises the concern of standardization and independent technological developments, which ultimately could be harmful to the compatibility of the industrial landscape. Therefore, it might be more of an immediate fix for blockchain-IIoT integration rather than a long-term solution. I am interested to hear your thoughts on this. Thanks for the great question @Tony_Siu

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I would also like to highlight the coffee shop gurus sayings to “never store data on the blockchain”. According to an article from forum.cardano.org, it clearly differentiates between a database and a blockchain. Essentially, the concept of a blockchain can be surmised as a kind of database but it was never designed to be a data storage in its inception. It may be better to think of blockchain not only as a simple decentralized database but to better utilize and develop blockchain according to its original concepts.

Regarding the issue with IoT data standardization along with “independent technology developments”, I do not think this to be an issue at all. With the subject of standardization, I would reckon that many conventional feature engineering techniques are already being deployed in commercial data science. There is even an entire profession dedicated to it called the “data engineer”. Unsupervised data preprocessing algorithms for generating N-dimensional numerical feature embeddings are widely used in sentiment analysis or in the underwriting for large insurance companies. I personally know and met researchers of research projects underway to further develop data oracles as individual preprocessing nodes called “knowledge graphs” that are currently being researched to automate the dispersed abstraction layer of IoT incumbent data! So I do not think a database requiring a single source of truth to be a short term fix nor do I think “independent technology developments” be any possible obstacle with the advent of newer and newer techniques and technologies.

Here’s a paper on unsupervised feature engineering techniques for automated sensor anomaly detection:

H. Y. Teh, K. I. -K. Wang and A. W. Kempa-Liehr, “Expect the Unexpected: Unsupervised Feature Selection for Automated Sensor Anomaly Detection,” in IEEE Sensors Journal, vol. 21, no. 16, pp. 18033-18046, 15 Aug.15, 2021, doi: 10.1109/JSEN.2021.3084970.

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I just read an interesting World Economic Forum post that pushes back on the folk-hysteria surrounding blockchain energy consumption. Entitled “Why the debate about crypto’s energy consumption is flawed,” the post notes that crypto “consumes less energy globally than tumble driers or domestic refrigeration” yet provides enormous societal benefit that is rarely factored into discussions of the topic.

The post goes on to catalog a number of energy-consuming activities that most people would consider vital to modern society, ranging from aviation transport to air conditioning and domestic refrigeration. Bitcoin (which is the barometer used in this post) uses a tiny fraction energy by comparison to any of “socially essential” use cases.

In return, “crypto uses energy to provide an alternative, borderless and decentralized store of value,” thus providing “economic freedom to people in developing countries.” By WEF’s reckoning, the energy used by crypto is a fantastic bargain. And the post goes on to note that the still-young crypto industry is also making progress toward greater sustainability.

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