“Things” have been around much longer than the Internet or Blockchain. The term “Internet of Things”, however, seems to have emerged around 1999 [1], [2], and gained more widespread recognition with the 2005 ITU report [3] (which seemed largely concerned with RFID technologies). In 2010, the IoT application domains included transportation and logistics, healthcare, smart environments, personal and social, and robot taxis, smart cities, and virtual reality were considered “futuristic”; while data authentication, data integrity, privacy, and data forgetting were considered open research issues [4]. IoT was added to the 2011 Gartner Hype Cycle, and hit the peak of inflated expectations in 2014, based on embedded sensors, image recognition, and near-field payment technologies. Early standardization efforts on IoT were primarily focused on optimized communication technologies (e.g. [5]). Google Glass was released in 2013 triggering popular interest in Augmented Reality and Virtual Reality, and Amazon released the Echo voice assistant in 2014. Around this time trust management aspects of IoT started to receive more attention [6] as did the intersection between IoT and social networks [7]. IoT Architectures in 2015 were mainly layer-oriented, separating sensing/ perception from communication and (centralized) processing [8], and security threats were also categorized by these layers[9]. By 2016, IoT device deployments we sufficiently large to form an attractive target for malware attacks (e.g., Mirai malware) and  Blockchain started to come into the IoT conversation [10]. Blockchain capabilities like immutability, transparency, auditability, data encryption, and operational resilience have been proposed to solve many architectural shortcomings of early IoT systems [11].

IoT architectures seem to be adopting blockchains to leverage advantages from decentralization, security/ trust models and enablement of new business models providing greater user control of the IoT data [12], [13]. Centralized IoT architectures have enabled users to surrender their data to others in exchange for IoT services; blockchain technologies enable more nuanced controls on data usage and offer possibilities of commercial microtransactions thus enabling new business models.  Existing IoT business models have been analyzed across multiple dimensions (e.g. [14]), but the impact of additional blockchain capabilities was not considered. With blockchains’ roots in cryptocurrencies, they can also be used to facilitate microtransactions and other trading activities in the IoT applications (e.g., smart-grid energy trading & settlement [15]). IoT architectures relying on centralized servers are vulnerable to failures and Denial of service attacks on a single point. IoT architectures are characterized by massive quantities of nodes, with the law of large numbers ensuring that some portion of the nodes is impacted by limited or intermittent connectivity, power or other faults. Blockchains based on redundant peer-peer infrastructure provide some degree of resiliency in the face of failure. Centralized IoT architectures rely on trusting a third party to handle the data, and typically do not support assurances against the life cycle of data integrity (e.g. data tampering, deletion or provenance). Blockchains can provide some assurances regarding data integrity, and blockchain consensus mechanisms can provide assurances of data provenance even amongst untrusting parties, and by distributing data over a peer-peer network provide alternate mechanisms for establishing trust in the IoT ecosystem [16].  Historically, centralized IoT architectures have provided users with only limited knowledge or control over how their data may be used and by whom. Blockchains and smart contracts can provide constraints on operations permitted on the data in the blockchain.  Massive IoT deployments in centralized architectures imply substantial costs for centralized infrastructure support; in contrast, distributed peer-peer blockchain IoT architectures have no centralized servers. An IoT ecosystem has numerous vulnerabilities concerning confidentiality, privacy, and data integrity. With its cryptographic characteristics, blockchain can help in addressing security requirements in IoT [17] ([18] provides a SWOT analysis of blockchain as a mechanism to improve the security of IoTs). [19] proposed a blockchain architecture for IoT for improved privacy by distributing the data and placing it under the control of the user. [20] proposed a design for the tamper-resistant gathering, processing, and exchange of IoT sensor data (car mileage) that was intended to be scalable, efficient, and privacy-preserving. [21] prototyped a blockchain IoT leveraging the immutability properties of blockchains to preserve evidence for use in law enforcement and insurance cases. Whether viewed from the perspective of adding blockchain features to IoT, or including IoT data flows in blockchains, the integration trend of these technologies is expected to continue.

The IoT encompasses a broad range of sensors, systems, and services that tend to be optimized for (or fragmented into) specific applications. [22] provides an overview of the scope of IoT across the perspectives of multiple taxonomies to identify the main dimensions used to characterize IoT systems. Most of the literature focused on the IoT “things”, their communication patterns and to a lesser extent, the data made available by the IoT system; complete treatments of all the potential elements of IoT systems or all the quality dimensions of IoT systems have typically not been provided. Given the breadth of IoT, not every IoT deployment requires a blockchain – IoT applications with multiple independent, interacting entities that do not have a shared trusted authority are more suitable for blockchains [23]. The scale, connectivity and transaction patterns of IoT architectures are not the same as cryptocurrency applications that blockchains were initially deployed in. Blockchains designed for other purposes may not have the inherent characteristics required for IoT [24]. IoT devices are typically resource-constrained (e.g. RFIDs have no computational elements), and blockchains involve computation heavy cryptographic functions; blockchains have, however, been demonstrated in the context resource-limited computing nodes such as the Raspberry Pi [25]. Blockchain also appears to be adopting IoT as a key use case, with increasing numbers of publications focused on the topic [26].  While blockchains and smart contracts can provide interesting features to IoT architectures, they may need optimization for the IoT context, and don’t necessarily address all of the emerging IoT requirements in areas such as privacy. With billions of IoT devices already deployed, existing IoT architectures may need to be adapted to support blockchain capabilities. Developers of new IoT architectures should consider whether to include blockchain capabilities. While new blockchain technologies optimized for IoT are emerging, existing blockchain deployments may also need to consider the impacts of IoT data flows on their infrastructure (e.g., address space consumption, transaction performance, etc.). Smart contracts may provide a path to ease the integration of IoT data on blockchains while enabling new capabilities (e.g. control loops or transactions triggered by IoT sensor data).

If you are looking for a book that provides a detailed overview of the legal implications of blockchain technology and smart contracts, then “Blockchains, Smart Contracts, and the Law” is the perfect choice for you. This book is written clearly and concisely, making it easy to understand even for those who are new to the topic.

References

[1] K. Ashton, “That ‘Internet of Things’ Thing”, RFID Journal, June 2009

[2] N. Gershenfeld, “When things start to think”, Henry Holt & Co, 1999. ISBN 0805058745

[3] ITU “ITU Internet Reports 2005” The Internet of Things”,  2005

[4] L. Atzori, et. al., “The internet of things: A survey.” Computer networks 54.15 (2010): 2787-2805.

[5] I. Ishaq, et. al., “IETF standardization in the field of the internet of things (IoT): a survey.” Journal of Sensor and Actuator Networks 2.2 (2013): 235-287.

[6] Z. Yan, et. al., “A survey on trust management for Internet of Things.” Journal of network and computer applications 42 (2014): 120-134.

[7] A. Ortiz, et. al., “The cluster between internet of things and social networks: Review and research challenges.” IEEE Internet of Things Journal 1.3 (2014): 206-215.

[8] S. Madakam, et. al., “Internet of Things (IoT): A literature review.” Journal of Computer and Communications 3.05 (2015): 164.

[9] E. Leloglu,  “A review of security concerns in Internet of Things.” Journal of Computer and Communications 5.1 (2016): 121-136.

[10] M. Conoscenti, et. al., “Blockchain for the Internet of Things: A systematic literature review.” 2016 IEEE/ACS 13th Int’l Conf. of Computer Systems and Applications (AICCSA). IEEE, 2016.

[11] A. Panarello, et. al., “Blockchain and iot integration: A systematic survey.” Sensors 18.8 (2018): 2575.

[12] M. Ali, et. al. “Applications of blockchains in the Internet of Things: A comprehensive survey.” IEEE Communications Surveys & Tutorials 21.2 (2018): 1676-1717.

[13] R. Thakore,  et al. “Blockchain-based IoT: A Survey.” Procedia Computer Science 155 (2019): 704-709.

[14] D. Hodapp, et. al., “Business Models for Internet of Things Platforms: Empirical Development of a Taxonomy and Archetypes.” AIS: 14th Int’l Conf. on Wirtschaftsinformatik, Feb. 24-27, 2019, Siegen, Germany

[15] M. Andoni, et. al., “Blockchain technology in the energy sector: A systematic review of challenges and opportunities.” Renewable and Sustainable Energy Reviews 100 (2019): 143-174.

[16] B. Yu, et. al. “IoTChain: Establishing trust in the internet of things ecosystem using blockchain.” IEEE Cloud Computing5.4 (2018): 12-23.

[17] M. Khan, et.al., “IoT security: Review, blockchain solutions, and open challenges.” Future Generation Computer Systems 82 (2018): 395-411.

[18] S. Moin, et. al. “Securing IoTs in distributed blockchain: Analysis, requirements and open issues.” Future Generation Computer Systems 100 (2019): 325-343.

[19] M. Ali, et.al., “IoT data privacy via blockchains and IPFS.” Proceedings of the Seventh International Conference on the Internet of Things. ACM, 2017.

[20] M. Chanson, et al. ,”Blockchain for the IoT: privacy-preserving protection of sensor data.” Journal of the Association for Information Systems 20.9 (2019): 10.

[21] D. Billard, et. al., “Digital Forensics and Privacy-by-Design: Example in a Blockchain-Based Dynamic Navigation System.” Annual Privacy Forum. Springer, Cham, 2019.

[22] F. Alkhabbas, et. al., “Characterizing Internet of Things Systems through Taxonomies: A Systematic Mapping Study.” Internet of Things7 (2019): 100084.

[23] N. El Ioini, et.al., “A decision framework for blockchain platforms for IoT and edge computing.” SCITEPRESS, 2018.

[24] R. Han, et.al., “Evaluating blockchains for iot.” 2018 9th IFIP International Conference on New Technologies, Mobility and Security (NTMS). IEEE, 2018.

[25] A. Reyna, et. al., “On blockchain and its integration with IoT. Challenges and opportunities.” Future Generation Computer Systems 88 (2018): 173-190.

[26] A. Firdaus, et al., “The rise of “blockchain”: bibliometric analysis of blockchain study.” Scientometrics 120.3 (2019): 1289-1331.

Blockchain and Smart contracts have evolved out of the technology underlying and popularized by bitcoin. So how widespread are these concepts? Have they reached the public awareness or are these merely niche technologies? Google Trends provides one perspective based on search queries which shows much greater search interest and therefore awareness of “Bitcoin” than “Blockchain” or “Smart Contracts”. It may also reflect the maturity and scale of bitcoin commercial offerings with multiple cryptocurrency exchanges in operation globally. In contrast, Blockchains and Smart Contracts appear to be at an earlier stage of development and commercialization as well as being targeted towards markets that are less mass market and more niche industrial applications (e.g. tracking supply chain provenance for pharmaceuticals).

The search terms “Bitcoin”, Blockchain” and “Smart Contract” all have a similar global spread, with peak search volumes coming, perhaps surprisingly, from Africa. Peak search volumes were associated with bitcoin price queries as might be expected. The results for “Smart Contract” also indicated related queries associated with mobile phones. This may reflect some different interpretations of the phrase (e.g. advertising for mobile phone subscription contracts) or perhaps an interest in access to bitcoins and blockchain smart contracts through wallets on mobile devices.

The Gartner Hype Cycle for Emerging technologies provides a perspective on perceived technology maturity. Newly emerging technologies are posited to go through stages from being an “innovation trigger” to the “Peak of Inflated Expectations” then through the “Trough of Disillusionment”, and up the “Slope of Enlightenment” to finally reach a “Plateau of Productivity”. The Gartner Hype Cycle 2016 identified “Blockchain as nearing the “Peak of Inflated Expectations”. The Gartner Hype Cycle 2017 identified “Blockchain” as about to cross between the “Peak of Inflated Expectations” and the “Trough of Disillusionment”. The Gartner Hype Cycle 2018 maintained “Blockchain” as about to cross between the “Peak of Inflated Expectations” and the “Trough of Disillusionment”. It also split out “Blockchain for Data Security” as being in the “Innovation Trigger” stage. The Gartner Hype Cycle 2019 does not list Bitcoin, Blockchain or Smart Contracts, but it does call out “Decentralized Autonomous Organizations” (DAOs) as being in the “Innovation Trigger” stage. DAOs may be considered as LegalTech – prototype legal entities associated with blockchain smart contracts. Gartner’s 2019 Hype Cycle for Blockchain Technologies provides a more detailed perspective. While the more generic term “blockchain” is sliding into the trough, smart contracts, decentralized identities, and consensus mechanisms are at the peak; zero-knowledge proofs, privacy-enhanced multiparty computing, and smart contract oracles are on the rise.

Bitcoin has moved into the mass market vocabulary and seems to be providing some operational utility as a financial asset with many searches for bitcoin prices. Blockchain applications beyond cryptocurrency are often not mass-market applications. Blockchain Loyalty Programs would target mass-market consumer awareness but even these have limitations of scale compared to cryptocurrencies. Industrial applications of blockchains, in supply chains, for example, would not reach consumer awareness to trigger searches.

Blockchains are hashed linked data structures replicated over a peer to peer network. In considering blockchain topologies we need to distinguish between the topology of the peer to peer network and the topology of the blockchain data structure.

Peer-Peer networks became prominent with the file-sharing application pioneered by Napster in 1999. File sharing was popular with many consumers sharing music or video files; however, it was much less popular with various copyright holders whose content was being shared without permission, and Napster eventually closed in 2001. File sharing continued with Gnutella, BitTorrent, and, others, though the underlying technology architectures evolved[1]. The node connectivity could be structured or unstructured. The files being shared could be centralized or distributed. Centralized file structures created a point of attack for opponents of file sharing, as did regular structured topologies. Peer – peer applications moved beyond file sharing with communications services like Skype.  

The nodes in peer-peer networks are not all completely meshed – each node is connected to a limited (and different!) set of peers.  Typically, less than 16 peers are sufficient[2] for the content to propagate through the peer-peer network, though specific performance with obviously be impacted by the processing power and bandwidth available to the nodes, and the content sharing protocols of the particular peer-peer network. In this model, nodes are also not required to be permanently connected – as long as some porting of the network remains active, new nodes can be connected, and the content propagates.

Permissionless blockchain systems rely on an unstructured public P2P network for information dissemination between participating peers. Flooding or gossip protocols are then used for the propagation of the required information to all peers so that the blockchain consensus protocols have the information they need. At design time, the node attachment and communication strategies that impact the topology of the network in operation are fixed. While a complete peer-peer network is not easily observable, these network characteristics are known to adversaries and can be targeted for attacks. The users of these permissionless blockchain networks have requirements[3] for their applications that typically include aspects such as performance, low participation cost, topology hiding, Denial of Service (DoS) resistance and anonymity. The tradeoffs between the implementation choices for these requirements are not well understood, and further work in these areas is expected to help improve the maturity of blockchain solutions.

[1] For a summary of file sharing approaches see, Masood, Saleha, et al. “Comparative Analysis of Peer to Peer Networks.” International Journal of Advanced Networking and Applications 9.4 (2018): 3477-3491

[2] For an example study on BitTorrent performance, see, Bharambe, Ashwin R., Cormac Herley, and Venkata N. Padmanabhan. “Analyzing and improving a BitTorrent network’s performance mechanisms.” Proceedings IEEE INFOCOM 2006. 25TH IEEE International Conference on Computer Communications. IEEE, 2006.

[3] Neudecker, Till, and Hannes Hartenstein. “Network layer aspects of permissionless blockchains.” IEEE Communications Surveys & Tutorials 21.1 (2018): 838-857.

Blockchain technologies are seen by many as a key infrastructure component enabling a wide variety of new applications – from Accounting applications like share registries, Biotech blockchains, Cryptocurrencies and down through the rest of the alphabet. While many claims are made for blockchains, the resilience of an infrastructure based on a peer-peer network operating autonomously of centralized actors is seen as key for what seems to be emerging as an infrastructure software layer for many fintech applications, if not the wider Internet.  While there are multiple blockchain architectures; beyond the peer-peer infrastructure and the blockchain data structure itself, many blockchains support a distributed applications layer of dApps or Smart contracts executing on the underlying blockchain infrastructure. Blockchain appears poised for wider adoption with open-source implementations available, large scale existing deployments in cryptocurrency mining and large commercial entities reportedly exploring and, in some cases, deploying the technology.  But is the technology really mature enough for wide-scale public use?

Adoption of a new technology can be limited by readiness or maturity issues in the operational processes using the new technology, the staff driving those processes, or the development of the blockchain itself.  Process maturity is typically measured with a 5-point scale such as:

1. Initial               (not under statistical process control)
2. Repeatable     (the organization has a stable process with repeatable levels of statistical process control and rigorous project management)
3. Defined           (the process is defined for consistent implementation)
4. Managed         (the process is comprehensively measured and analyzed)
5. Optimizing      (the process is continuously improved)   

These five levels have been adapted for use in a number of different industries. The blockchain software components (peer-peer network, blockchain data structure, consensus protocols, etc.) could be evaluated on such a scale. In a similar fashion, the operational context (market, regulation, consumer/ operator use-cases, etc.) could also be evaluated. Blockchains are inherently distributed applications (otherwise a centralized database could be used).  With distributed applications, multiple actors are involved.  Multiple independent human actors add complexity to process evaluations because their individual evaluations of the process maturity may be different, and their understanding of the expected operational use-cases may also differ. While there have been proposals[1] for blockchain maturity models, it is not clear how widely supported they are.  

To err is human, and the open-source blockchain developers have demonstrated their humanity in a number of ways[2]. What matters more is the process for resolving those inevitable bugs. One approach to tracking maturity, particularly for open source projects is the core infrastructure initiative  (CII) from the Linux Foundation.  This provides not just tooling and education, but also a (free) badging program for open source projects to attest to their adherence to industry best practices. CII is not restricted to Linux Foundation projects;  but as might perhaps be expected,  Hyperledger projects do report on CII; unfortunately, Etherium does not; though there are a number of other blockchain projects that do.

If blockchain systems and technologies are to live up to their promise as future infrastructure, then their maturity needs to be demonstrated. Developers and open source communities have tools like CII to demonstrate the maturity of their software. Users of blockchain software should ask their suppliers for evidence of the maturity of their products. Beyond the software, other aspects (e.g., market and regulatory dimensions) may need industry-specific adaptions of the process maturity scale to evaluate the operability of blockchain proposals in their context.

[1] See e.g., Wang, H., Chen, K. & Xu, D. Financ. Innov. (2016) 2: 12. https://doi.org/10.1186/s40854-016-0031-z

[2] See e.g., Wan, Z., Lo, D., Xia, X., & Cai, L. (2017, May). Bug characteristics in blockchain systems: a large-scale empirical study. In 2017 IEEE/ACM 14th International Conference on Mining Software Repositories (MSR) (pp. 413-424). IEEE.