Page 27 - Artificial Intelligence for the Internet of Everything

14 Artificial Intelligence for the Internet of Everything

Security and Energy Technology and Blockchain Lead at the Pacific North-
west National Laboratory (PNNL) in Richland, WA. In his chapter, Mylrea
writes that blockchain technology combines cryptography and distributed
computing with a more secure multiparty consensus algorithm to reduce
the need for third-party intermediaries (e.g., bankers, meter readers, accoun-
tants, lawyers, etc.). Blockchain helps securely automate exchanges of value
between parties in a more efficient and secure way that, he predicts, may give
impetus to organizations known as “Distributed Autonomous Energy Orga-
nizations” (DAEO). In this chapter, the author applies the blockchain
research that he developed while he was at the Pacific Northwest National
Laboratory in combination with a new theoretical approach that allows
him and others to explore how blockchain technology might be able to
help users to construct a more distributed, more autonomous, and more
cyber-resilient energy organization that can more autonomously respond
to evolving cyber-physical threats. In the face of these threats, blockchain
may help increase the resiliency and efficiency of energy utilities by linking
producers securely with consumers and to create a new class of consumers
combined with producers known as “prosumers.” DAEO will give prosu-
mers increased flexibility and control of how they consume and exchange
energy and trade energy credits while securing data from the critical com-
munications required for these complex transactions. The author offers an
innovative approach for more autonomous and secure energy transactions
that replaces third-party intermediaries with blockchain technology in a
way that could potentially increase the efficiency and resilience of electric
utilities. Blockchain-enabled distributed energy markets may unlock new
value, empowering “prosumers” and replacing some third-party intermedi-
aries with a distributed ledger consensus algorithm. However, many block-
chain regulatory, policy, and technology obstacles ahead could potentially
challenge this innovative change for the energy sector.
Chapter 13, titled “Compositional Models for Complex Systems,” was
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written by Spencer Breiner, Ram D. Sriram, and Eswaran Subrahmanian
of the National Institute of Standards and Technology (NIST), Information
Technology Lab, Gaithersburg, MD. Breiner is a specialist in graphical
methods in the CyberInfrastructure Group at NIST; Sriram is currently
the Chief of the Software and Systems Division of the Information Tech-
nology Laboratory at NIST; Subrahmanian, a Fellow of the American
Association of Advancement of Science (AAAS), is also part of the

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Corresponding author: spencer.breiner@nist.gov.