Page 160 - Artificial Intelligence for the Internet of Everything

146 Artificial Intelligence for the Internet of Everything

immediate help with pragmatic concerns that exist in the Internet of Things
(IoT) where information is plentiful and can also be excessive.
The nature of the IoT is one of pervasive data, continuously gathered
and acted on by fully or semiautonomous devices and systems. This nature
creates an interesting paradox in the context of VoI. If the IoT ushers
in unimaginable volumes of information and one considers value as an
economic construct, should not the “value” of information decrease?
Perhaps in the average sense, for example, all information’s overall value
may decrease, but certain information would still retain a value higher than
most. This notion calls into question how applicable existing VoI theory
would be in the context of IoT information and related decision making.
Moreover, the implications of an intelligent IoT system of systems, which
the IoT is, in implementation and operation, introduce another complicat-
ing factor toward a generalized theory of VoI. The intelligence in the IoT
itself necessarily makes VoI determinations in its autonomous operation
on behalf of human decision makers. In this manner, the IoT itself is
imbued with its own AI, that manifests as self-star (self-*)behaviors.
Self-* behaviors are (Babaoglu et al., 2005) autonomic behaviors (such
as self-management, self-awareness, self-protecting, etc.) that provide a
device or system with an understanding of its contribution (or value) to
global, greater, or external objectives/goals. The concept of the IoT’s
AI brings additional constraints to understanding VoI, given such a perva-
sive information system. Like the limitations of Shannon’s information
theory (Shannon, 1956), these considerations also create a fundamental
issue for a solely quantitative theory of information’s applicability to
IoT decision making.
We attempt to address this issue by examining a VoI theory in the con-
text of information provided by the IoT. Our thinking is grounded in the
work of Ponssard (1975) and especially Howard (1966). These works dis-
cuss how VoI is part of decision analysis. We attempt to make an optimal
decision, based upon expected utility/value. Howard (1966) discusses how
a company decides how much to bid on a contract based upon the a priori
information it has available. In this situation, the company attempts to
maximize its expected profit. We note though that we disagree with
how Howard obtained his “clairvoyant” results in the situation when addi-
tional information is available to the decision maker. AI plays a major role
in any consideration of the VoI because techniques, such as machine learn-
ing, can distill additional information from the IoT, which can be used by a
decision maker.