Page 180 - Artificial Intelligence for the Internet of Everything

166 Artificial Intelligence for the Internet of Everything

Vol

Descriptor End-use
context context

Content Provenance

Relevance Integrity Timeliness Presentation Trust

Spatiotemporal Accuracy
relevance

Thematic-
relevance

Fig. 9.10 Volume of Information attribute taxonomy. (Adapted from Bisdikian, C.,
Kaplan, L. M., & Srivastava, M. B. (2013). On the quality and value of information in
sensor networks. ACM Transactions on Sensor Networks (TOSN), 9(4), 48.)

9.9 CONCLUSION

IoT will provide a rich environment, supplying VOIs for nearly every aspect
of humans’ activities and environments. The IoT will gain ever increasing
amounts of AI that will only provide greater degrees of autonomic capabil-
ities and self-star behaviors. This AI-enriched IoT environment will change
the fundamental notions of information value for decision making by pro-
ducing huge quantities of information that are managed by AI functionality.
Like Shannon’s information theories, our understanding of VoI theory will
implicitly go beyond just a quantitative concept to include qualitative
notions. However, there is surprisingly little literature that examines VoI
in the context of the IoT. In this chapter, we have extended Howard’s
(1966) VoI theory to examine a generalization of that notion toward a guar-
antee of a minimal value.
We presented a rework of Howard’s theoretical problem and solution
identifying some limitations in his treatment of a random variable, relative
to VoI. Howard’s idea of clairvoyance, or insight into future information
(and its value) treats the value of the random variable deterministically rather
than probabilistically. By giving the random variable a probabilistic context,