  Current research on Internet of Things (IoT) mainly focuses on how to enable
general objects to see, hear, and smell the physical world for themselves, and
make them connected to share the observations. In this paper, we argue that
only connected is not enough, beyond that, general objects should have the
capability to learn, think, and understand both the physical world by
themselves. On the other hand, the future IoT will be highly populated by large
numbers of heterogeneous networked embedded devices, which are generating
massive or big data in an explosive fashion. Although there is a consensus
among almost everyone on the great importance of big data analytics in IoT, to
date, limited results, especially the mathematical foundations, are obtained.
These practical needs impels us to propose a systematic tutorial on the
development of effective algorithms for big data analytics in future IoT, which
are grouped into four classes: 1) heterogeneous data processing, 2) nonlinear
data processing, 3) high-dimensional data processing, and 4) distributed and
parallel data processing. We envision that the presented research is offered as
a mere baby step in a potentially fruitful research direction. We hope that
this article, with interdisciplinary perspectives, will stimulate more
interests in research and development of practical and effective algorithms for
specific IoT applications, to enable smart resource allocation, automatic
network operation, and intelligent service provisioning.
