The aim of this book, the first of two volumes, is to present selected research that
has been undertaken under COST Action IC0902 ‘‘Cognitive Radio and Net-
working for Cooperative Coexistence of Heterogeneous Wireless Networks’’
(http://newyork.ing.uniroma1.it/IC0902/). COST (European Cooperation in Sci-
ence and Technology) is one of the longest-running European frameworks sup-
porting cooperation among scientists and researchers across Europe.
The idea of writing this book entitled “Cognitive Networked Sensing and Big Data”
started with the plan to write a briefing book on wireless distributed computing
and Cognitive sensing. During our research on large-scale Cognitive radio network
(and its experimental testbed), we realized that big data played a central role. As a
result, the book project reflects this paradigm shift. In the context, sensing roughly
is equivalent to “measurement.”
This introduction takes a visionary look at ideal Cognitive radios (CRs) that inte-
grate advanced software-defined radios (SDR) with CR techniques to arrive at
radios that learn to help their user using computer vision, high-performance
speech understanding, global positioning system (GPS) navigation, sophisticated
adaptive networking, adaptive physical layer radio waveforms, and a wide range
of machine learning processes.
Cognitive radio has emerged as a promising technology for maximizing the utiliza-
tion of the limited radio bandwidth while accommodating the increasing amount of
services and applications in wireless networks. A Cognitive radio (CR) transceiver
is able to adapt to the dynamic radio environment and the network parameters to
maximize the utilization of the limited radio resources while providing flexibility in
wireless access. The key features of a CR transceiver are awareness of the radio envi-
ronment (in terms of spectrum usage, power spectral density of transmitted/received
signals, wireless protocol signaling) and intelligence.
Cognitive radios have become a vital solution that allows sharing of the scarce
frequency spectrum available for wireless systems. It has been demonstrated
that it can be used for future wireless systems as well as integrated into 4G/5G
wireless systems. Although there is a great amount of literature in the design of
Cognitive radios from a system and networking point of view, there has been very
limited available literature detailing the circuit implementation of such systems.
Our textbook, Radio Frequency Integrated Circuit Design for Cognitive Radios, is
the first book to fill a disconnect in the literature between Cognitive Radio systems
and a detailed account of the circuit implementation and architectures required to
implement such systems. In addition, this book describes several novel concepts
that advance state-of-the-art Cognitive radio systems.
Resource allocation is an important issue in wireless communication networks. In
recent decades, Cognitive radio technology and Cognitive radio-based networks have
obtained more and more attention and have been well studied to improve spectrum
utilization and to overcomethe problem of spectrum scarcity in future wireless com-
munication systems. Many new challenges on resource allocation appear in cogni-
tive radio-based networks. In this book, we focus on effective solutions to resource
allocation in several important Cognitive radio-based networks, including a cogni-
tive radio-basedopportunisticspectrum access network, a Cognitiveradio-basedcen-
tralized network, a Cognitive radio-based cellular network, a Cognitive radio-based
high-speed vehicle network, and a Cognitive radio-based smart grid.
In this paper, we describe the development of a mobile butterfly-watching learning (BWL)
system to realize outdoor independent learning for mobile learners. The mobile butterfly-watching
learning system was designed in a wireless mobile ad-hoc learning environment. This is first result
to provide a Cognitive tool with supporting the independent learning by applying PDA with
wireless communication technology to extend learning outside of the classroom. Independent
learning consists of self-selection, self-determination, self-modification, and self-checking.
