| Summary: | <p>The exploding growth of wireless devices like smartphones and tablets has driven the
emergence of various applications, which has exacerbated the congestion over current
wireless networks. Noticing the limitation of current wireless network architectures and
the static spectrum policy, in this dissertation, we study a novel hybrid network architecture,
called multihop cognitive cellular network (MC<sup>2</sup>N), taking good advantage of both
local available channels and frequency spatial reuse to increase the throughput of the network,
enlarge the coverage area of the base station, and increase the network scalability.
Although offering significant benefits, the MC<sup>2</sup>N also brings unique research challenges
over other wireless networks. Of note are the problems associated with the architecture,
modeling, cross-layer design, privacy, and security issues.</p>
<p>In this dissertation, we aim to address these challenging and fundamental issues in
MC<sup>2</sup>Ns. Our contributions in this dissertation are multifold. First, we consider multiradio
multi-channel in MC<sup>2</sup>Ns and propose a multi-radio multi-channel multi-hop cognitive
cellular network (M<sup>3</sup>C<sup>2</sup>N). Under the proposed architecture, we then investigate the
minimum length scheduling problem by exploring joint frequency allocation, link scheduling,
and routing. Second, energy consumption minimization problem is further studied for
MC<sup>2</sup>N under physical model. Third, we introduce device-to-device (D2D) communications
among cellular users in MC<sup>2</sup>Ns by bypassing the base stations (BSs) and utilizing
local available spectrums, and hence potentially further alleviate network congestion. A
secondary spectrum auction market is constructed to dynamically allocate the available licensed
spectrums. Fourth, we propose realtime detection, defense, and penalty schemes to
identify, defend against, and punish MAC layer selfish misbehavior, respectively, in multihop
IEEE 802.11 networks, noticing that most traditional detection approaches are for
wireless local area networks only, and rely on a large amount of historical data to perform
statistical detection. Last, a new location-based rewarding system, called LocaWard
is proposed, where mobile users can collect location-based tokens from token distributors,
and then redeem their gathered tokens at token collectors for beneficial rewards. Besides,
we also develop a security and privacy aware location-based rewarding protocol for the
LocaWard system.</p>
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