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 (MC2N), 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 MC2N 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. In this dissertation, we aim to address these challenging and fundamental issues in MC2Ns. Our contributions in this dissertation are multifold. First, we consider multiradio multi-channel in MC2Ns and propose a multi-radio multi-channel multi-hop cognitive cellular network (M3C2N). 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 MC2N under physical model. Third, we introduce device-to-device (D2D) communications among cellular users in MC2Ns 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 I 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.
| Identifer | oai:union.ndltd.org:MSSTATE/oai:scholarsjunction.msstate.edu:td-4224 |
| Date | 15 August 2014 |
| Creators | Li, Ming |
| Publisher | Scholars Junction |
| Source Sets | Mississippi State University |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Theses and Dissertations |
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