Device-to-device (D2D) is a new paradigm in cellular networks that enhances network performance by introducing increased spectral efficiency and reduced communication delay. Efficient data dissemination is indispensable for supporting many D2D applications such as content distribution and location-aware advertisement. In this work, I investigate a new and interesting data dissemination problem where the receivers are not explicitly known and data must be disseminated to the receivers within a probabilistic delay budget. I propose to exploit data depositories, which can temporarily house data and deliver them to interested receivers upon requests. I formally formulate the delay-constrained profit maximization problem for data deposition in D2D networks and show its NP-hardness. Under the unique mobile opportunistic network setting, a practical solution for such problem must be distributed, localized, and online. To this end, I introduce three algorithms for Direct Online Selection of 1-Depository, Direct Online Selection of L-Depositories, and Mixed Online Selection of L-Depositories. To demonstrate and evaluate the system, I implement a prototype using Google Nexus handsets and conduct experiments for five weeks. I further carry out simulations based on real-world mobility traces for evaluation of large-scale networks and various network settings that are impractical to experiment.
|Commitee:||Bayoumi, Magdy A., Perkins, Dimitri, Tzeng, Nian-Feng|
|School:||University of Louisiana at Lafayette|
|School Location:||United States -- Louisiana|
|Source:||DAI-B 78/04(E), Dissertation Abstracts International|
|Keywords:||Cellular networks, Device-to-device networks, Mobile opportunistic networks|
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