Crowdsourcing is emerging as a new data-collection, solution-finding, and opinion-seeking model that obtains needed services, ideas, or content by soliciting contributions from a large crowd of public participants. D2D based crowdsensing is particularly desired when the initiator cannot directly reach out to the participants or the conventional approaches for data transportation are costly. This dissertation studies the ultra-large scale crowdsensing applications in such mobile D2D networks. First, I proposed and addressed MCC(Minimum-Cost Crowdsourcing) problem by exploring a multi-dimensional design space to seek an optimal solution that minimizes the total crowdsensing cost while satisfying the coverage probability over the FoI. In particular, three strategies (or options) are in consideration: task allocation strategy, data processing strategy and computation offloading strategy. The difficulty is to determine the three options for each node in order to minimize the overall system cost. Second, there are a class of applications, where the originator is only allowed to recruit a given number of participants. Therefore, from the perspective of limited participants, we proposed a competition based participant recruitment mechanism to wisely choose the set of nodes while achieving the best benefit. I have proposed a dynamic programming algorithm as a first attack to this problem, followed by two distributed alternatives, which prove to be more practical and adaptive. During the above two topics, we find the existing routing protocols cannot efficiently support the ultra-large scale crowdsensing, thus we built a resource constrained routing protocol in D2D, aiming to approach the large-scale, bandwidth-hungry crowdsensing task in a more efficient way. With the requirement of restricted node storage and link bandwidth as well as end-to-end delay, I formulated a non-linear traffic allocation optimization problem with an approximation algorithm and distributed heuristic solution. Finally, I have carried out extensive complexity analysis, simulation, prototyping and implementation, experimentation and performance evaluation. Through the step-by-step exploration and verification, I have demonstrated the efficiency of the proposed heuristics and revealed empirical insights into the design tradeoffs and practical considerations in D2D-based crowdsourcing.
|Commitee:||Bayoumi, Magdy A., Perkins, Dimitri, Tzeng, Nian-Feng, Wu, Hongyi|
|School:||University of Louisiana at Lafayette|
|School Location:||United States -- Louisiana|
|Source:||DAI-B 79/01(E), Dissertation Abstracts International|
|Keywords:||Crowdsensing, Device-to-device network, Opportunistic, Optimization, Prototype|
Copyright in each Dissertation and Thesis is retained by the author. All Rights Reserved
The supplemental file or files you are about to download were provided to ProQuest by the author as part of a
dissertation or thesis. The supplemental files are provided "AS IS" without warranty. ProQuest is not responsible for the
content, format or impact on the supplemental file(s) on our system. in some cases, the file type may be unknown or
may be a .exe file. We recommend caution as you open such files.
Copyright of the original materials contained in the supplemental file is retained by the author and your access to the
supplemental files is subject to the ProQuest Terms and Conditions of use.
Depending on the size of the file(s) you are downloading, the system may take some time to download them. Please be