Doctoral Dissertations

Keywords and Phrases

Crowdsensing; Information; Networks; Optimization; Quality; Trust


"Given its potential for a large variety of real-life applications, smartphone crowdsensing has recently gained tremendous attention from the research community. Smartphone crowdsensing is a paradigm that allows ordinary citizens to participate in large-scale sensing surveys by using user-friendly applications installed in their smartphones. In this way, fine-grained sensing information is obtained from smartphone users without employing fixed and expensive infrastructure, and with negligible maintenance costs.

Existing smartphone sensing systems depend completely on the participants' willingness to submit up-to-date and accurate information regarding the events being monitored. Therefore, it becomes paramount to scalably and effectively determine, enforce, and optimize the information quality of the sensing reports submitted by the participants. To this end, mechanisms to improve information quality in smartphone crowdsensing systems were designed in this work. Firstly, the FIRST framework is presented, which is a reputation-based mechanism that leverages the concept of "mobile trusted participants" to determine and improve the information quality of collected data. Secondly, it is mathematically modeled and studied the problem of maximizing the likelihood of successful execution of sensing tasks when participants having uncertain mobility execute sensing tasks. Two incentive mechanisms based on game and auction theory are then proposed to efficiently and scalably solve such problem. Experimental results demonstrate that the mechanisms developed in this thesis outperform existing state of the art in improving information quality in smartphone crowdsensing systems"--Abstract, page iii.


Das, Sajal K.

Committee Member(s)

Chellappan, Sriram
Saifullah, Abu Sayeed
Silvestri, Simone
Zawodniok, Maciej Jan, 1975-


Computer Science

Degree Name

Ph. D. in Computer Science


Missouri University of Science and Technology

Publication Date

Fall 2016


ix, 93 pages

Note about bibliography

Includes bibliographic references (pages 83-92).


© 2016 Francesco Restuccia, All rights reserved.

Document Type

Dissertation - Open Access

File Type




Subject Headings

Information storage and retrieval systems
Information retrieval -- Mathematical models
Application software

Thesis Number

T 11051

Electronic OCLC #