THESIS
2016
xiii, 96 pages : illustrations ; 30 cm
Abstract
With the development of wireless networks and the proliferation of mobile devices, mobile crowdsourcing (MCS) has enabled us to collect and analyze real-word data with unprecedented coverage and intelligence. Mobile crowdsourcing has inspired many applications and systems, bringing great convenience to research, production and people’s daily life. Yet to further exploit MCS, we still face many challenges ranging from mechanism design to system implementation. In this thesis, we address three challenging topics in mobile crowdsourcing. They are task management for quality control, incentive mechanism design and mobile apps development during MCS system implementation. For each topic, we focus on certain scenarios and specific problems.
In the first work, we consider the quality-aware on...[
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With the development of wireless networks and the proliferation of mobile devices, mobile crowdsourcing (MCS) has enabled us to collect and analyze real-word data with unprecedented coverage and intelligence. Mobile crowdsourcing has inspired many applications and systems, bringing great convenience to research, production and people’s daily life. Yet to further exploit MCS, we still face many challenges ranging from mechanism design to system implementation. In this thesis, we address three challenging topics in mobile crowdsourcing. They are task management for quality control, incentive mechanism design and mobile apps development during MCS system implementation. For each topic, we focus on certain scenarios and specific problems.
In the first work, we consider the quality-aware online assignment problem for location-based tasks, which are typical in mobile crowdsourcing. After mathematically formulating the online assignment problem, a polynomial-time online assignment algorithm is designed to optimize tasks’ overall quality. The proposed algorithm is proven to approximates the offline optimal solution with a competitive ratio of
10/7.
Crowdsouced mobile network access (CMNA), or user-provided connectivity, provides more flexible and ubiquitous Internet access among mobile users. In the second work, we study the incentive mechanism for an operator-assisted CMNA model, where subscribers are incentivized by a mobile virtual network operator (MVNO) to operate as mobile WiFi hot spots. We characterize their equilibrium strategies on the basis of probabilistic analysis as a two-stage Bayesian game. A partial cooperation strategy (PCS) is designed for MVNO and subscribers to optimize their benefit, which is more practical (compared with symmetric strategy) and incurs much less overhead.
As most MCS systems rely on mobile apps on smartphones as terminals, mobile apps development is a critical part in MCS systems implementation. In the third work we target at the resource management problem during mobile apps development. We mine resource management specifications in a crowdsourced way and a tool called Automatic Resource Specification Miner (ARSM) is developed. ARSM collects the usage of resources related APIs from off-the-shelf apps, which come from the developer crowd. Then it mines frequent patterns from the gathered resource usage information. Experimental results demonstrate its efficiency and effectiveness. Furhter we develop an Eclipse plugin to detect specification violations with the patterns from ARSM. It guides software engineers during app development by pinpointing potential resource management bugs.
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