We propose a decentralized belief propagation-based method, PD-LBP, for multi-agent task
allocation in open and dynamic grid and cloud environments where both the sets of agents …

The recent paradigm of mobile crowd sensing (MCS) enables a broad range of mobile
applications. A critical challenge for the paradigm is to incentivize phone users to be workers …

Citizen science projects have been very successful at collecting rich datasets for different
applications. However, the data collected by the citizen scientists are often biased, more …

We introduce the problem of Task Assignment and Sequencing, which models online
optimization in expert crowdsourcing settings that involve non-decomposable macrotasks …

Ontology evaluation has proven to be one of the more difficult problems in ontology
engineering. Researchers proposed numerous methods to evaluate logical correctness of …

Crowd-sourcing has attracted much attention due to its growing importance to society, and
numerous studies have been conducted on task allocation and wage determination. Recent …

Crowdsourcing arose as a problem-solving strategy that uses a large number of workers to
achieve tasks and solve specific problems. Although there are many studies that explore …

To date, on-demand provisioning models of human-based services in the cloud are mainly
used to deal with simple human tasks solvable by individual compute units (ICU). In this …

Crowdsensing recently attracts great attention from both industry and academia. By fusing
and analyzing multi-dimensional sensing data collected from crowdsensing users, it is …

This article-based doctoral thesis explores the stakeholder perspectives and experiences of
crowdsourced creative work on two of the leading crowdsourcing platforms. The thesis has …