By fully exploring various sensing capabilities and multiple wireless interfaces of mobile devices and integrating them with human power and intelligence, mobile crowdsourcing (MCS) is emerging as an effective paradigm for large-scale multimedia-related applications, say the authors of “Word of Mouth Mobile Crowdsourcing: Increasing Awareness of Physical, Cyber, and Social Interactions.”
However, most MCS schemes use a direct mode, in which crowd workers passively or actively select tasks and contribute without interacting and collaborating with each other. Such a mode can hamper some time-constrained crowdsourced tasks.
Researchers from universities in China, Japan, and Sweden execute a different approach: MCS based on word of mouth (WoM), in which crowd workers, apart from executing tasks, exploit their mobile social networks and/or physical encounters to actively recruit other appropriate individuals to work on the task.
They describe a WoM-based MCS architecture and typical applications, which they divide into Internet-scale and local scale. They then systematically summarize the main technical challenges, including crowd worker recruitment, incentive design, security and privacy, and data quality control, and compare typical solutions. Finally, from a systems-level viewpoint, they discuss several practical issues that must be resolved.