The large scale and powerful smart devices make the mobile crowdsensing applications which leverage public crowd equipped with various mobile devices for large scale sensing tasks be possible.
One is the scheduling of data, including aggregation and extraction of large scale sensing data [5 8] and time-continuous data generation [9].
The system consists of many static sensors, scattered in a large scale sensing field and multiple mobile sinks, cruising among the clusters.
When the observation of natural cycles played a greater role in people's awareness of temporality, change was "softer," less precisely calculable, and intimately tied to a more fluid and large-scale sense of rhythm.
In both commercial industry and academia, significant progress has been made on problems related to the analysis of community structure; however, traditional work in social networks has focused on static situations (i.e., classical social network analysis) or dynamics in a large-scale sense (e.g., disease propagation) [2].
While sex toy recycling still hasn't caught on in any meaningful large-scale sense, there are a handful of purveyors that are taking steps to address the issue for both practical business concerns and environmental considerations.
Research shows that co-operative sensing methods can aid in significant data reduction in large-scale sensing infrastructures [58].
Wireless body sensor networks are expected to extend human-centered applications in large-scale sensing and detecting environments.
Participatory smartphone sensing has lately become more and more popular as a new paradigm for performing large-scale sensing, in which each smartphone contributes its sensed data for a collaborative sensing application.
Mobile crowdsensing can be formally defined as a large-scale sensing paradigm in which spatially distributed participants with sensing and computing devices capture and collectively share data in order to measure and map phenomena of common interest [3].
The phenomenal growth in the richness and diversity of sensors on smart devices [3], combined with the inherent mobility of mobile device users provide a unique opportunity to harvest large-scale sensing data with fine-grained spatio-temporal coverage [1, 4].
