Pricing Mechanisms for Crowd-Sensed Spatial-Statistics-Based Radio Mapping

Remotely Sensed Data Segmentation under a Spatial Statistics Framework

Spatial indexing in agent-based crowd simulation

Crowd modeling and simulation has become a critical tool for understanding crowds and predicting their behaviours. This is accomplished by modelling the characteristics and behaviours of large groups of people, as well as their interactions. Agent-based crowd simulation may involve thousands of complex agents interacting in sophisticated ways, in close spatial proximity, with each other. A key ...

Spatial Statistics for Content Based Image Retrieval

A Content Based Image Retrieval (CBIR) system retrieves relevant images from an image database. Over the years, several methods have been proposed to extract these features. Previous researches show that the effectiveness of a CBIR system increases when spatial relationship of colours is considered. In this paper we propose using the Looseness parameter from geostat, a branch of statistics whic...

Spatial activity-based modeling for pedestrian crowd simulation

This paper proposes a spatial activity-based modeling approach and applies it to pedestrian crowd behavior simulations. Compared to conventional pedestrian crowd models where pedestrians make movement decisions in a time-based manner, the spatial activity-based pedestrian crowd model allows a pedestrian to make decisions only when needed, that is, when there is significant change in the pedestr...

Service-Constraint Based Truthful Incentive Mechanisms for Crowd Sensing

Crowd sensing is a new paradigm which leverages the pervasive smartphones to efficiently collect and upload sensing data, enabling numerous novel applications. To achieve good service quality for a crowd sensing application, incentive mechanisms are necessary for attracting more user participation. Most of existing mechanisms apply only for the budget-constraint scenario where the platform (the...