Spatial Crowdsourcing (SC) is a transformative platform that engages individuals, groups and communities in the act of collecting, analyzing, and disseminating environmental, social and other spatio-temporal information. The objective of SC is to outsource a set of spatio-temporal tasks to a set of workers, i.e., individuals with mobile devices that perform the tasks by physically traveling to specified locations of interest. However, current solutions require the workers, who in many cases are simply volunteering for a cause, to disclose their locations to untrustworthy entities. In this paper, we introduce a framework for protecting location privacy of workers participating in SC tasks. We argue that existing location privacy techniques are not sufficient for SC, and we propose a mechanism based on differential privacy and geocasting that achieves effective SC services while offering privacy guarantees to workers. We investigate analytical models and task assignment strategies that balance multiple crucial aspects of SC functionality, such as task completion rate, worker travel distance and system overhead. Extensive experimental results on real-world datasets show that the proposed technique protects workers' location privacy without incurring significant performance metrics penalties.

/pdf/a-framework-for-protecting-worker-location-privacy-in-pe2g2e3on4.pdf

A framework for protecting worker location privacy in spatial crowdsourcing

Numerics of the lattice boltzmann method on nonuniform grids: Standard LBM and finite-difference LBM

We present a review of some of the state-of-the-art numerical methods for solving the Stefan problem and the Poisson and the diffusion equations on irregular domains using (i) the level-set method for representing the (possibly moving) irregular domain's boundary, (ii) the ghost-fluid method for imposing the Dirichlet boundary condition at the irregular domain's boundary and (iii) a quadtree/octree node-based adaptive mesh refinement for capturing small length scales while significantly reducing the memory and CPU footprint. In addition, we highlight common misconceptions and describe how to properly implement these methods. Numerical experiments illustrate quantitative and qualitative results.

/pdf/high-resolution-sharp-computational-methods-for-elliptic-and-1cvx5nrftw.pdf

High Resolution Sharp Computational Methods for Elliptic and Parabolic Problems in Complex Geometries

Spatial co-location pattern mining discovers the subsets of features of which the events are frequently located together in geographic space. The current research on this topic adopts a distance threshold that has limitations in spatial data sets with various magnitudes of neighborhood distances, especially for mining of regional co-location patterns. In this paper, we propose a hierarchical co-location mining framework accounting for both variety of neighborhood distances and spatial heterogeneity. By adopting k-nearest neighbor graph (kNNG) instead of distance threshold, we propose "distance variation coefficient" as a new measure to drive the mining operations and determine an individual neighborhood relationship graph for each region. The proposed mining algorithm outputs a set of regions with each of them an individual set of regional co-location patterns. The experimental results on both synthetic and real world data sets show that our framework is effective to discover these regional co-location patterns.

Mining regional co-location patterns with kNNG

The adaptation of an existing volumetric simulation mesh to updated parameters of the underlying CAD geometry is a crucial component within automatic design optimization. By avoiding costly automatic or even manual (re-)meshing it enables the automatic generation and evaluation of new design variations, e.g., through FEM or CFD simulations. This is particularly important for stochastic global optimization techniques—such as evolutionary algorithms—which typically require a large number of design variations to be created and evaluated. In this paper we present a simple yet versatile method for high quality mesh morphing. Building upon triharmonic radial basis functions, our shape deformations minimize distortion and thereby implicitly preserve shape quality. Moreover, the same unified code can be used to morph tetrahedral, hexahedral, or arbitrary polyhedral volume meshes. We compare our method to several other recently proposed techniques and show that ours yields superior results in most cases.

High Quality Mesh Morphing Using Triharmonic Radial Basis Functions

The design and analysis of spatial data structures

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The design and analysis of spatial data structures