Spatial Task Assignment Research Articles

Clustering Based Priority Queue Algorithm for Spatial Task Assignment in Crowdsourcing

Clustering Based Priority Queue Algorithm for Spatial Task Assignment in Crowdsourcing

Method for Spatial Crowdsourcing Task Assignment Based on Integrating of Genetic Algorithm and Ant Colony Optimization

With the rapid development of mobile networks and the proliferation of mobile devices, Spatial Crowdsourcing (SC) has attracted the interest of industry and research groups. In addition to considering the specific spatial constraints in the existing research spatial crowdsourcing, each task has an effective duration, operational complexity, number of workers required, and incentive budget constraints. In this scenario, we studied the MQC-TA (Maximum Quality and Minimum Cost Task Assignment) problem. Firstly, the worker incentive model is established. The MQC-GAC algorithm is designed according to the MQC-TA problem to maximize the task completion quality and minimize the incentive budget. The algorithm combined the fast convergence of Genetic Algorithm and the positive feedback mechanism of Ant Colony Optimization Algorithm. Finally, the effectiveness and efficiency of the proposed method are verified by a comprehensive experiment on the data set.

Spatial Task Assignment Based on Information Gain in Crowdsourcing

Spatial crowdsourcing provides workers for performing cooperative tasks considering their locations, and is drawing much attention with the rapid development of mobile Internet. The key techniques in spatial crowdsourcing include worker-mobitlity-based task matching for more information gain and efficient cooperation among coworkers. In this paper, we first propose information gain based maximum task matching problem, where each spatial task needs to be performed before its expiration time and workers are moving dynamically. We then prove it is a NP-hard problem. Next, we propose two approximation algorithms: greedy and extremum algorithms. In order to improve the time efficiency and the task assignment accuracy, we further propose an optimization approach. Subsequently, for complex spatial tasks, we propose a feedback-based cooperation mechanism, model the worker affinity and the matching degree between a task and a group of coworkers, and design a feedback-based assignment algorithm with group affinity. We conducted extensive experiments on both real-world and synthetic datasets. The results demonstrate that our approach outperforms related schemes.

Preference-Aware Task Assignment in Spatial Crowdsourcing

With the ubiquity of smart devices, Spatial Crowdsourcing (SC) has emerged as a new transformative platform that engages mobile users to perform spatio-temporal tasks by physically traveling to specified locations. Thus, various SC techniques have been studied for performance optimization, among which one of the major challenges is how to assign workers the tasks that they are really interested in and willing to perform. In this paper, we propose a novel preference-aware spatial task assignment system based on workers’ temporal preferences, which consists of two components: History-based Context-aware Tensor Decomposition (HCTD) for workers’ temporal preferences modeling and preference-aware task assignment. We model worker preferences with a three-dimension tensor (worker-task-time). Supplementing the missing entries of the tensor through HCTD with the assistant of historical data and other two context matrices, we recover worker preferences for different categories of tasks in different time slots. Several preference-aware task assignment algorithms are then devised, aiming to maximize the total number of task assignments at every time instance, in which we give higher priorities to the workers who are more interested in the tasks. We conduct extensive experiments using a real dataset, verifying the practicability of our proposed methods.

Combining Spatial Optimization and Multi-Agent Temporal Difference Learning for Task Assignment in Uncertain Crowdsourcing

In recent years, spatial crowdsourcing has emerged as an important new framework, in which each spatial task requires a set of right crowd-workers in the near vicinity to the target locations. Previous studies have focused on spatial task assignment in the static crowdsourcing environment. These algorithms may achieve local optimality by neglecting the uncertain features inherent in real-world crowdsourcing environments, where workers may join or leave during run time. Moreover, spatial task assignment is more complicated when large-scale crowd-workers exist in crowdsourcing environments. The large-scale nature of task assignments poses a significant challenge to uncertain spatial crowdsourcing. In this paper, we propose a novel algorithm combining spatial optimization and multi-agent temporal difference learning (SMATDL). The combination of grid-based optimization and multi-agent learning can achieve higher adaptability and maintain greater efficiency than traditional learning algorithms in the face of large-scale crowdsourcing problems. The SMATDL algorithm decomposes the uncertain crowdsourcing problem into numerous sub-problems by means of a grid-based optimization approach. In order to adapt to the change in the large-scale environment, each agent utilizes temporal difference learning to handle its own spatial region optimization in online crowdsourcing. As a result, multiple agents in SMATDL collaboratively learn to optimize their efforts in accomplishing the global assignment problems efficiently. Through extensive experiments, we illustrate the effectiveness and efficiency of our proposed algorithms on the experimental data sets.

Spatial task management method for location privacy aware crowdsourcing

Spatial crowdsourcing is a promising architecture that collects various types of data online with the help of participants powerful mobile devices. Humans are involved in the crowdsourcing process, thereby increasing its accuracy; however, it is also associated with some privacy and security problems. The crowd tasks are executed in participants mobile devices, and the results are send to the server through networks, so that attackers could eavesdrop participants location information. Thus, we studied and proposed a spatial task assignment method for privacy-aware spatial crowdsourcing using a secure grid-based index. The secure grid index used an encrypted grid number and grid cell-based local coordinate system to protect participants location privacy. By using the grid based index in spatial task management process, it also could increase the spatial task processing time. In the experimental test, we showed that the proposed method is faster than the current method and extremely efficient when the spatial crowdsourcing tasks are geometry based tasks.

Task-Management Method Using R-Tree Spatial Cloaking for Large-Scale Crowdsourcing

With the development of sensor technology and the popularization of the data-driven service paradigm, spatial crowdsourcing systems have become an important way of collecting map-based location data. However, large-scale task management and location privacy are important factors for participants in spatial crowdsourcing. In this paper, we propose the use of an R-tree spatial cloaking-based task-assignment method for large-scale spatial crowdsourcing. We use an estimated R-tree based on the requested crowdsourcing tasks to reduce the crowdsourcing server-side inserting cost and enable the scalability. By using Minimum Bounding Rectangle (MBR)-based spatial anonymous data without exact position data, this method preserves the location privacy of participants in a simple way. In our experiment, we showed that our proposed method is faster than the current method, and is very efficient when the scale is increased.

Dynamic Data Driven Crowd Sensing Task Assignment

To realize the full potential of mobile crowd sensing, techniques are needed to deal with uncertainty in participant locations and trajectories. We propose a novel model for spatial task assignment in mobile crowd sensing that uses a dynamic and adaptive data driven scheme to assign moving participants with uncertain trajectories to sensing tasks, in a near-optimal manner. Our scheme is based on building a mobility model from publicly available trajectory history and estimating posterior location values using noisy/uncertain measurements upon which initial tasking assignments are made. These assignments may be refined locally (using exact information) and used by participants to steer their future data collection, which completes the feedback loop. We present the design of our proposed approach with rationale to suggest its value in effective mobile crowd sensing task assignment in the presence of uncertain trajectories.