On Reliable Task Assignment for Spatial Crowdsourcing

With the popularity of mobile devices and Online To Offline (O2O) marketing model, various spatial crowdsourcing platforms, such as Gigwalk, WeGoLook, TaskRabbit and gMission, are getting popular. An important task of spatial crowdsourcing platforms is to allocate spatial tasks to suitable workers. Existing approaches only simply focus on maximizing the number of completed spatial tasks but neglect the influence of supplies and demands from real crowdsourcing market, which leads to different optimal objectives for crowdsourcing task assignments. In this paper, to address the shortcomings of the existing approaches, we first propose a more general spatial crowdsourcing task assignment problem, called Market-driven Optimal Task Assignment (MOTA) problem, consisting of two real scenarios, Excess Demand of Crowd Workers and Insufficient Supply of Spatial Tasks, in daily life. Unfortunately, we prove that the two variants of this problem are NP-Hard. Thus, we design two approximation algorithm to solve this problem. Finally, we verify the effectiveness and efficiency of the proposed methods through extensive experiments on synthetic datasets.

Multi-stage complex task assignment in spatial crowdsourcing

Owing to its benefits such as flexibility, scalability, and interoperability, Software-Defined Networking (SDN) has been incorporated into Internet of Vehicles (IoV) to cope with the increasing demands of vehicular applications. The integration of SDN and IoV, namely SDN-IoV, can enrich many new applications for intelligent transportation such as traffic monitoring, smart navigation, and self-driving. The spatial crowdsourcing technology has been adopted as an effective data collection and processing method that is the premise of various SDN-IoV applications. However, as huge amounts of data are generated in spatial crowdsourcing services, the data privacy and security has become a key challenge for SDN-IoV. To overcome abovementioned challenge, a Deep Reinforcement Learning (DRL) and Blockchain empowered Spatial Crowdsourcing System (DB-SCS) is proposed. In DB-SCS, we design an improved multi-blockchain structure and a blockchain-based hierarchical task management method, which divide the spatial tasks into different categories according to the privacy requirements and the areas of the task and then decompose different categories of tasks and task receivers into sub-blockchains. While guaranteeing the data privacy, DB-SCS can also enhance the spatial crowdsourcing performance by using the proposed DRL-based management strategy to dynamically select the consensus algorithm, block size, and block generation rule. Extensive simulation experiments demonstrate that the DB-SCS can obtain high throughput, low overhead, and data privacy under various SDN-IoV scenarios.

With the continuous popularization of smart devices, Spatial Crowdsourcing(SC) came into being a novel paradigm of collecting smart sensing. As a core issue, the task assignment aims to arrange tasks to suitable workers for different objectives. However, most existing approaches matched tasks and workers under the premise of already known their number, while the arrival information of crowdsourcing participants cannot be understood in advance for their dynamically appearing. We fill this gap by proposing a Dynamic Task Assignment Framework based on Prediction and Adaptive Batching (DTAF-PAB). Specifically, we first predict the number of coming tasks and/or workers by a deep learning model Gated Recurrent Unit (GRU). Then, the adaptive batching strategies, based on Deep Q Network (DQN) and Double DQN (DDQN), are investigated to address the problem of adaptive and dynamic batching for task assignment. We design a deep reinforcement learning (DRL)-based algorithm to improve the overall benefit of the assignment. Finally, we use real-world datasets to evaluate the competitiveness of DTAF-PAB, and the experimental results show that the proposed framework is superior to other existing methods in terms of both predictive performance and crowdsourcing platform benefit.

With the rapid development of mobile devices and the crowdsourcing platforms, the spatial crowdsourcing has attracted much attention from the database community, specifically, spatial crowdsourcing refers to sending a location-based request to workers according to their positions. In this paper, we consider an important spatial crowdsourcing problem, namely reliable diversity-based spatial crowdsourcing (RDB-SC), in which spatial tasks (such as taking videos/photos of a landmark or firework shows, and checking whether or not parking spaces are available) are time-constrained, and workers are moving towards some directions. Our RDB-SC problem is to assign workers to spatial tasks such that the completion reliability and the spatial/temporal diversities of spatial tasks are maximized. We prove that the RDB-SC problem is NP-hard and intractable. Thus, we propose three effective approximation approaches, including greedy, sampling, and divide-and-conquer algorithms. In order to improve the efficiency, we also design an effective cost-model-based index, which can dynamically maintain moving workers and spatial tasks with low cost, and efficiently facilitate the retrieval of RDB-SC answers. Through extensive experiments, we demonstrate the efficiency and effectiveness of our proposed approaches over both real and synthetic datasets.

With the proliferation of GPS-enabled smart devices and increased availability of wireless network, spatial crowdsourcing (SC) has been recently proposed as a framework to automatically request workers (i.e., smart device carriers) to perform location-sensitive tasks (e.g., taking scenic photos, reporting events). In this paper, we study a destination-aware task assignment problem that concerns the optimal strategy of assigning each task to proper worker such that the total number of completed tasks can be maximized whilst all workers can reach their destinations before deadlines after performing assigned tasks. Finding the global optimal assignment turns out to be an intractable problem since it does not imply optimal assignment for individual worker. Observing that the task assignment dependency only exists amongst subsets of workers, we utilize tree-decomposition technique to separate workers into independent clusters and develop an efficient depth-first search algorithm with progressive bounds to prune non-promising assignments. In order to make our proposed framework applicable to more scenarios, we further optimize the original framework by proposing strategies to reduce the overall travel cost and allow each task to be assigned to multiple workers. Extensive empirical studies verify that the proposed technique and optimization strategies perform effectively and settle the problem nicely.

With the rapid development of mobile networks and the proliferation of mobile devices, spatial crowdsourcing, which refers to recruiting mobile workers to perform location-based tasks, has gained emerging interest from both research communities and industries. In this paper, we consider a spatial crowdsourcing scenario: in addition to specific spatial constraints, each task has a valid duration, operation complexity, budget limitation, and the number of required workers. Each volunteer worker completes assigned tasks while conducting his/her routine tasks. The system has a desired task probability coverage and budget constraint. Under this scenario, we investigate an important problem, namely heterogeneous spatial crowdsourcing task allocation (HSC-TA), which strives to search a set of representative Pareto-optimal allocation solutions for the multi-objective optimization problem, such that the assigned task coverage is maximized and incentive cost is minimized simultaneously. To accommodate the multi-constraints in heterogeneous spatial crowdsourcing, we build a worker mobility behavior prediction model to align with allocation process. We prove that the HSC-TA problem is NP-hard. We propose effective heuristic methods, including multi-round linear weight optimization and enhanced multi-objective particle swarm optimization algorithms to achieve adequate Pareto-optimal allocation. Comprehensive experiments on both real-world and synthetic data sets clearly validate the effectiveness and efficiency of our proposed approaches.

In spatial crowdsourcing, requesters submit their task-related locations and increase the demand of a local area. The platform prices these tasks and assigns spatial workers to serve if the prices are accepted by requesters. There exist mature pricing strategies which specialize in tackling the imbalance between supply and demand in a local market. However, in global optimization, the platform should consider the mobility of workers; that is, any single worker can be the potential supply for several areas, while it can only be the true supply of one area when assigned by the platform. The hardness lies in the uncertainty of the true supply of each area, hence the existing pricing strategies do not work. In the paper, we formally define this Global Dynamic Pricing(GDP) problem in spatial crowdsourcing. And since the objective is concerned with how the platform matches the supply to areas, we let the matching algorithm guide us how to price. We propose a MAtching-based Pricing Strategy (MAPS) with guaranteed bound. Extensive experiments conducted on the synthetic and real datasets demonstrate the effectiveness of MAPS.

This paper addresses the coverage and workload-balancing requirements of worker recruiting in spatial crowdsourcing. That is, the recruited workers should be able to visit all the crowdsourcing locations to satisfy a certain quality, e.g., traffic monitoring or climate forecast. In addition, each crowdsourcing operation has a cost, e.g., data traffic or energy consumption, and each crowdsourcing location might have a crowdsourcing budget for the visited workers. The objective of this paper is to find a worker recruiting algorithm, which ensures the coverage requirement and minimizes the maximal crowdsourcing cost for any crowdsourcing location. We gradually discuss the problem from the 1-D scenario to the general 2-D scenario. In the 1-D scenario, we propose a bounded directional greedy algorithm first. Then, we propose a PTAS extension. A dynamic programming solution is further proposed with a higher computation complexity. In the 2-D scenario, we propose a randomized rounding algorithm with an O(log n/log log n) approximation ratio in a high probability. Extensive experiments on realistic traces demonstrate the effectiveness of the proposed algorithms.

The popularity of mobile Internet techniques and Online-To-Offline(O2O) business models has led to the emergence of various spatial crowdsourcing (SC) platforms in our daily life. A core issue of SC platforms is to assign tasks to suitable crowd workers. Existing approaches usually focus on the matching of two types of objects,tasks and workers, and let workers to travel to the location of usersto provide services, which is a 2D matching problem. However, recent services provided by some new platforms, such as person-alized haircut service1and station ride-sharing, need users andworkers travel together to a third workplace to complete the service, which is indeed a 3D matching problem. Approaches in the existingstudies either cannot solve such 3D matching problem, or lack aassignment plan satisfying both users' and workers' preference inreal applications. Thus, in this paper, we propose a 3-Dimensional Stable Spatial Matching(3D-SSM) for the 3D matching problem innew SC services. We prove that the 3D-SSM problem is NP-hard, and propose two baseline algorithms and two efficient approximatealgorithms with bounded approximate ratios to solve it. Finally, weconduct extensive experiment studies which verify the efficiencyand effectiveness of the proposed algorithms on real and synthetic datasets.

The ubiquity of mobile sensors makes spatial crowd-sourcing a very promising platform for acquiring spatial tasks (i.e., tasks that are related to a location). Some frameworks have been successfully developed for crowdsourcing spatial tasks to a set of workers. Most of the current frameworks assume that all tasks belong to the same category and that workers are self-motivated to voluntarily perform tasks. However, the assumptions may not be practical in reality since different tasks may belong to different expertise and workers may not be self-incentivised to voluntarily perform tasks. In this paper, we introduce a reward-based approach for crowdsourcing spatial expert tasks (i.e., spatial tasks that are related to specific expertise). We formally define the Maximum Task Minimum Cost Assignment (MTMCA) problem and propose a solution for it. Subsequently, we perform various experiments to prove the usability and scalability of our approach as well as investigate factors that may effect the overall assignment. The experimental evaluation was conducted using both real-world and synthetic data sets.

Spatial Crowdsourcing (SC) is a transformative platform that engages individuals in collecting and analyzing environmental, social, and other spatio-temporal information. SC outsources spatio-temporal tasks to a set of workers , i.e., individuals with mobile devices that perform the tasks by physically traveling to specified locations. However, current solutions require the workers to disclose their locations to untrusted parties. In this paper, we introduce a framework for protecting location privacy of workers participating in SC tasks. We propose a mechanism based on differential privacy and geocasting that achieves effective SC services while offering privacy guarantees to workers. We address scenarios with both static and dynamic (i.e., moving) datasets of workers. Experimental results on real-world data show that the proposed technique protects location privacy without incurring significant performance overhead.

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.

These days, Online To Offline (O2O) platforms have been developing rapidly because of the popularization of smart phones and Mobile Internet. Spatial crowdsourcing, a burgeoning area in O2O market, is gaining more and more attention. It is a typical spatial crowdsourcing scenario in which an employer publishes a task and some workers will help him or her to accomplish it. However, most of previous work only considers the spatial information of workers and tasks, but ignores the individual variations among workers. In this paper, we raise a new problem called Software Development Team Formation (SDTF) problem, which aims to find a team of workers whose ability satisfies the requirement of the task. After showing the problem is NP-hard, we propose three greedy algorithms to approximately solve the problem. Besides, extensive experiments are conducted on synthetic and real datasets, which verify the effectiveness and efficiency of our algorithms.

SQCS: A sustainable quality control system for spatial crowdsourcing via three-party evolutionary game: Theory and practice