Finding Optimal Team for Multi-skill Task in Spatial Crowdsourcing

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.

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 large quantity of mobile devices equipped with various built-in sensors and the easy access to the high-speed wireless networks have made spatial crowdsourcing receive much attention in the research community recently. Generally, the objective of spatial crowdsourcing is to outsource location-based sensing tasks (e.g., traffic monitoring and pollution monitoring) to ordinary mobile workers (e.g., users carrying smartphones) efficiently. In this paper, we study a reliable task assignment problem for spatial crowdsourcing in a large worker market. Specifically, we use worker confidence to represent the reliability of successfully completing the assigned sensing tasks, and we formulate two optimization problems, maximum reliability assignment (MRA) under a recruitment budget and minimum cost assignment (MCA) under a task reliability requirement. We reveal the special structure properties of these problems, based on which we design effective approaches to assign tasks to the most suitable workers. The performances of the proposed algorithms are verified by theoretic analysis and experimental results on both real and synthetic datasets.

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.

Recently, there are several studies focusing on the bottleneck optimization objective in Spatial Crowdsourcing (SC). However, these studies usually do not consider the deadline constraint. Different from these studies, we take deadlines into consideration and identify the Fully Online Bottleneck Matching with Deadlines (FOBMD) problem in SC. Because of the deadlines, consideration must be given to both the bottleneck cost and the cardinality, which makes the FOBMD problem more challenging, and no online algorithm without actively refusing tasks can achieve a constant competitive ratio of the bottleneck cost for the FOBMD problem. To settle the FOBMD problem, we consider three baseline algorithms and propose an online algorithm, namely Local Isolated Point Greedy (LIPG). Finally, we validate the effectiveness and efficiency of our proposed algorithm via extensive experiments on both synthetic and real world datasets.

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.

Spatial crowd sourcing uses workers for performing tasks that require travel to different locations in the physical world. This paper considers the online spatial task assignment problem. In this problem, spatial tasks arrive in an online manner and an appropriate worker must be assigned to each task. However, outcome of an assignment is stochastic since the worker can choose to accept or reject the task. Primary goal of the assignment algorithm is to maximize the number of successful assignments over all tasks. This presents an exploration-exploitation challenge, the algorithm must learn the task acceptance behavior of workers while selecting the best worker based on the previous learning. We address this challenge by defining a framework for online spatial task assignment based on the multi-armed bandit formalization of the problem. Furthermore, we adapt a contextual bandit algorithm to assign a worker based on the spatial features of tasks and workers. The algorithm simultaneously adapts the worker assignment strategy based on the observed task acceptance behavior of workers. Finally, we present an evaluation methodology based on a real world dataset, and evaluate the performance of the proposed algorithm against the baseline algorithms. The results demonstrate that the proposed algorithm performs better in terms of the number of successful assignments.

Spatial Crowdsourcing (SC) is a popular outsourcing platform that assigns tasks to some individual workers, groups and communities through network. Commonly, the SC needs workers to upload their private information to untrust-worthy entities, which may cause a privacy breach. In this paper, we propose a framework for protecting privacy of workers participating in SC tasks. And we consider a complex scenario that the workers may have some different skills and a task may require a set of skills. What's more, in this framework, we propose an incentive mechanism which considered the feedbacks from task publishers and then adjust the skill level of workers. Experimental results based on synthetic datasets show the obvious improvement on completion quality and efficiency of our framework.

Task assignment for hybrid scenarios in spatial crowdsourcing: A Q-Learning-based approach

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.

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.

Given a project, the team formation problem (TFP) aims to find a team of experts with all the required skills and minimize the team’s communication cost. The TFP has emerged as a prominent research topic due to its substantial influence on team performance and the subsequent impact on organizational success. Previous research mainly concentrates on proposing heuristic or metaheuristic algorithms, yet fails to consider the historical experience of solving previous TFP. In addition, most existing learning-based approaches rely on complex multistage workflows, rather than learning the entire process end-to-end. This article proposes an end-to-end deep reinforcement learning approach that focuses on effectively reusing previous experience to tackle the TFP. First, we apply the Markov decision process to model the team formation process, in which the state is primarily characterized by the currently formed team and the skill requirements of the project. Second, we propose a self-updating deep reinforcement learning (SURL) model, in which the social network information serves as the input for the static encoding module, while the skills’ experts and the skill requirements of the project act as the inputs for the dynamic encoding module. Next, a novel deep reinforcement learning algorithm is designed to train the model by fully leveraging the historical experience of team formation. Finally, comprehensive experiments with real-world datasets demonstrate that our proposed model outperforms several commonly used optimization algorithms in terms of the average communication cost.

A unified framework for effective team formation in social networks

The ubiquity of mobile internet has led to the success of Spatial Crowdsourcing platforms like real-time taxi-hailing services, online food ordering services, etc. A critical component of such services is the task assignment algorithm employed for assigning the tasks to the workers of the platform. Our study of the literature in this domain shows that most of the task assignment algorithms developed for spatial crowdsourcing platforms address the problem from a utilitarian perspective, i.e., they optimise for only kind of entity. In contrast, we address the task assignment problem in spatial crowdsourcing platforms from an egalitarian perspective. An egalitarian approach aims to optimise the expectation of all entities involved. Specifically, we aim to minimise the waiting time for the customers and workers, while maximising the profit earned by the platform. To the best of our knowledge, ours is the only study that achieves this objective in a fully-online setting, with deadlines for both customers and workers. We propose two heuristic algorithms to solve the problem, and evaluate our algorithms on a real taxi-trips records dataset. Our algorithms exhibit a superior performance than the state-of-the-art algorithm for the fully-online bottleneck matching problem with deadlines, in terms of solution quality, running time and response time.

Many heuristic algorithms have been proposed in the literature to solve the team formation problem. The researchers considered a project as a set of skills selected randomly from the given pool of skills. But this leads to a skewed distribution of skills in the projects with many skills having very few experts, which we term as rare skills. In this work, we create a realistic bench-mark dataset for this problem. In general, any project/task in the industry can be seen to have a good mix of popular as well as rare skills. We first conduct an empirical study of the distribution of popular skills vs rare skills in the well-known DBLP (Digital Bibliography & Library Project) data set. The distribution of popularity of skills is shown to satisfy a power law with a heavy tail, indicating the presence of a large number of skills with very few experts and a small number of highly popular skills. We build a realistic a benchmark dataset using stratified random sampling to form tasks with various distributions of popular and rare skills. The classical team formation algorithms are evaluated using this new benchmark dataset. The evaluation is done with respect to the available communication costs in the literature as well as the execution time incurred by the algorithms. It has been observed from the experiments that all the measures show lower values of communication cost for tasks having higher proportion of popular skills.