Spatial Crowdsourcing Platforms Research Articles

Matching Tasks and Workers under Known Arrival Distributions: Online Task Assignment with Two-sided Arrivals

Efficient allocation of tasks to workers is a central problem in crowdsourcing. In this paper, we consider a setting inspired by spatial crowdsourcing platforms, where both workers and tasks arrive at different times, and each worker-task assignment yields a given reward. The key challenge is to address the uncertainty in the stochastic arrivals from both workers and the tasks. In this work, we consider a ubiquitous scenario where the arrival patterns of worker “types” and task “types” are not erratic but can be predicted from historical data. Specifically, we consider a finite time horizon T and assume that in each time-step the arrival of a worker and a task can be seen as an independent sample from two (different) distributions. Our model, called Online Task Assignment with Two-Sided Arrival (OTA-TSA), is a significant generalization of the classical online task assignment problem when all the tasks are statically available. For the general case of OTA-TSA, we present an optimal non-adaptive algorithm (NADAP), which achieves a competitive ratio (CR) of at least 0.295. For a special case of OTA-TSA when the reward depends only on the worker type, we present two adaptive algorithms, which achieve CRs of at least 0.343 and 0.355, respectively. On the hardness side, we show that (1) no non-adaptive can achieve a CR larger than that of NADAP, establishing the optimality of NADAP among all non-adaptive algorithms; and (2) no (adaptive) algorithm can achieve a CR better than 0.581 (unconditionally) or 0.423 (conditionally on the benchmark linear program), respectively. All aforementioned negative results apply to even unweighted OTA-TSA when every assignment yields a uniform reward. At the heart of our analysis is a new technical tool, called two-stage birth-death process , which is a refined notion of the classical birth-death process. We believe it may be of independent interest. Finally, we perform extensive numerical experiments on a real-world rideshare dataset collected in Chicago and a synthetic dataset, and results demonstrate the effectiveness of our proposed algorithms in practice.

Competition and Cooperation: Global Task Assignment in Spatial Crowdsourcing

Online spatial crowdsourcing platforms provide popular O2O services in people's daily. Users submit real-time tasks through the Internet and require the platform to immediately assign workers to serve them. However, the imbalance distribution of tasks and workers leads to the rejection of some tasks, which reduces the profit of the platform. In this paper, we propose that similar platforms can form an alliance to make full use of the global service supply through cooperation. We name the problem as <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">G</u> lobal <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">T</u> ask <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">A</u> ssignment (GTA), in which platforms are allowed to hire idle workers from other platforms to improve the profit of all the platforms together. Different from relevant works, the decision-makers in GTA are platforms rather than individual workers, which can better assign workers in all platforms and improve the overall profit. We design an auction-based incentive mechanism (AIM), to motivate platforms to rent idle workers to other platforms so that increase their own profit. Based on the mechanism, we propose a greedy-based assignment algorithm (BaseGTA), in which platforms greedily maximizes their current profit. We further propose a prediction-based assignment algorithm (ImpGTA), in which platforms make decisions based on the spatial-temporal distribution in the future time. Experimental results show that platforms using our algorithms can achieve higher profit than the existing studies.

Online Dependent Task Assignment in Preference Aware Spatial Crowdsourcing

Spatial crowdsourcing platforms have become increasingly popular in people's daily life. A fundamental problem in spatial crowdsourcing is task assignment, which assigns spatial tasks to the workers appropriately in order to satisfy certain objectives. Previous studies usually focus on the real-time micro-task allocation, which does not consider the dependency relationships among tasks. To address this limitation, in this paper, we define and formulate a new problem, called Online Dependent Task Assignment (ODTA) in preference aware spatial crowdsourcing. We first prove that ODTA is <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathcal{NP}\ $</tex-math></inline-formula> -hard. Then, we design a threshold-based algorithm in the adversarial order model and obtain a near-optimal theoretical bound on the competitive ratio. More importantly, considering the random order arrival model, we further present three algorithms based on a two-stage framework, namely ODTA-Greedy, ODTA-Greedy-OP and ODTA-OPT, which are more effective with a constant competition ratio of 1/8, 1/8 and 1/4 respectively. Experimental results on both synthetic and real datasets show that our proposed ODTA-OPT approach outperforms the representative approaches in terms of overall utility

ACTA: Autonomy and Coordination Task Assignment in Spatial Crowdsourcing Platforms

Spatial platforms have become increasingly important in people's daily lives. Task assignment is a critical problem in these platforms that matches real-time orders to suitable workers. Most studies only focus on independent platforms that are in a competitive relationship. Recently, an emerging service model was proposed, where orders are shared with multiple similar platforms. It aims to solve the imbalance between supply and demand through cooperation. However, it faces the following main challenges: 1) Coordinating independent platforms fairly based on the limited information; 2) Building a task assignment process with personalized algorithms. In this paper, we study real applications and define the Autonomy and Coordination Task Assignment problem (ACTA) to maximize the global revenue and fairness. We propose a framework to solve ACTA that consists of public order sending, local matching, global conflict adjustment and results notification. The framework uses mid-products and public data to train a revenue estimation model to coordinate participants. We further propose dynamic weight task assignment algorithms to guarantee fairness. Through the experiments, we prove that the platforms can obtain higher revenue, which shows the effectiveness and efficiency of our work.

An overview of location privacy protection in spatial crowdsourcing platforms during the task assignment process

An overview of location privacy protection in spatial crowdsourcing platforms during the task assignment process

An Overview of Location Privacy Protection in Spatial Crowdsourcing Platforms During the Task Assignment Process

An Overview of Location Privacy Protection in Spatial Crowdsourcing Platforms During the Task Assignment Process

Privacy-preserving cooperative online matching over spatial crowdsourcing platforms

With the continuous development of spatial crowdsourcing platform, online task assignment problem has been widely studied as a typical problem in spatial crowdsourcing. Most of the existing studies are based on a single-platform task assignment to maximize the platform's revenue. Recently, cross online task assignment has been proposed, aiming at increasing the mutual benefit through cooperations. However, existing methods fail to consider the data privacy protection in the process of cooperation and cause the leakage of sensitive data such as the location of a request and the historical data of cooperative platforms. In this paper, we propose Privacy-preserving Cooperative Online Matching (PCOM), which protects the privacy of the users and workers on their respective platforms. We design a PCOM framework and provide theoretical proof that the framework satisfies the differential privacy property. We then propose two PCOM algorithms based on two different privacy-preserving strategies. Extensive experiments on real and synthetic datasets confirm the effectiveness and efficiency of our algorithms.

Profit Optimization in Spatial Crowdsourcing: Effectiveness and Efficiency

In Spatial crowdsourcing, mobile users perform spatio-temporal tasks that involve travel to specified locations. Spatial crowdsourcing (SC) is enabled by SC platforms that support mobile worker recruitment and retention, as well as task assignment, which is essential to maximize profits that are accrued from serving task requests. Specifically, how to best achieve task assignment in a cost-effective manner while contending with spatio-temporal constraints is a key challenge in SC. To address this challenge, we formalize and study a novel Profit-driven Task Assignment problem. We first establish a task reward pricing model that takes into account the temporal constraints (i.e., expected completion time and deadline) of tasks. Then we adopt an optimal algorithm based on tree decomposition to achieve an optimal task assignment and propose greedy algorithms based on Random Tuning Optimization to improve the computational efficiency. To balance effectiveness and efficiency, we also provide a heuristic task assignment algorithm based on Ant Colony Optimization that assigns tasks by simulating behavior of ant colonies foraging for food. Finally, we conduct extensive experiments using real and synthetic data, offering detailed insight into effectiveness and efficiency of the proposed methods.

Multi-Objective Task Scheduling Optimization in Spatial Crowdsourcing

Recently, with the development of mobile devices and the crowdsourcing platform, spatial crowdsourcing (SC) has become more widespread. In SC, workers need to physically travel to complete spatial–temporal tasks during a certain period of time. The main problem in SC platforms is scheduling a set of proper workers to achieve a set of spatial tasks based on different objectives. In actuality, real-world applications of SC need to optimize multiple objectives together, and these objectives may sometimes conflict with one another. Furthermore, there is a lack of research dealing with the multi-objective optimization (MOO) problem within an SC environment. Thus, in this work we focused on task scheduling based on multi-objective optimization (TS-MOO) in SC, which is based on maximizing the number of completed tasks, minimizing the total travel costs, and ensuring the balance of the workload between workers. To solve the previous problem, we developed a new method, i.e., the multi-objective task scheduling optimization (MOTSO) model that consists of two algorithms, namely, the multi-objective particle swarm optimization (MOPSO) algorithm with our fitness function Alabbadi, et al. and the ranking strategy algorithm based on the task entropy concept and task execution duration. The main purpose of our ranking strategy is to improve and enhance the performance of our MOPSO. The primary goal of the proposed MOTSO model is to find an optimal solution based on the multiple objectives that conflict with one another. We conducted our experiment with both synthetic and real datasets; the experimental results and statistical analysis showed that our proposed model is effective in terms of maximizing the number of completed tasks, minimizing the total travel costs, and balancing the workload between workers.

Dynamic pricing in profit-driven task assignment: a domain-of-influence based approach

The development of mobile Internet and sharing economy brings the prosperity of spatial crowdsourcing (SC). Pricing is a crucial step for SC platforms to solve the Profit-driven Task Assignment (PTA) problem to maximize their total profit. However, dynamic pricing is still large unexplored in PTA. In addition, existing works seek solutions without considering the uncertainty of workers’ acceptance for assigned tasks in the task assignment process. To deal with these challenges, we develop a two-stage task assignment framework with dynamic pricing. Specifically, we propose a novel Domain-of-Influence based dynamic pricing algorithm, which can iteratively figure out the price that represents the balance between task demand and worker supply. Then we employ hyperbolic temporal discounting function to estimate the worker’s psychological reward that indicates the acceptance or rejection of assigned task. With considering the driver’s psychological reward, we adopt an optimal algorithm to achieve the optimal task assignment and propose greedy algorithms to improve the computational efficiency. Finally, we evaluate the performance using two road network datasets of Jinan and Luoyang in China. The experimental results show the effectiveness and efficiency of our proposed approaches.

Worker Collaborative group estimation in spatial crowdsourcing

Spatial crowdsourcing (SC) has gained much attention in recent years. On SC platforms, requesters can publish spatial tasks on a specific topic such as taking photos at certain locations which is reflected by some tags of the task, and workers can choose tasks according to their tags. An interesting phenomenon is that workers often form groups based on their social relationships and common interests to perform same tasks. A nature question often raised when a task is published is how to predict which (or how many) workers are attracted by the task if its tags are specified. In particular, the tags of a new task affect the willingness of the workers to choose it. On the other hand, workers are also affected by the willingness of their co-workers or friends when deciding to choose a task or not. In this paper, we study the problem of potential workers estimation for a spatial crowdsourcing task, the Worker Collaborative Group Estimation (WCGE) problem, and model whether workers will join the group of a task as a game. We present efficient algorithms to find the Nash Equilibrium of the game and estimate the potential workers for the new task. Using synthetic datasets, we experimentally study the performance of proposed solutions. Our solutions can also help understand big geospatial data for self-driving cars and more intelligent transportation applications.

Two-sided Online Micro-Task Assignment in Spatial Crowdsourcing

With the rapid development of smartphones, spatial crowdsourcing platforms are getting popular. A foundational research of spatial crowdsourcing is to allocate micro-tasks to suitable crowd workers. Many existing studies focus on the offline scenario, where all the spatiotemporal information of micro-tasks and crowd workers is given. In this paper, we focus on the online scenario and identify a more practical micro-task allocation problem, called the G lobal O nline M icro-task A llocation in spatial crowdsourcing (GOMA) problem. We first extend the state-of-the-art algorithm for the online maximum weighted bipartite matching problem to the GOMA problem as the baseline algorithm. Although the baseline algorithm provides a theoretical guarantee for the worst case, its average performance in practice is not good enough since the worst case happens with a very low probability in the real world. Thus, we consider the average performance of online algorithms, a.k.a. random order model. We propose a two-phase-based framework, based on which we present the TGOA algorithm with a $\frac{1}{4}$ 1 4 -competitive ratio under the random order model. To improve its efficiency, we further design the TGOA-Greedy and TGOA-OP algorithm following this framework, which runs faster than the TGOA algorithm with a competitive ratio of $\frac{1}{8}$ 1 8 and $\frac{1}{4}$ 1 4 , respectively. We also revisit the average performance of Greedy, which has long been considered as the worst due to its unbounded competitive ratio in the worst case. Finally, we verify the effectiveness and efficiency of the proposed methods through extensive experiments on synthetic and real datasets.

Budget-aware online task assignment in spatial crowdsourcing

The prevalence of mobile internet techniques stimulates the emergence of various spatial crowdsourcing applications. Certain of the applications serve for requesters, budget providers, who submit a batch of tasks and a fixed budget to platform with the desire to search suitable workers to complete the tasks in maximum quantity. Platform lays stress on optimizing assignment strategies on seeking less budget-consumed worker-task pairs to meet requesters' demands. Existing research on the task assignment with budget constraint mostly focuses on static offline scenarios, where the spatiotemporal information of all workers and tasks is known in advance. However, workers usually appear dynamically on real spatial crowdsourcing platforms, where existing solutions can hardly handle it. In this paper, we formally define a novel problem Budget-aware Online task Assignment(BOA) in spatial crowdsourcing applications. BOA aims to maximize the number of assigned worker- task pairs under a budget constraint where workers appear dynamically on platforms. To address the BOA problem, we first propose an efficient threshold-based greedy algorithm Greedy-RT which utilizes a random generated threshold to prune the pairs with large travel cost. Greedy-RT performs well in adversary model when compared with simple greedy algorithm, but it is unstable in random model for its randomly generated threshold may produce poor quality in matching size. We then propose a revised algorithm Greedy-OT which could learn approximately optimal threshold from historical data, and consequently improves matching size significantly in both models. Finally, we verify the effectiveness and efficiency of the proposed methods through extensive experiments on real and synthetic datasets.

Online Multi-Skilled Task Assignment on Road Networks

With the development of smart phones and online to offline, spatial platforms, such as TaskRabbit, are getting famous and popular. Tasks on these platforms have three main characters: they are in real-time dynamic scenario, they are on the road networks, and some of them have multiple skills. However, existing studies do not take into account all these three things simultaneously. Therefore, an important issue of spatial crowdsourcing platforms is to assign workers to tasks according to their skills on road networks in a real-time scenario. In this paper, we first propose a practical problem, called online multi-skilled task assignment on road networks (OMTARN) problem, and prove that the OMTARN problem is NP-Hard and no online algorithms can achieve a constant competitive ratio on this problem. Then, we design a framework using batch-based algorithms, including fixed and dynamic batch-based algorithm, and we show that how the algorithms update the batch. After that, we use the hierarchically separated tree structure to accelerate our algorithms. Finally, we implement all the algorithms of the OMTARN problem and clarify their strengths and weaknesses by testing them on both synthetic and real datasets.

Spatial crowdsourcing

Crowdsourcing is a new computing paradigm where humans are actively enrolled to participate in the procedure of computing, especially for tasks that are intrinsically easier for humans than for computers. The popularity of mobile computing and sharing economy has extended conventional web-based crowdsourcing to spatial crowdsourcing (SC), where spatial data such as location, mobility and the associated contextual information, plays a central role. In fact, spatial crowdsourcing has stimulated a series of recent industrial successes including Citizen Sensing (Waze), P2P ride-sharing (Uber) and Real-time Online-To-Offline (O2O) services (Instacart and Postmates). In this tutorial, we review the paradigm shift from web-based crowdsourcing to spatial crowdsourcing. We dive deep into the challenges and techniques brought by the unique spatio-temporal characteristics of spatial crowdsourcing. Particularly, we survey new designs in task assignment, quality control, incentive mechanism design and privacy protection on spatial crowdsourcing platforms, as well as the new trend to incorporate crowdsourcing to enhance existing spatial data processing techniques. We also discuss case studies of representative spatial crowdsourcing systems and raise open questions and current challenges for the audience to easily comprehend the tutorial and to advance this important research area.

Top-k Team Recommendation and Its Variants in Spatial Crowdsourcing

With the rapid development of mobile internet and online to offline marketing model, various spatial crowdsourcing platforms, such as Gigwalk and Gmission, are getting popular. Most existing studies assume that spatial crowdsourced tasks are simple and trivial. However, many real crowdsourced tasks are complex and need to be collaboratively finished by a team of crowd workers with different skills. Therefore, an important issue of spatial crowdsourcing platforms is to recommend some suitable teams of crowd workers to satisfy the requirements of skills in a task. In this paper, to address the issue, we first propose a more practical problem, called Top-k team recommendation in spatial crowdsourcing (TopkTR) problem. We prove that the TopkTR problem is NP-hard and designs a two-level-based framework, which includes an approximation algorithm with provable approximation ratio and an exact algorithm with pruning techniques to address it. In addition, we study a variant of the TopkTR problem, called TopkTRL, where a team leader is appointed among each recommended team of crowd workers in order to coordinate different crowd workers conveniently, and the aforementioned framework can be extended to address this variant. Finally, we verify the effectiveness and efficiency of the proposed methods through extensive experiments on real and synthetic datasets.