Task Assignment In Spatial Crowdsourcing Research Articles

Real-time Multi-platform Route Planning in ridesharing

The increasing availability of mobile Internet and portable devices has led to the popularity of spatial crowdsourcing recently. The significant burgeoning of ridesharing services in spatial crowdsourcing has transformed urban mobility. However, due to the imbalance between supply and demand, some requests may be rejected or workers may keep idle for a long time. Fortunately, sharing tasks and workers across multiple platforms in a collaborative manner can diminish the negative effects of non-uniform distribution. In this paper, we propose a Multi-platform Route Planning in ridesharing problem (MPRP), which integrates route planning and task assignment in spatial crowdsourcing. We study how to improve the total revenue of platforms through coordination with multiple ridesharing platforms. To solve the problem, we propose the Greedy Multi-platform Route Planning algorithm (G-MPRP), which extends the dynamic programming insertion operation and assigns workers in a greedy manner. To overcome the shortcoming of G-MPRP, we further propose the Pack-based Multi-platform Route Planning algorithm (P-MPRP), which packs requests through a ranking-based function. Extensive experimental results shows the proposed algorithms can improve the revenues significantly.

Three-sided online stable task assignment in spatial crowdsourcing

Task assignment is the fundamental problem of crowdsourcing, which addresses the matching between service providers and demanders in urban services. As crowdsourcing has been receiving more attention, the focus of task assignment has expanded from one-sided or two-sided task assignment to three-sided task assignment. Recent research on the three-sided version aims at the online case or stable case, which only partially reflects the real scenario. In this paper, we consider a hybrid case that contains the aforementioned cases. Concretely, we propose a three-sided online stable task assignment problem. We first show the fundamental notations of task assignment. Then, we analyze and present the matching conditions and formulate the problem. As a warm-up, we propose three iconic algorithms as baselines and analyze their performance in the three-sided scenario. Inspired by two suggested matching technique in a two-sided task assignment problem, we present a new algorithm that divides the arrival sequence into three parts in which we utilize different strategies for different arrival intervals. It balances efficiency and effectiveness. In the experiment, all the algorithms are verified in size, memory, and time and tested on a real and a synthetic dataset. The results show that our algorithm maintains efficiency and effectiveness.

Non-Rejection Aware Online Task Assignment in Spatial Crowdsourcing

Non-Rejection Aware Online Task Assignment in Spatial Crowdsourcing

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.

Preference-Aware Group Task Assignment in Spatial Crowdsourcing: Effectiveness and Efficiency

With the diffusion of online mobile devices with geo-location capabilities, the infrastructure necessary for real-world deployment of Spatial Crowdsourcing (SC), where so-called mobile workers are assigned location-sensitive tasks, is in place. Some SC tasks cannot be completed by a single worker due to their complexity, but rather must be assigned to and completed by a group of users. Achieving such group assignments that satisfy all group members evenly is an open challenge. To address this challenge, we propose a novel preference-aware group task assignment framework encompassing two components: Mutual Information-based Preference Modeling (MIPM) and Preference-aware Group Task Assignment (PGTA). The MIPM component learns the preferences of groups contrastively by maximizing the mutual information between workers and worker groups based on worker-task and group-task interaction data and by using an attention mechanism to weight group members adaptively. In addition, curriculum negative sampling is adopted to generate a small number of negative workers for each worker group, following the principles of curriculum learning. Next, the PGTA component offers an optimal task assignment algorithm that employs tree decomposition to assign tasks to appropriate worker groups, with the aim of maximizing the number of task assignments while prioritizing more interested groups when assigning tasks. The task assignment framework also features preference-constrained pruning of unpromising worker groups to speed up the assignment process. Finally, we report extensive experiments that offer evidence of the effectiveness and practicality of the paper's proposal.

Worker-Churn-Based Task Assignment With Context-LSTM in Spatial Crowdsourcing

The pervasiveness of GPS-enabled devices and wireless communication technologies flourish the market of Spatial Crowdsourcing (SC), which consists of location-based tasks and requires workers to be at specific locations physically to complete them. In this work, we study the problem of worker-churn-based task assignment in SC, where tasks are assigned by considering workers' churn. In particular, we aim to maximize the total rewards of task assignments based on the worker churn prediction. To solve the problem, we propose a two-phase framework, which consists of a worker churn prediction and a task assignment phase. In the first phase, we use an LSTM-based model to extract latent feelings of workers based on historical data and then estimate idle time intervals of workers. In the assignment phase, we design an efficient greedy algorithm and a Kuhn-Munkras-based algorithm that can achieve the optimal task assignment. To improve the accuracy of the idle time interval estimation for workers, we adopt a context-dependent LSTM model, which involves interactions between inputs and their context. We further optimize the original task assignment framework by proposing a travel distance optimization strategy to reduce the overall travel distance. Extensive experiments offer insight into the effectiveness and efficiency of the proposed solutions.

Location-and-Preference Joint Prediction for Task Assignment in Spatial Crowdsourcing

With the rapid development of mobile networks and the ubiquity of mobile devices, spatial crowdsourcing (SC), which refers to assigning spatial–temporal tasks to moving workers, has drawn increasing attention. Thus, many researchers aim at various task assignment methods in SC. However, existing works generally consider workers’ location and preference categories separately. Ignorance of the correlation between them can often lead to poor assignment results. In this article, we propose a location-and-preference joint prediction model (JPM) to predict workers’ locations and preference categories jointly at each sample timestamp. Based on the predictive location probability distribution and preference probability distribution, we elaborately design a greedy multiattribute joint task assignment algorithm (MAJA) to maximize the average number of completed tasks under constraints. Then, an overall procedure incorporating the JPM and MAJA, called the location-and-preference joint prediction-based task assignment (LPJTA), is implemented to focus on assigning tasks to workers who are near the task location and willing to perform the task based on predicting locations and preference categories. We theoretically analyze the time complexity and approximation ratio of the proposed methods and construct extensive experiments on three real datasets to empirically verify their effectiveness, comparing with the state-of-the-art baselines.

PPTA: A location privacy-preserving and flexible task assignment service for spatial crowdsourcing

With the rapid growth of sensor-rich mobile devices, spatial crowdsourcing (SC) has emerged as a new crowdsourcing paradigm harnessing the crowd to perform location-dependent tasks. To appropriately select workers that are near the tasks, SC systems need to perform location-based task assignment, which requires collecting worker locations and task locations. Such practice, however, may easily compromise the location privacy of workers. In light of this, in this paper, we design, implement, and evaluate PPTA, a new system framework for location privacy-preserving task assignment in SC with strong security guarantees. PPTA takes advantage of only lightweight cryptography (such as additive secret sharing, function secret sharing, and secure shuffle), and provides a suite of tailored secure components required by practical location-based task assignment processes. Specifically, aiming for practical usability, PPTA is designed to flexibly support two realistic task assignment settings: (i) the online setting where tasks arrive and get processed at the SC platform one by one, and (ii) the batch-based setting where tasks arrive and get processed in a batch. Extensive experiments over a real-world dataset demonstrate that while providing strong security guarantees, PPTA supports task assignment with efficacy comparable to plaintext baselines and with promising performance.

Satisfaction-aware Task Assignment in Spatial Crowdsourcing

With the ubiquitous of GPS-equipped devices, spatial crowdsourcing (SC) technology has been widely utilized in our daily life. As a novel computing paradigm, it hires mobile users as workers who physically move to the location of the task and perform the task. Task assignment is a fundamental issue in SC. In real life, there are many complex tasks requiring different workers, among which the quality of worker cooperation and the price satisfaction of users should not be ignored. Hence, this paper examines a satisfaction-aware task assignment (SATA) problem with the goal of maximizing overall user satisfaction, where the user satisfaction integrates the satisfaction towards price and cooperation quality. The SATA problem has been proved to be NP-hard by reducing it from the k-set packing problem. In addition, two algorithms, namely, conflict-aware greedy (CAG) algorithm and game theoretic (GT) algorithm with an optimization strategy, are proposed for solving the SATA problem. The CAG algorithm can efficiently obtain a result with provable approximate bound, while the GT algorithm is proven to be convergent which can find a Nash equilibrium. Extensive experiments have demonstrated the effectiveness and efficiency of our proposed approaches on both real and synthetic datasets.

Cooperative task assignment in spatial crowdsourcing via multi-agent deep reinforcement learning

With the rapid development of mobile Internet, spatial crowdsourcing (SC) has become an emerging paradigm with many applications. As a key challenge in SC, the problem of task assignment has attracted extensive research. However, most previous work focus on the single mode setting where cooperation among workers is either allowed or prohibited. Moreover, only short-term benefit of either workers or requesters is considered separately. To address these issues, we first propose a new spatial crowdsourcing scenario that permits cooperation with no mandate among workers and tasks. Furthermore, we propose a multi-agent deep reinforcement learning (MADRL) solution for SC. Specifically, we extend the Advantage Actor–Critic (A2C) algorithm to multi-agent settings, and design a reward function that considers both local and global return. Through the game between agents, we generate a task assignment scheme that considers both workers’ and requesters’ long-term benefit. In order to improve the performance of our model, we further introduce the attention mechanism to guide information sharing between agents. We use simulations to conduct experimental evaluation on both synthetic and real-world datasets. Experimental results show that our proposed method outperforms other state-of-the-art task assignment algorithms in terms of worker profitability rate and task completion rate.

Multi-stage complex task assignment in spatial crowdsourcing

With the widespread application of smart devices, spatial crowdsourcing (SC) has been extensively integrated into daily life. Task assignment is a crucial issue in SC and has attracted much attention. Most prior studies on task assignment ignore the importance of dependency among tasks, resulting in some ineffective matching pairs and wasting workers’ time. To this end, we formulate a new problem in SC, abbreviated as multi-stage complex task assignment (MSCTA), which aims to assign workers to multi-stage complex tasks to maximize the total profit. Compared with existing studies, MSCTA can obtain more effective assignments by considering the dependency constraints among tasks. We prove that the MSCTA problem is NP-hard and propose a greedy algorithm and a game algorithm. Specifically, both algorithms iteratively utilize a filtering module to obtain a set of executable tasks (ET) for assignment. The greedy algorithm can quickly assign the most profitable workers to the subtasks in each round of ET, and obtain a provable approximate result. The game algorithm is proved to be convergent and can win a Nash equilibrium when processing the subtasks in each round of ET. Extensive experimental results demonstrate the efficiency of our algorithm.

TASC-MADM: Task Assignment in Spatial Crowdsourcing Based on Multiattribute Decision-Making

The methodology, formulating a reasonable task assignment to find the most suitable workers for a task and achieving the desired objectives, is the most fundamental challenge in spatial crowdsourcing. Many task assignment approaches have been proposed to improve the quality of crowdsourcing results and the number of task assignment and to limit the budget and the travel cost. However, these approaches have two shortcomings: (1) these approaches are commonly based on the attributes influencing the result of task assignment. However, different tasks may have different preferences for individual attributes; (2) the performance and efficiency of these approaches are expected to be improved further. To address the above issues, we proposed a task assignment approach in spatial crowdsourcing based on multiattribute decision-making (TASC-MADM), with the dual objectives of improving the performance as well as the efficiency. Specifically, the proposed approach jointly considers the attributes on the quality of the worker and the distance between the worker and the task, as well as the influence differences caused by the task’s attribute preference. Furthermore, it can be extended flexibly to scenarios with more attributes. We tested the proposed approach in a real-world dataset and a synthetic dataset. The proposed TASC-MADM approach was compared with the RB-TPSC and the Budget-TASC algorithm using the real dataset and the synthetic dataset; the TASC-MADM approach yields better performance than the other two algorithms in the task assignment rate and the CPU cost.

Deep Reinforcement Learning for Task Assignment in Spatial Crowdsourcing and Sensing

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.

Destination-Aware Task Assignment in Spatial Crowdsourcing: A Worker Decomposition Approach

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.

Fair task assignment in spatial crowdsourcing

With the pervasiveness of mobile devices, wireless broadband and sharing economy, spatial crowdsourcing is becoming part of our daily life. Existing studies on spatial crowdsourcing usually focus on enhancing the platform interests and customer experiences. In this work, however, we study the fair assignment of tasks to workers in spatial crowdsourcing. That is, we aim to assign tasks, considered as a resource in short supply, to individual spatial workers in a fair manner. In this paper, we first formally define an online bi-objective matching problem, namely the Fair and Effective Task Assignment (FETA) problem, with its special cases/variants of it to capture most typical spatial crowdsourcing scenarios. We propose corresponding solutions for each variant of FETA. Particularly, we show that the dynamic sequential variant, which is a generalization of an existing fairness scheduling problem, can be solved with an O ( n ) fairness cost bound ( n is the total number of workers), and give an O ( n/m ) fairness cost bound for the m -sized general batch case ( m is the minimum batch size). Finally, we evaluate the effectiveness and efficiency of our algorithm on both synthetic and real data sets.

An Approximation Algorithm for Bounded Task Assignment Problem in Spatial Crowdsourcing

Spatial crowdsourcing, a human-centric compelling paradigm in performing spatial tasks, has drawn rising attention. Task assignment is of paramount importance in spatial crowdsourcing. Existing studies often use heuristics of various kinds to solve task assignment problems. These schemes usually only apply some specific cases, once the environment changes, the efficiency of the algorithms is significantly reduced. In this paper, we first introduce a taxonomy of task assignment in spatial crowdsourcing. Next, we design an approximation algorithm and get an efficient solution for the important problem, namely, Bounded and Heterogeneous Task Assignment (BHTA), such that the sum of the rewards of workers is maximized subject to multiple constraints. We prove that the BHTA problem is NP-hard. Subsequently, we propose a constant-ratio approximation algorithm based on partition and shifting method to achieve the assignment solution. To meet with the workers' dynamism, we further devise a greedy algorithm and provide theoretical guarantee. Experiments on synthetic and real datasets demonstrate the efficiency of our strategy over previous methods. So far as we know, this paper is the first attempt to give a constant-ratio approximation for such task assignment problems in spatial crowdsourcing.

SRA: Secure Reverse Auction for Task Assignment in Spatial Crowdsourcing

In this paper, we study a new type of spatial crowdsourcing, namely competitive detour tasking, where workers can make detours from their original travel paths to perform multiple tasks, and each worker is allowed to compete for preferred tasks by strategically claiming his/her detour costs. The objective is to make suitable task assignment by maximizing the social welfare of crowdsourcing systems and protecting workers’ private sensitive information. We first model the task assignment problem as a reverse auction process. We formalize the winning bid selection of reverse auction as an $n$ n -to-one weighted bipartite graph matching problem with multiple 0-1 knapsack constraints. Since this problem is NP-hard, we design an approximation algorithm to select winning bids and determine corresponding payments. Based on this, a Secure Reverse Auction (SRA) protocol is proposed for this novel spatial crowdsourcing. We analyze the approximation performance of the proposed protocol and prove that it has some desired properties, including truthfulness, individual rationality, computational efficiency, and security. To the best of our knowledge, this is the first theoretically provable secure auction protocol for spatial crowdsourcing systems. In addition, we also conduct extensive simulations on a real trace to verify the performance of the proposed protocol.

User experience-driven secure task assignment in spatial crowdsourcing

With the ubiquity of mobile devices and wireless networks, Spatial Crowdsourcing (SC) has earned considerable importance and attention as a new strategy of problem-solving. Tasks in SC have location constraints and workers need to move to certain locations to perform them. Current studies mainly focus on maximizing the benefits of the SC platform. However, user average waiting time, which is an important indicator of user experience, has been overlooked. To enhance user experience, the SC platform needs to collect lots of data from both workers and users. During this process, the private information may be compromised if the platform is not trustworthy. In this paper, we first define user experience-driven secure task assignment problem and propose two privacy-preserving online task assignment strategies to minimize the average waiting time. We securely construct an encrypted bipartite graph to protect private data. Based on this encrypted graph, we propose a secure Kuhn-Munkres algorithm to realize task assignment without privacy disclosure. Theoretical analysis shows the security of our approach and experimental results demonstrates its efficiency and effectiveness.

Preference-aware Task Assignment in Spatial Crowdsourcing: from Individuals to Groups

With the ubiquity of smart devices, Spatial Crowdsourcing (SC) has emerged as a new transformative platform engaging mobile users to perform tasks by physically traveling to specified locations. In this paper, we propose a novel preference-aware task assignment system based on workers' temporal preferences, which includes two components: History-based Context-aware Tensor Decomposition (HCTD) for workers' temporal preferences modeling and preference-aware task assignment. We model workers' preferences with a three-dimension tensor. Supplementing missing entries of the tensor through HCTD with the assistant of historical data and other two context matrices, we recover workers' preferences for different categories of tasks in different time slots. Several preference-aware individual task assignment algorithms are then devised, aiming to maximize the total number of task assignments at every time instance, where we give higher priorities to workers who are more interested in the tasks. To make our proposed framework applicable to more scenarios, we further optimize the original framework by proposing strategies to allow each task to be assigned to a group of workers such that the task can be completed by these workers simultaneously, where workers' tolerable waiting time and consensus are considered. We conduct extensive experiments using a real dataset, verifying the practicability of the methods.

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.