Multi-stage complex task assignment in spatial crowdsourcing

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 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 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.

This work was partially supported by King Abdullah University of Science and Technology (KAUST) and the National Natural Science Foundation of China under Grant Nos. 61572336, 61632016, 61402313, 61572335, and 61472337.

As a novel intelligent sensing paradigm, spatial crowdsourcing has received extensive attention. Task assignment is a key issue in spatial crowdsourcing. In practice, tasks are unevenly distributed in time and space. Accordingly, the problem of cross task assignment attracts growing attention in both industry and academia. Although there has been a research on this problem, it focuses only on maximizing total revenues for inner platforms. Therefore, it can also be improved to bring a multi-win situation for outer workers and task requesters as well as the inner platform. Inspired by this, we first formulate a new cross dynamic task assignment (CDTA) problem by introducing the reputation scores of workers, and prove it to be NP-hard. For the CDTA problem, a hybrid batch-based framework is presented on the basis of a new cross-platform incentive mechanism and a hybrid batch processing strategy, which are efficient in solving the problem of uneven spatial and time distribution of tasks, respectively. After that, a KM-based algorithm and a density-aware greedy algorithm are proposed to gain an accurate assignment result of tasks in each batch and good performance, respectively. Furthermore, the CDTA problem is modeled as a potential game that is proven to have at least a pure Nash Equilibrium theoretically. Last but not least, a game-theoretic approach is developed to maximize the revenues of the inner platform and outer workers at the same time. Extensive experiments on both real and synthetic datasets are conducted to demonstrate the effectiveness and efficiency of the proposed algorithms.

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.

Spatial crowdsourcing, e.g., Vehicular Ad-Hoc Network (VANET)-based spatial crowdsourcing, is a new distributed computing paradigm, in which task assignments highly rely on the wisdom of the crowdsourcing platform. However, with the increase in user data leakage incidents, the existing task assignment methods are no longer sufficient to meet the privacy requirements of users. In the existing VANET-based spatial crowdsourcing, task assignments are usually performed by a trusted third party based on the real locations of tasks and drivers (task performers), which may lead to the leakage of the users' locations. Furthermore, the drivers usually prefer to query the nearest tasks to them in a geometric range, at this point sending query requests to remote crowdsourcing servers increases unnecessary response delays. To assign tasks securely and efficiently, we propose a privacy-preserving task assignment scheme based on OT and edge computing (PriTAEC), which is the first to apply Oblivious Transfer (OT) and edge computing to preserve the location privacy of VANET-based spatial crowdsourcing. In the scheme, we first utilize Hilbert Curve and Bloom Filter to implement location range queries. Then, we use geohash location encoding and Oblivious Transfer to achieve fine-grained location matching. In particular, we design a task assignment algorithm with an offline-online phase to improve the efficiency of task assignments. Finally, we prove the security of the scheme and evaluate its performance, which shows our scheme is secure and efficient.

Spatial crowdsourcing (SC) task assignment is to find the optimal worker for the task from abundant alternative workers based on the information of the task and workers, such as location, time, and ability. This information will undoubtedly reveal the privacy of both the task and workers. The disclosure of private information is a crucial issue constraining the development of SC. To this end, various privacy-preserving task assignments have been proposed to protect privacy by obfuscating or encrypting information. Fuzzy processing will limit matching accuracy, while encrypted information will increase the time cost of data computation. Therefore, this paper proposes a privacy-preserving map retrieval task assignment scheme (pMATE), which can divide the map and accurately retrieve the optimal workers according to this division. In pMATE, relevant information about tasks and workers is encrypted, and neighboring workers are searched based on the task presence partition. The task location can also be hidden in that partition. Partitioned retrieval reduces the amount of encrypted data needed to be matched. Furthermore, to reduce the problem of multiple communications during encrypted data comparison, we propose the Find MinNumber (FMN) algorithm, which can determine the optimal worker or top-k optimal workers need only two communications. Experimental evaluations of real-world data show that pMATE is efficient and accurate.

With the pervasiveness of GPS-enabled smart devices and increased wireless communication technologies, Spatial Crowdsourcing (SC) has drawn increasing attention in assigning location-sensitive tasks to moving workers. In real-world scenarios, for the complex tasks, SC is more likely to assign each task to more than one worker, called Group Task Assignment (GTA), for the reason that an individual worker cannot complete the task well by herself. It is a challenging issue to assign worker groups the tasks that they are interested in and willing to perform. In this paper, we propose a novel framework for group task assignment based on worker groups’ preferences, which includes two components: Social Impact-based Preference Modeling (SIPM) and Preference-aware Group Task Assignment (PGTA). SIPM employs a Bipartite Graph Embedding Model (BGEM) and the attention mechanism to learn the social impact-based preferences of different worker groups on different task categories. PGTA utilizes an optimal task assignment algorithm based on the tree-decomposition technology to maximize the overall task assignments, in which we give higher priorities to the worker groups showing more interests in the tasks. Our empirical studies based on a real-world dataset verify the practicability of our proposed framework.

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.

Recently, crowdsourcing applications for smart cities have become more and more popular due to its higher work efficiency and lower work costs. However, the reasonable task assignment is still one of the important challenges for crowdsourcing. The existing researches on crowdsourcing task assignment focus on the tradeoff between maximizing the utility of platforms and minimizing the cost of requesters, but they lack of the considerations of stability and satisfactory. In this paper, we propose an intelligent multi attributes crowdsourcing task assignment with stability and satisfactory, called TASS. TASS can exploit the multi attributes to solve the stability of the transaction, and adopt the game theory to maximize the satisfaction of both sides during the task assignment. Next, we theoretically prove that the task assignment mechanism is truthfulness, individual rationality, stable and satisfactory assignment, and budget-balanced. Finally, we evaluate the performances of TASS with the state-of-the-art task assignment works. The experimental results show that TASS is better than the state-of-the-art task assignment works in terms of truthfulness, individually rationality, stable and satisfactory assignment, and balanced budget.

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

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.

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.

Macrotasks like document writing, product design, or web development, require expert skills and the collective contributions of the crowds. In fact, there are few studies give close attention to the workers with social network in crowdsourcing. Social networks provide an available platform for cooperation among social workers. In this paper, we discuss the complex task assignment in crowdsourcing where the workers are in a social network. A Truthful Mechanism for the task assignment in Crowdsourcing with Social Network (TMC-SN) is proposed to solve the challenges in this scenario. The problem is modeled as an auction, where the requesters are buyers, workers are sellers, and the crowdsourcing platform serves as an auctioneer. In order to find out the most appropriate team, TMC-SN measures Worker Fitness of the worker for the team from both Marginal Contribution and Team Cohesion. We demonstrate that TMC-SN has fine economic properties such as truthfulness, individual rationality, and budget balance. The evaluation results show that TMC-SN is advantageous in terms of Social Welfare and favorable for the workers.