Oriented Online Route Recommendation for Spatial Crowdsourcing Task Workers

With the continuous popularization of various mobile devices, spatial crowdsourcing tasks have also developed rapidly. Recently, vehicles are being used to complete spatial crowdsourcing tasks. Drivers and passengers can provide the ability to actively complete query tasks, while sensors on the vehicle can passively complete sensing tasks. In this paper, we consider a spatial crowdsourcing scenario where a worker can complete a location-based query task and also a sensing task at the same time. The formal description of the problem is given, and then a multiobjective ant colony algorithm for vehicle-based crowdsourcing is proposed to optimize both task query reliability and sensing coverage. The performance of the algorithm is evaluated on a real-world traffic trace dataset.

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.

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.

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 (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.

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.

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.

Spatial Crowdsourcing Platforms (SCP) are systems that act as brokers that help people requesting location dependent tasks to find suitable workforce to accomplish it. This kind of system has been source of much interest for both academy and industry. However, little has been done to define a crowdsourcing platform and its technical details precisely. Furthermore, much of the research works on this subject have focused specifically on crowdsensing platforms, a special case of SCP. There is also a need to contrast what has been proposed, so far, in this matter with real world SCPs in order to refine and validate the current state of the art results. In this paper, we discuss a list of requirements for SCPs. This list has been identified and validated by an analysis of scientific related works and several commercial SCPs. Based on this list, we also propose a generic architecture for SCPs that is meant to help the understanding of SCPs and the design of better spatial crowdsourcing solutions.

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 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.

Spatial crowdsourcing engages individuals to collect and process social, environmental and other information with spatio-temporal features, making the data collection and analysis efficient, scalable and smart. The quality of task fulfillment strongly depends on the set of recruited workers. The more suitable workers are engaged, the better results may be obtained, meanwhile, the more privacy of workers will be disclosed. In this paper, we propose LATE, a novel location privacy-aware task recommendation framework in spatial crowdsourcing, which enables spatial crowdsourcing servers (SC-servers) to recommend spatial tasks released by customers to the workers in geocast regions. Based on Lagrange Interpolating Polynomials, we design a privacy-preserving location matching mechanism to allow the SC-server to determine whether a worker is in geocast region of a spatial task or not without any knowledge about the task's geocast region and the worker's location. In addition, the spatial tasks and crowdsourcing reports are protected against privacy leakage for both customers and workers. Finally, we discuss the security properties of LATE and demonstrate its efficiency on computation and communication.

A Trustworthiness-Aware Spatial Task Allocation using a Fuzzy-based Trust and Reputation System 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.

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.

In order to further improve the enthusiasm of spatial crowdsourcing workers, considering the service quality of workers, different incentive strategies are proposed and tasks are assigned. Firstly, the incentive model is constructed from the unit time revenue of task and online idle time, and the evaluation function of the evaluation model is constructed; Secondly, the task allocation is transformed into a combinatorial optimization problem by delay matching, and an improved glowworm swarm algorithm is proposed to solve the problem by discrete coding, introducing six kinds of mobile modes, adaptive probability matching and infeasible solution processing; Finally, the algorithm is used to solve the task allocation. The experimental results show that compared with the travel cost minimization strategy and random allocation strategy, the positive incentive index of the proposed strategy is improved by 11.79% and 14.60% respectively, and the fair incentive index is improved by 0.83% and 0.22% respectively, which can effectively improve the positive incentive range and incentive fairness of workers.