Reliable diversity-based spatial crowdsourcing by moving workers

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.

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.

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

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.

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.

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.

These days, Online To Offline (O2O) platforms have been developing rapidly because of the popularization of smart phones and Mobile Internet. Spatial crowdsourcing, a burgeoning area in O2O market, is gaining more and more attention. It is a typical spatial crowdsourcing scenario in which an employer publishes a task and some workers will help him or her to accomplish it. However, most of previous work only considers the spatial information of workers and tasks, but ignores the individual variations among workers. In this paper, we raise a new problem called Software Development Team Formation (SDTF) problem, which aims to find a team of workers whose ability satisfies the requirement of the task. After showing the problem is NP-hard, we propose three greedy algorithms to approximately solve the problem. Besides, extensive experiments are conducted on synthetic and real datasets, which verify the effectiveness and efficiency of our algorithms.

Unlike crowdsourcing, spatial crowdsourcing requires workers to travel to a specific physical location to complete a task. As a spatial crowdsourcing system can deploy any interested workers, the system is prone to unreliable and untrustworthy workers. Therefore, finding and allocating tasks to trustworthy workers is paramount to ensure the quality and sustainability of the system. Trust and reputation are key factors for evaluating workers’ trustworthiness. Unfortunately, current approaches to the evaluation of workers’ trustworthiness mostly rely on a single trust or reputation factor, and the trustworthiness is mainly based on a binary decision. To address these limitations, we propose a spatial task allocation framework that allocates every task in accordance with a workers’ degree of perceived trustworthiness, which is computed based on multi-criteria trust and reputation factors and a fuzzy method. Our approach evaluates workers’ reputations based on the ratings of workers’ past completed tasks, and workers’ trust is evaluated based on the sentiment analysis of their past tasks’ reviews. The attained reputation and trust values are then combined using a fuzzy inference system to obtain the degree of trustworthiness of each worker for every spatial task. Our experimental findings show that the proposed framework is able to find and allocate every spatial task to the most trustworthy workers from a pool of available workers. When evaluated against other baseline approaches, our proposed approach demonstrates the highest accuracy in terms of allocating every task to workers in accordance with their degree of perceived trustworthiness.

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.

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.

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.

Owing to its benefits such as flexibility, scalability, and interoperability, Software-Defined Networking (SDN) has been incorporated into Internet of Vehicles (IoV) to cope with the increasing demands of vehicular applications. The integration of SDN and IoV, namely SDN-IoV, can enrich many new applications for intelligent transportation such as traffic monitoring, smart navigation, and self-driving. The spatial crowdsourcing technology has been adopted as an effective data collection and processing method that is the premise of various SDN-IoV applications. However, as huge amounts of data are generated in spatial crowdsourcing services, the data privacy and security has become a key challenge for SDN-IoV. To overcome abovementioned challenge, a Deep Reinforcement Learning (DRL) and Blockchain empowered Spatial Crowdsourcing System (DB-SCS) is proposed. In DB-SCS, we design an improved multi-blockchain structure and a blockchain-based hierarchical task management method, which divide the spatial tasks into different categories according to the privacy requirements and the areas of the task and then decompose different categories of tasks and task receivers into sub-blockchains. While guaranteeing the data privacy, DB-SCS can also enhance the spatial crowdsourcing performance by using the proposed DRL-based management strategy to dynamically select the consensus algorithm, block size, and block generation rule. Extensive simulation experiments demonstrate that the DB-SCS can obtain high throughput, low overhead, and data privacy under various SDN-IoV scenarios.