Spatial Two-Sided Online Bottleneck Matching With Deadlines

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 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.

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.

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.

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.

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.

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.

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 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.

Network Intrusion Detection Systems (NIDSs) are an increasingly important tool for the prevention and mitigation of cyber attacks. Over the past years, a lot of research efforts have aimed at leveraging the increasingly powerful models of Machine Learning (ML) for this purpose. A number of labelled synthetic datasets have been generated and made publicly available by researchers, and they have become the benchmarks via which new ML-based NIDS classifiers are being evaluated. Recently published results show excellent classification performance with these datasets, increasingly approaching 100 percent performance across key evaluation metrics such as Accuracy, F1 score, AUC, etc. Unfortunately, we have not yet seen these excellent academic research results translated into practical NIDS systems with such near-perfect performance. This motivated our research presented in this paper, where we analyse the statistical properties of the benign traffic in three of the more recent and relevant NIDS datasets, (CIC_IDS, UNSW_NB15, TON_IOT), by converting them into a common flow format. As a comparison, we consider two datasets obtained from real-world production networks, one from a university network and one from a medium size Internet Service Provider (ISP). Our results show that the two real-world datasets are quite similar among themselves in regards to most of the considered statistical features. Equally, the three synthetic datasets are also relatively similar within their group. However, and most importantly, our results show a distinct difference of most of the considered statistical features between the three synthetic datasets and the two real-world datasets. Since ML relies on the basic assumption of training and test datasets being sampled from the same distribution, this raises the question of how well the performance results of ML-classifiers trained on the considered synthetic datasets can translate and generalise to real-world networks. We believe this is an interesting and relevant question which provides motivation for further research in this space.

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

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 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.

Spatial crowdsourcing (SC) is a popular distributed problem-solving paradigm that harnesses the power of mobile workers (e.g., smartphone users) to perform location-based tasks (e.g., checking product placement or taking landmark photos). Typically, a worker needs to travel physically to the target location to finish the assigned task. Hence, the worker’s familiarity level on the target location directly influences the completion quality of the task. In addition, from the perspective of the SC server, it is desirable to finish all tasks with a low recruitment cost. Combining these issues, we propose a Bi-Objective Task Planning (BOTP) problem in SC, where the server makes a task assignment and schedule for the workers to jointly optimize the workers’ familiarity levels on the locations of assigned tasks and the total cost of worker recruitment. The BOTP problem is proved to be NP-hard and thus intractable. To solve this challenging problem, we propose two algorithms: a divide-and-conquer algorithm based on the constraint method and a heuristic algorithm based on the multi-objective simulated annealing algorithm. The extensive evaluations on a real-world dataset demonstrate the effectiveness of the proposed algorithms.

A location privacy protection method in spatial crowdsourcing