Spatial Crowdsourcing Platform Research Articles

Coalition-based task assignment with priority-aware fairness in spatial crowdsourcing

With the widespread use of networked and geo-positioned mobile devices, e.g., smartphones, Spatial Crowdsourcing (SC), which refers to the assignment of location-based tasks to moving workers, is drawing increasing attention. One of the critical issues in SC is task assignment that allocates tasks to appropriate workers. We propose and study a novel SC problem, namely Coalition-based Task Assignment (CTA), where the spatial tasks (e.g., home improvement and furniture installation) may require more than one worker (forming a coalition) to cooperate to maximize the overall rewards of workers. We design a greedy and an equilibrium-based CTA approach. The greedy approach forms a set of worker coalitions greedily for performing tasks and uses an acceptance probability to identify high-value task assignments. In the equilibrium-based approach, workers form coalitions in sequence and update their strategies (i.e., selecting a best-response task), to maximize their own utility (i.e., the reward of the coalition they belong to) until a Nash equilibrium is reached. Since the equilibrium obtained is not unique and optimal in terms of total rewards, we further propose a simulated annealing scheme to find a better Nash equilibrium. To achieve fair task assignments, we optimize the framework to distribute rewards fairly among workers in a coalition based on their marginal contributions and give workers who arrive first at the SC platform highest priority. Extensive experiments demonstrate the efficiency and effectiveness of the proposed methods on real and synthetic data.

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.

Privacy-preserving cooperative online matching over spatial crowdsourcing platforms

With the continuous development of spatial crowdsourcing platform, online task assignment problem has been widely studied as a typical problem in spatial crowdsourcing. Most of the existing studies are based on a single-platform task assignment to maximize the platform's revenue. Recently, cross online task assignment has been proposed, aiming at increasing the mutual benefit through cooperations. However, existing methods fail to consider the data privacy protection in the process of cooperation and cause the leakage of sensitive data such as the location of a request and the historical data of cooperative platforms. In this paper, we propose Privacy-preserving Cooperative Online Matching (PCOM), which protects the privacy of the users and workers on their respective platforms. We design a PCOM framework and provide theoretical proof that the framework satisfies the differential privacy property. We then propose two PCOM algorithms based on two different privacy-preserving strategies. Extensive experiments on real and synthetic datasets confirm the effectiveness and efficiency of our algorithms.

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

Unlike crowdsourcing, Spatial Crowdsourcing (SC) requires workers to travel to a specific physical location to accomplish a task. Due to its open concept, the platform accepts any interested individual as workers or task requesters, including those who may be unreliable and untrustworthy. Deploying untrustworthy workers in spatial tasks can negatively impact the quality of the completed tasks, thus threatening the sustainability of the SC platform. Recent research has been carried out to evaluate workers’ trustworthiness based on the Trust and Reputation (TR) system. Current TR system approaches for evaluating workers’ trustworthiness are mostly relying on a single trust or reputation factor, and the decisions are mainly binary. This binary representation of trustworthiness is considerably rigid and may cause severe repercussions like, an untrustworthy worker who could be the victim of partial ratings may end up not getting any kind of spatial tasks from the system, or a trustworthy worker who may have malicious intention may be allocated a spatial task. To address these limitations, we propose a novel framework that allocates every spatial task according to a workers’ degree of perceived trustworthiness computed based on multi-criteria trust and reputation factors using a Mamdani fuzzy inference system. Our work considers historical ratings to calculate reputation value, applies sentiment analysis to infer trust value, implements Mamdani fuzzy inference to determine trustworthiness degree, and introduces the concept of referral to mitigate worker cold-start problems in spatial crowdsourcing. Our experimental findings on the Yelp real-world datasets demonstrate the reliability of the proposed framework to allocate every spatial task of various types to the most trustworthy workers from huge crowds of available workers. When evaluated against other baseline approaches, our approach achieves greater accuracy in allocating the right tasks to the most trustworthy workers.

Opinions of Ghanaians on the management of petroleum revenue in Ghana

ABSTRACT Scholars of natural resource governance argue that national and local governments must engage ordinary community members. When ordinary community members access information about the utilization of natural resource revenue and get an opportunity to provide feedback, the revenue management improves. In this article, the authors engaged Ghanaians through a spatial crowdsourcing platform for their opinion about petroleum management revenue in Ghana. The participants accessed the platform via their mobile phones and completed a survey on their opinions about petroleum revenue management, the Free Senior High School program, and their priority areas for petroleum revenue funding in Ghana. The results suggest that ordinary community members, and particularly women, seemed less informed about the management of petroleum revenue in Ghana. Furthermore, Ghanaians’ opinions regarding their prioritized projects for petroleum revenue funding vary geographically. The authors conclude that decision-makers can use spatial crowdsourcing to engage ordinary community members in natural resource revenue management.

Spatial crowdsourcing in natural resource revenue management

In many resource-rich developing countries, policymakers, academics, and practitioners alike seek to promote citizen engagement and monitoring in the management of natural resource revenues. However, many of the approaches to improve citizen engagement have not been effective as citizens often lack access to relevant information and opportunities to voice their concerns, and governments have faced financial challenges in engaging citizens. In this article, the authors propose spatial crowdsourcing as an alternative to the traditional ways of encouraging citizen engagement. The proposed approach is illustrated using a simple, intuitive multimedia-based spatial crowdsourcing platform that was tested among farmers who were benefiting from a petroleum-funded project in Ghana. The farmers accessed the platform via their mobile phones and completed a survey relating to the project and petroleum revenue management in Ghana. The findings suggest that spatial crowdsourcing is a promising approach to promote informed citizen engagement in the context of natural resource revenue management. In particular, the farmers indicated that an opportunity to give feedback on the project through a user-friendly platform was very important for them. Furthermore, the representatives of both the government and an oversight body for petroleum revenue management regarded spatial crowdsourcing as a useful tool for collecting feedback on petroleum-funded projects and to increase citizen engagement in natural resource management in general. The authors conclude that although spatial crowdsourcing can help in citizen engagement, its effectiveness in the management of natural resource revenues depends on behavioural changes in governments and citizens.

GPSC: A Grid-Based Privacy-Reserving Framework for Online Spatial Crowdsourcing

Spatial crowdsourcing (SC) allows requesters to crowdsource tasks to workers based on location proximity. To preserve privacy, the location should not be disclosed to untrustworthy entities (even the SC platform). Previous solutions to preserve workers’ location privacy require an online trusty third party (TTP), which is not practical in reality. In this paper, we design a framework that allows the SC platform to assign tasks to nearest workers in an online manner without knowing their actual locations. We propose an encryption algorithm to encrypt the locations of tasks and workers, and design an indexing method that assigns tasks to workers without losing too much privacy. We prove that there exists a trade-off between efficiency and security theoretically, which can be controlled based on user preference. We verify our method on real-world datasets and experimental results show that our method is efficient, effective and practical.

Location Privacy-Preserving Distance Computation for Spatial Crowdsourcing

Data privacy, especially location privacy, is paramountly important for protecting individual's information in smart cities in the big data era. One of the examples is in spatial crowdsourcing (SC). It enables people not only to issue spatiotemporal tasks to ask for help as requesters but also to solve others' tasks as workers on the SC platform. While SC brings convenience to people, it also produces severe location privacy problems, which have been recently paid more attention from both academia and industries. In this article, we address the location privacy problem in SC in a practical and secure way. We propose a location privacy-preserving framework for almost all existed mainstream distance computations in the SC system, namely, Euclidean-L3P, Minkowski-L3P, Manhattan-L3P, and Chebyshev-L3P, among which the first two are constructed based on homomorphic encryption and composite-order multilinear mapping while the latter two on the homomorphic encryption and prefix membership verification approach. Location privacy is resolved because of the above techniques having enabled that all distance computations are evaluated through ciphertexts without disclosing any location information. Security analysis shows that our framework can prevent a strong adversary from obtaining participants' location privacy. Performance analysis evaluates computation and communication overheads between protocols. The results show that Euclidean-L3P is more efficient than Manhattan-L3P and Chebyshev-L3P in terms of computation overheads when the SC applications require a small number of participants, a large plaintext space, and a small number of base stations. Moreover, compared with Manhattan-L3P and Chebyshev-L3P, Euclidean-L3P is a better choice in terms of communication overhead.

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.

Mechanism Design for Wireless Powered Spatial Crowdsourcing Networks

Wireless power transfer (WPT) is a promising technology to prolong the lifetime of the sensors and communication devices, i.e., workers, in completing crowdsourcing tasks by providing continuous and cost-effective energy supplies. In this paper, we propose a wireless powered spatial crowdsourcing framework which consists of two mutually dependent phases: task allocation phase and data crowdsourcing phase. In the task allocation phase, we propose a Stackelberg game based mechanism for the spatial crowdsourcing platform to efficiently allocate spatial tasks and wireless charging power to each worker. In the data crowdsourcing phase, the workers may have an incentive to misreport its real working location to improve its utility, which causes adverse effects to the spatial crowdsourcing platform. To address this issue, we present three strategyproof deployment mechanisms for the spatial crowdsourcing platform to place a mobile base station, e.g., vehicle or robot, which is responsible for transferring the wireless power and collecting the crowdsourced data. As the benchmark, we first apply the classical median mechanism and evaluate its worst-case performance. Then, we design a conventional strategyproof deployment mechanism to improve the expected utility of the spatial crowdsourcing platform under the condition that the workers' locations follow a known geographical distribution. For a more general case with only the historical location data available, we propose a deep learning based strategyproof deployment mechanism to maximize the spatial crowdsourcing platform's utility. Extensive experimental results based on synthetic and real-world datasets reveal the effectiveness of the proposed framework in allocating tasks and charging power to workers while avoiding the dishonest worker's manipulation.

Proximity-Aware Worker Preferred Task Allocation for Spatial Crowdsourcing

Abstract Crowdsourcing is a mechanism of outsourcing a task to a multitude of people. It includes workers, tasks and a web-based platform to intermediate between the tasks and workers. Spatial crowdsourcing is a paradigm of crowdsourcing, that extends beyond the traditional web-based crowdsourcing by including physical entities of the geographic system. This data could be in the form of topology, location or longitude, and latitude coordinates. The task allocation on a spatial crowdsourcing platform based on proximity between task and worker is challenging since the worker must be physically present at the location to complete the task. Existing methods use Euclidean distance measure for task allocation. However, it offers less accuracy when applied in a 3D plane. Hence, this paper proposes a novel task allocation scheme through a score calculation that uses Haversine distance measure incorporated with worker preference and reputation for allocating the task. We validate the proposed task allocation model by comparing the task allocation cost between 2D and 3D planes, using real and synthetic data. Our observations clearly display considerable cost-benefit and increased task allocation in our proposed solution.

Spatial crowdsourcing based on Web mapping services

With the rapid development of mobile Internet and smartphones, crowdsourcing marketplaces has extended to spatial crowdsourcing (SC), where crowd workers perform spatial tasks (i.e., tasks related to a location) in the physical world using their mobile phones. Currently, most of existing spatial crowdsourcing algorithms (e.g., task assignment) assume the underlying road network is given or simply base on Euclidean space. However, in fact, not every spatial crowdsourcing platform has enough resources to possess map data (e.g., the road network and live traffic information) by itself, especially for these that are small or startup companies, while the Euclidean distance is usually not accurate enough for SC processing. To overcome these limitations, we propose a spatial crowdsourcing system based on Web mapping services, i.e., the spatial crowdsourcing platform can subscribe distance, live travel time and detailed route information from Web mapping services through their APIs, and utilize these retrieved map data for SC processing; furthermore, workers and task requesters can also snap their real locations to the locations on the road for privacy protection through the APIs. As retrieving map data from Web mapping services is much more expensive than accessing local data, we take the advantage of the pruning and route sharing approaches to reduce the number of external requests to Web mapping services, and our experimental results have proved their effectiveness.

GMission

As one of the successful forms of using Wisdom of Crowd, crowdsourcing, has been widely used for many human intrinsic tasks, such as image labeling, natural language understanding, market predication and opinion mining. Meanwhile, with advances in pervasive technology, mobile devices, such as mobile phones and tablets, have become extremely popular. These mobile devices can work as sensors to collect multimedia data(audios, images and videos) and location information. This power makes it possible to implement the new crowdsourcing mode: spatial crowdsourcing. In spatial crowdsourcing, a requester can ask for resources related a specific location, the mobile users who would like to take the task will travel to that place and get the data. Due to the rapid growth of mobile device uses, spatial crowdsourcing is likely to become more popular than general crowdsourcing, such as Amazon Turk and Crowdflower. However, to implement such a platform, effective and efficient solutions for worker incentives, task assignment, result aggregation and data quality control must be developed. In this demo, we will introduce gMission, a general spatial crowdsourcing platform, which features with a collection of novel techniques, including geographic sensing, worker detection, and task recommendation. We introduce the sketch of system architecture and illustrate scenarios via several case analysis.