Crowdsourcing Paradigm Research Articles

ExCrowd: A Blockchain Framework for Exploration-Based Crowdsourcing

Because of the rise of cryptocurrencies and decentralized apps, blockchain technology has generated a lot of interest. Among these is the emergent blockchain-based crowdsourcing paradigm, which eliminates the centralized conventional mechanism servers in favor of smart contracts for task and reward allocation. However, there are a few crucial challenges that must be resolved properly. For starters, most reputation-based systems favor high-performing employees. Secondly, the crowdsourcing platform’s expensive service charges may obstruct the growth of crowdsourcing. Finally, unequal evaluation and reward allocation might lead to job dissatisfaction. As a result, the aforementioned issues will substantially impede the development of blockchain-based crowdsourcing systems. In this study, we introduce ExCrowd, a blockchain-based crowdsourcing system that employs a smart contract as a trustworthy authority to properly select workers, assess inputs, and award incentives while maintaining user privacy. Exploration-based crowdsourcing employs the hyperbolic learning curve model based on the conduct of workers and analyzes worker performance patterns using a decision tree technique. We specifically present the architecture of our framework, on which we establish a concrete scheme. Using a real-world dataset, we implement our model on the Ethereum public test network leveraging its reliability, adaptability, scalability, and rich statefulness. The results of our experiments demonstrate the efficiency, usefulness, and adaptability of our proposed system.

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.

Optimal Task Partition with Delay Requirement in Mobile Crowdsourcing

Mobile crowdsourcing takes advantage of mobile devices such as smart phones and tablets to process data for a lot of applications (e.g., geotagging for mobile touring guiding monitoring and spectrum sensing). In this paper, we propose a mobile crowdsourcing paradigm to make a task requester exploit encountered mobile workers for high-quality results. Since a task may be too complex for a single worker, it is necessary for a task requester to divide a complex task into several parts so that a mobile worker can finish a part of the task easily. We describe the task crowdsourcing process and propose the worker arrival model and task model. Furthermore, the probability that all parts of the complicated task are executed by mobile workers is introduced to evaluate the result of task crowdsourcing. Based on these models, considering computing capacity and rewards for mobile workers, we formulate a task partition problem to maximize the introduced probability which is used to evaluate the result of task crowdsourcing. Then, using a Markov chain, a task partition policy is designed for the task requester to realize high-quality mobile crowdsourcing. With this task partition policy, the task requester is able to divide the complicated task into precise number of parts based on mobile workers’ arrival, and the probability that the total parts are executed by mobile workers is maximized. Also, the invalid number of task assignment attempts is analyzed accurately, which is helpful to evaluate the resource consumption of requesters due to probing potential workers. Simulations show that our task partition policy improves the results of task crowdsourcing.

An Optimization and Auction-Based Incentive Mechanism to Maximize Social Welfare for Mobile Crowdsourcing

Mobile crowdsourcing is an emerging crowdsourcing paradigm, which generates large-scale sensing tasks and sensing data. One of the major issues in mobile crowdsourcing is how to maximize social welfare through selecting appropriate sensing tasks for crowd workers and selecting appropriate workers for sensing tasks such that it can improve the effectiveness and efficiency of mobile crowdsourcing. This paper proposes an incentive mechanism to maximize social welfare for mobile crowdsourcing and, respectively, investigates worker-centric task selection and platform-centric worker selection. This paper applies an optimization algorithm in task selection for mobile crowdsourcing systems. A discrete particle swarm optimization (DPSO) algorithm for worker-centric task selection is designed to maximize the utilities of workers. In addition, a platform-centric worker selection method, which integrates multiattribute auction and two-stage auction, is proposed to maximize the utility of the platform. The performance of the proposed incentive mechanism is evaluated through experiments. The experimental results show that the proposed incentive mechanism can improve the efficiency and truthfulness of mobile crowdsourcing effectively.

A Data-Driven Game Theoretic Strategy for Developers in Software Crowdsourcing: A Case Study

Crowdsourcing has the advantages of being cost-effective and saving time, which is a typical embodiment of collective wisdom and community workers’ collaborative development. However, this development paradigm of software crowdsourcing has not been used widely. A very important reason is that requesters have limited knowledge about crowd workers’ professional skills and qualities. Another reason is that the crowd workers in the competition cannot get the appropriate reward, which affects their motivation. To solve this problem, this paper proposes a method of maximizing reward based on the crowdsourcing ability of workers, they can choose tasks according to their own abilities to obtain appropriate bonuses. Our method includes two steps: Firstly, it puts forward a method to evaluate the crowd workers’ ability, then it analyzes the intensity of competition for tasks at Topcoder.com—an open community crowdsourcing platform—on the basis of the workers’ crowdsourcing ability; secondly, it follows dynamic programming ideas and builds game models under complete information in different cases, offering a strategy of reward maximization for workers by solving a mixed-strategy Nash equilibrium. This paper employs crowdsourcing data from Topcoder.com to carry out experiments. The experimental results show that the distribution of workers’ crowdsourcing ability is uneven, and to some extent it can show the activity degree of crowdsourcing tasks. Meanwhile, according to the strategy of reward maximization, a crowd worker can get the theoretically maximum reward.

Task Allocation in Spatial Crowdsourcing: Current State and Future Directions

Spatial crowdsourcing (SC) is an emerging paradigm of crowdsourcing, which commits workers to move to some particular locations to perform spatio-temporal-relevant tasks (e.g., sensing and activity organization). Task allocation or worker selection is a significant problem that may impact the quality of completion of SC tasks. Based on a conceptual model and generic framework of SC task allocation, this paper first gives a review of the current state of research in this field, including single task allocation, multiple task allocation, low-cost task allocation, and quality-enhanced task allocation. We further investigate the future trends and open issues of SC task allocation, including skill-based task allocation, group recommendation and collaboration, task composition and decomposition, and privacy-preserving task allocation. Finally, we discuss the practical issues on real-world deployment as well as the challenges for large-scale user study in SC task allocation.

Analyzing crowdsourcing to teach mobile crowdsensing a few lessons

In recent years, mobile computing has shown so much potential that one can see a boundary-blurring expansion between the physical and the digital world. In this context, one of the most sought after research areas is mobile crowdsensing. To realize the vision of crowdsensing, there is a plethora of work in the literature that focuses on the technical capabilities of a mobile device. However, an important and a critical problem that eludes literature is the issue of human participation. We base this argument on a very simple, yet powerful fact that a mobile device is still a person’s private property. Therefore, considering human mentality, can we expect a person to contribute all the time? In this respect, it is not feasible for a human dependent computational system to ignore the inevitable human factor and focus only on the mechanical properties. In this paper, we look into the often ignored human aspects and will study the problem from a psychological perspective. We take inspiration from the mature paradigm of crowdsourcing and discuss the importance of a few human factors that could teach us how to encourage user participation in mobile crowdsensing. Further, we also explore a person’s habitual characteristics that could help answer the decade’s old question: How to get quality responses from the crowd? We use a psycho-technological approach to observe, understand, and find a few details regarding human behavior in online systems. Lastly, we take inspiration from the analysis to present a roadmap that aids in engineering a better and effective crowdsensing platform.

Crowdsourcing, Mixed Elastic Systems and Human-Enhanced Computing–A Survey

State-of-the-art practices have recognized the utility of leveraging human intervention as a crucial aspect of modern computing systems. The emerging crowdsourcing paradigm is based on harnessing human intelligence, effort and rational behaviors to augment computation and analysis. In addition to the crowdsourcing paradigm, new techniques have emerged that incorporate machine and human computational resources together forming a hybrid intelligence when addressing complex problems and tasks. This combined technique is particularly impactful if human and machine contributions can scale automatically in response to their respective efficiency and effectiveness when addressing subsets of a bigger problem – an approach that we have named mixed elastic systems . In this survey, we highlight state-of-the-art projects that investigate crowdsourcing, hybrid intelligence systems and mixed elastic systems. We also present a taxonomy and classification of the broader domain of human-enhanced computing systems as it assimilates crowdsourcing, hybrid intelligence, and mixed elastic systems.

Talker-specificity and adaptation in quantifier interpretation.

Linguistic meaning has long been recognized to be highly context-dependent. Quantifiers like many and some provide a particularly clear example of context-dependence. For example, the interpretation of quantifiers requires listeners to determine the relevant domain and scale. We focus on another type of context-dependence that quantifiers share with other lexical items: talker variability. Different talkers might use quantifiers with different interpretations in mind. We used a web-based crowdsourcing paradigm to study participants' expectations about the use of many and some based on recent exposure. We first established that the mapping of some and many onto quantities (candies in a bowl) is variable both within and between participants. We then examined whether and how listeners' expectations about quantifier use adapts with exposure to talkers who use quantifiers in different ways. The results demonstrate that listeners can adapt to talker-specific biases in both how often and with what intended meaning many and some are used.

Quantitative evaluation of volunteered geographic information paradigms: social location-based services case study

As of 2010, 90% of the data that existed in the world were created within the previous 2 years, while personal location data have been singled out as one of the five primary ‘big data’ streams in the 2011 McKinsey report. By 2020, the volume of existing data will increase by 50-fold, where a large percentage of this volume will be associated with geospatial data. One of the reasons for this is the existence of the volunteered geographic information (VGI) paradigm, which encapsulates the idea of using the Internet (Web 2.0) to create, share, visualise, and analyse geographic information and knowledge. This neogeography revolution has started to fundamentally transform how geographic data are acquired, maintained, analysed, visualised, and consequently used. Thus, it has the potential to influence common practices, since it captures a broad knowledge of the environment we live in, in all aspects of life, encompassing new services to take place, applications and processes to be developed – all of which are location based. The diversity of applications and services that explore the potential of VGI argues for its current usability relevance: ranging from transportation network analysis, to air pollution and air quality, to natural disaster decision-making systems. This revolution has contributed to the development of two important working and knowledge paradigms: Crowdsourcing and Wisdom of the Crowd, widely used today within the mapping and geo-information discipline. Still, both terms are commonly misused and replaced. This paper aims at distinguishing between the terms via the quantitative and theoretical examination of four widely used social location-based services: OpenStreetMap (OSM), Moovit, Waze and Ushahidi. Eight primary characteristics that influence the paradigm of both Crowdsourcing and Wisdom of the Crowd are defined and examined, aiming to investigate and emphasise the differences between the four, namely: diversity, decentralisation, independency, aggregation, knowledge, activity, privacy and exploitation. It was found that OSM is an excellent example of a Crowdsourcing service, while though Ushahidi is considered as a Crowdsourcing service, its characteristics are coupled better with those of Wisdom of the Crowd. Moovit and Waze do not correspond to the Crowdsourcing paradigm, and thus are categorised as Wisdom of the Crowd services.

Approaches of Humanitarian Crisis Management - Associated Risks with the ICT-based Crowdsourcing Paradigm

Since the early fifties of Twentieth Century, numbers of guidelines, frameworks and methodologies have been developed by countries, UN and other agencies to provide humanitarian assistance and manage humanitarian crisis properly. There are numbers of national policies and plans on disaster risk reduction and national plans for disaster management have also been established. However, in this Age of ICTs, anyone engaged in humanitarian crisis management activities understands that effective crisis management is now more demanding than ever before. Thus, crisis coordinators and crisis management researchers have started developing and using new ICT tools and technologies to deal crisis efficiently and effectively. However, all these tools and techniques create some critical challenges in data protection, privacy and security of users and some risks related to Malware, fear and confusion, government surveillance and so on. On this background, this research paper aims to provide a brief analysis on evolution of humanitarian crisis management approach and several risks related to Crowdsourcing for Humanitarian Crisis Management.

World Stage: Crowdsourcing Paradigm for Expressive Social Mobile Music

The combination of powerful mobile devices and the connective potential of cloud-based computing are changing how, where, and when people use computers. No longer physically tethered, computers embedded in mobile phones and tablets freely roam in daily life alongside their human users while persistently connected to the network. At the same time, the pervasiveness of computing enables us to engage people to solve large-scale problems by leveraging human intelligence and the effect of large crowds. In this paper, we discuss a new model for crowdsourced musical interactions based on mobile devices. This model, which we call World Stage, is a pseudo-anonymous, location-aware ecosystem designed to connect hundreds of thousands of users in a social-musical game involving expressive musical performance and collaborative musical feedback. We describe the motivation and mechanics of the World Stage, and present a full implementation and case study around a commercial iPhone application: Smule’s Leaf Trombone: World Stage. We also present the experiential design and the technological infrastructure around the World Stage and discuss the unique social/musical possibilities it affords. This work is about exploring, perhaps for the very first time, a crowdsourcing eco-system that incorporates expressive music-making and with game-like elements, aimed at inciting a mass audience.

TASI will double the capacity of submarine telephone cables

Linguistic meaning has long been recognized to be highly context-dependent. Quantifiers like many and some provide a particularly clear example of context-dependence. For example, the interpretation of quantifiers requires listeners to determine the relevant domain and scale. We focus on another type of context-dependence that quantifiers share with other lexical items: talker variability. Different talkers might use quantifiers with different interpretations in mind. We used a web-based crowdsourcing paradigm to study participants’ expectations about the use of many and some based on recent exposure. We first established that the mapping of some and many onto quantities (candies in a bowl) is variable both within and between participants. We then examined whether and how listeners’ expectations about quantifier use adapts with exposure to talkers who use quantifiers in different ways. The results demonstrate that listeners can adapt to talker-specific biases in both how often and with what intended meaning many and some are used.