Mobile Crowdsourcing Platform Research Articles

Task Execution Quality Maximization for Mobile Crowdsourcing in Geo-Social Networks

With the rapid development of smart devices and high-quality wireless technologies, mobile crowdsourcing (MCS) has been drawing increasing attention with its great potential in collaboratively completing complicated tasks on a large scale. A key issue toward successful MCS is participant recruitment, where a MCS platform directly recruits suitable crowd participants to execute outsourced tasks by physically traveling to specified locations. Recently, a novel recruitment strategy, namely Word-of-Mouth(WoM)-based MCS, has emerged to effectively improve recruitment effectiveness, by fully exploring users' mobility traces and social relationships on geo-social networks. Against this background, we study in this paper a novel problem, namely Expected Task Execution Quality Maximization (ETEQM) for MCS in geo-social networks, which strives to search a subset of seed users to maximize the expected task execution quality of all recruited participants, under a given incentive budget. To characterize the MCS task propagation process over geo-social networks, we first adopt a propagation tree structure to model the autonomous recruitment between the referrers and the referrals. Based on the model, we then formalize the task execution quality and devise a novel incentive mechanism by harnessing the business strategy of multi-level marketing. We formulate our ETEQM problem as a combinatorial optimization problem, and analyze its NP hardness and high-dimensional characteristics. Based on a cooperative co-evolution framework, we proposed a divide-and-conquer problem-solving approach named ETEQM-CC. We conduct extensive simulation experiments and a case study, verifying the effectiveness of our proposed approach.

Health Sentinel: A mobile crowdsourcing platform for self-reported surveys provides early detection of COVID-19 clusters in San Luis Potosí, Mexico

BackgroundThe Health Sentinel (Centinela de la Salud, CDS), a mobile crowdsourcing platform that includes the CDS app, was deployed to assess its utility as a tool for COVID-19 surveillance in San Luis Potosí, Mexico. MethodsThe CDS app allowed anonymized individual surveys of demographic features and COVID-19 risk of transmission and exacerbation factors from users of the San Luis Potosí Metropolitan Area (SLPMA). The platform’s data processing pipeline computed and geolocalized the risk index of each user and enabled the analysis of the variables and their association. Point process analysis identified geographic clustering patterns of users at risk and these were compared with the patterns of COVID-19 cases confirmed by the State Health Services. ResultsA total of 1554 COVID-19 surveys were administered through the CDS app. Among the respondents, 50.4 % were men and 49.6 % women, with an average age of 33.5 years. Overall risk index frequencies were, in descending order: no-risk 77.8 %, low risk 10.6 %, respiratory symptoms 6.7 %, medium risk 1.4 %, high risk 2.0 %, very high risk 1.5 %. Comorbidity was the most frequent vulnerability category (32.4 %), followed by the inability to keep home lockdown (19.2 %). Statistically significant risk clusters identified at a spatial scale between 5 and 730 m coincided with those in neighborhoods containing substantial numbers of confirmed COVID-19 cases. ConclusionsThe CDS platform enables the analysis of the sociodemographic features and spatial distribution of individual risk indexes of COVID-19 transmission and exacerbation. It is a useful epidemiological surveillance and early detection tool because it identifies statistically significant and consistent risk clusters in neighborhoods with a substantial number of confirmed COVID-19 cases.

SocialRecruiter: Dynamic Incentive Mechanism for Mobile Crowdsourcing Worker Recruitment With Social Networks

Worker recruitment is an important problem in mobile crowdsourcing (MCS), which aims to find sufficient and suitable participants to perform tasks. However, existing worker recruitment approaches mainly focus on how to select the most suitable workers for tasks from a large worker pool, while the recruitment problem under insufficient workers (e.g., a new MCS system) has not been well addressed. In this paper, we focus on the insufficient participation problem of MCS systems with limited number of workers, and propose to leverage social network to recruit workers for task completion as well as expanding the worker pool. To this end, we propose a dynamic incentive mechanism, called SocialRecruiter, to encourage workers on the MCS platform to propagate tasks through social networks, so that inviting friends to join in the MCS platform to further propagate and complete tasks. Motivated by the SIR epidemic model, we propose a novel task-specific epidemic model to characterize the status change of users for task propagation and completion through social networks. In order to encourage task completion and propagation, the propagating reward and completing reward are provided according to workers’ actions. In particular, in order to maximize the task completion within the financial budget, the propagating and completing rewards are dynamically updated at each cycle according to real-time worker recruitment progress. The extensive experimental results on two real-world datasets demonstrate that SocialRecruiter outperforms the state-of-the-art approaches in terms of worker recruitment and task completion.

Eliciting Information From Heterogeneous Mobile Crowdsourced Workers Without Verification

In mobile crowdsourcing, platforms seek to incentivize heterogeneous workers to complete tasks (e.g., road traffic sensing) and truthfully report their solutions. When platforms cannot verify the quality of the workers’ solutions, the crowdsourcing problem is known as <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">information elicitation without verification</i> (IEWV). In an IEWV problem, a platform needs to provide incentives to motivate high-quality solutions and truthful reporting of the solutions from the workers. A common approach to solve the IEWV problem is majority voting, where each worker is rewarded according to whether his solution matches the majority’s solution. However, previous work has not considered workers with heterogeneous solution accuracy. This is unrealistic in many domains, where one would expect workers to differ in judgment, expertise, and reliability. Moreover, prior work has not considered how this heterogeneity affects a platform’s tradeoff between the quality of the workers’ solutions and the platform’s cost of achieving this. We address these gaps by studying the interactions between the mobile crowdsourcing platform and workers as a two-stage Stackelberg game. In Stage I, the platform chooses the reward level for majority voting. In Stage II, the workers decide their effort levels and reporting strategies. We show that as a worker’s solution accuracy increases, he is more likely, in equilibrium, to exert effort and truthfully report his solution. However, given a fixed total worker population, surprisingly, the platform’s payoff may decrease in the number of high-accuracy workers. We further characterize the value of knowing the workers’ solution accuracy in terms of improving the platform’s optimal reward design and maximizing its payoff. Knowing such information enables a more effective aggregation of the workers’ solutions. We further design a discriminatory reward policy to incentivize heterogeneous workers. Surprisingly, such a discriminatory policy can improve both the platform’s and the workers’ payoffs, and hence improve the social welfare.

Differentially Private Mechanisms for Budget Limited Mobile Crowdsourcing

Recently, Mobile Crowdsourcing (MC) has aroused great interest on the part of both academic and industrial circles. One of the key problems in MC is designing the proper mechanisms to incentivize user participation, as users are typically self-interested and must consume a substantial amount of MC resources/costs. Although considerable research has been devoted to this problem, the majority of studies have neglected the privacy issue in mechanism design. In this study, we consider the scenario where a mobile crowdsourcing platform aims to maximize the crowdsourcing revenue under a budget constraint, and users are interested in maximizing their utility while keeping their cost private. We design differentially-private mechanisms for such a scenario under an offline setting where users bid their costs simultaneously and under an online setting where user bids are revealed one by one. We show that our mechanisms simultaneously achieve provable performance bounds with respect to several measures, including revenue, differential privacy, truthfulness, and individual rationality. Finally, we also conduct extensive numerical experiments to demonstrate the effectiveness of our approach.

Text Mining Customer Reviews for Aspect-Based Restaurant Rating

This study applies text mining to analyze customer reviews and automatically assign a collective restaurant star rating based on five predetermined aspects: ambiance, cost, food, hygiene, and service. The application provides a web and mobile crowd sourcing platform where users share dining experiences and get insights about the strengths and weaknesses of a restaurant through user contributed feedback. Text reviews are tokenized into sentences. Noun-adjective pairs are extracted from each sentence using Stanford Core NLP library and are associated to aspects based on the bag of associated words fed into the system. The sentiment weight of the adjectives is determined through AFINN library. An overall restaurant star rating is computed based on the individual aspect rating. Further, a word cloud is generated to provide visual display of the most frequently occurring terms in the reviews. The more feedbacks are added the more reflective the sentiment score to the restaurants’ performance.

TEXT MINING CUSTOMER REVIEWS FOR ASPECTBASED RESTAURANT RATING

This study applies text mining to analyze customer reviews and automatically assign a collective restaurant star rating based on five predetermined aspects: ambiance, cost, food, hygiene, and service. The application provides a web and mobile crowd sourcing platform where users share dining experiences and get insights about the strengths and weaknesses of a restaurant through user contributed feedback. Text reviews are tokenized into sentences. Noun-adjective pairs are extracted from each sentence using Stanford Core NLP library and are associated to aspects based on the bag of associated words fed into the system. The sentiment weight of the adjectives is determined through AFINN library. An overall restaurant star rating is computed based on the individual aspect rating. Further, a word cloud is generated to provide visual display of the most frequently occurring terms in the reviews. The more feedbacks are added the more reflective the sentiment score to the restaurants’ performance.

Context-Aware Hierarchical Online Learning for Performance Maximization in Mobile Crowdsourcing

In mobile crowdsourcing (MCS), mobile users accomplish outsourced human intelligence tasks. MCS requires an appropriate task assignment strategy, since different workers may have different performance in terms of acceptance rate and quality. Task assignment is challenging, since a worker's performance (i) may fluctuate, depending on both the worker's current personal context and the task context, (ii) is not known a priori, but has to be learned over time. Moreover, learning context-specific worker performance requires access to context information, which may not be available at a central entity due to communication overhead or privacy concerns. Additionally, evaluating worker performance might require costly quality assessments. In this paper, we propose a context-aware hierarchical online learning algorithm addressing the problem of performance maximization in MCS. In our algorithm, a local controller (LC) in the mobile device of a worker regularly observes the worker's context, her/his decisions to accept or decline tasks and the quality in completing tasks. Based on these observations, the LC regularly estimates the worker's context-specific performance. The mobile crowdsourcing platform (MCSP) then selects workers based on performance estimates received from the LCs. This hierarchical approach enables the LCs to learn context-specific worker performance and it enables the MCSP to select suitable workers. In addition, our algorithm preserves worker context locally, and it keeps the number of required quality assessments low. We prove that our algorithm converges to the optimal task assignment strategy. Moreover, the algorithm outperforms simpler task assignment strategies in experiments based on synthetic and real data.

Influence of environmental background noise on speech quality assessments task in crowdsourcing microtask platform

It is important to realize in which environments a speech quality evaluation test can be carried out to achieve reliable results outside the laboratory. We report on our current activity on using microphone signals for evaluating environmental conditions in crowdtesting. In order to analyze the impact of environmental noise, a two-phase experiment is conducted using a mobile crowdsourcing platform. A speech quality assessment task is used with stimuli from the SwissQual 501 database from the ITU-T Rec. P.863 competition (kindly provided by SwissQual AG). The first phase of the experiment is conducted in a laboratory, in which participants are assigned to either silent or background noise simulation (with Cafeteria and Road Noise) study groups. In each case 5 seconds of environmental sound are recorded by the participants before and after performing the task. Next, the same participants were asked to perform the same task in a real living room, cafeteria, or any other environment (and proofing it by taking photos) and again to record the environmental noise. Environments are labelled based on their influence on the result of speech quality assessments. Later, different features like loudness, noisiness, etc. will be extracted from the recorded audio files and used to predict if the environment is suitable for performing a crowdtesting speech quality assessment.

SenseCityVity: Mobile Crowdsourcing, Urban Awareness, and Collective Action in Mexico

This work describes SenseCityVity, an approach to engage and support youth in a city in Mexico as they investigate, document, and reflect upon urban problems through mobile crowdsourcing. SenseCityVity focused on the development of a mobile crowdsourcing platform; the deployment of the Urban Data Challenge, codesigned by the authors' research team and actors to collect geolocalized images, audio, and video; and the analysis, appropriation, and creative use of the collected data for community reflection and artistic creation. The approach integrates mobile technology and community practices involving a large population of young people for urban engagement. The collective action generated a new multimedia dataset that is rich in terms of content and is enabling a number of studies aimed at better understanding the urban landscape of cities in the Global South. This article is part of a special issue on smart cities.

Public services or private gains: Motives behind participation on a mobile crowdsourcing application in a smart city

ABSTRACTThe increased use of crowdsourcing to support smart city initiatives has been reported in the literature. However, a gap remains in understanding the needs motivating crowdsourcing participation in smart city projects. Integrating perspectives from self‐determination theory and public service motivation, this study investigates the motives driving the use of a mobile crowdsourcing platform prototype known as My Smart Mobile City app (i.e., MSMC) that aims to help smart cities manage public engagement with their residents. Data was collected from 100 participants. Factor analysis was conducted and results indicate that the motives driving the use of MSMC fell into three categories: affective, norm‐based and rational. Results and implications of our work are discussed.

Optimal Task Recommendation for Mobile Crowdsourcing With Privacy Control

Mobile crowdsourcing (MC) is a transformative paradigm that engages a crowd of mobile users (i.e., workers) in the act of collecting, analyzing, and disseminating information or sharing their resources. To ensure quality of service, MC platforms tend to recommend MC tasks to workers based on their context information extracted from their interactions and smartphone sensors. This raises privacy concerns hard to address due to the constrained resources on mobile devices. In this paper, we identify fundamental tradeoffs among three metrics-utility, privacy, and efficiency-in an MC system and propose a flexible optimization framework that can be adjusted to any desired tradeoff point with joint efforts of MC platform and workers. Since the underlying optimization problems are NP-hard, we present efficient approximation algorithms to solve them. Since worker statistics are needed when tuning the optimization models, we use an efficient aggregation approach to collecting worker feedbacks while providing differential privacy guarantees. Both numerical evaluations and performance analysis are conducted to demonstrate the effectiveness and efficiency of the proposed framework.

PEER

While government agencies, NGOs, and even commercial entities immediately swing into action to help out, in the case of large disasters, one of the biggest resources – citizens themselves – are underutilized. The rise of social media creates an opportunity for the citizen participation for disaster response management. By harnessing the power of citizen crowdsourcing, the government can have enhanced disaster situation awareness and utilize resources provided by citizen volunteers, resulting in more effective disaster responses. In this paper, the prototype Public Engagement in Emergency Response (PEER) framework is presented. It provides a comprehensive online and mobile crowdsourcing platform for situation reporting and resource volunteering. Events are described that transpired in the aftermath of superstorm Sandy, which demonstrate the benefits of using the PEER framework in a major disaster situation. Also described is how it can alleviate some of the issues associated with the crowdsourcing responses such as fraud.

Mobile and Ubiquitous Multimedia

Mobile and Ubiquitous Multimedia (MUM) is an annual international conference sponsored by ACM, which provides a forum for presenting the latest research results on mobile and ubiquitous multimedia. Every year, the conference brings together experts from both academia and industry over a period of 3 days in mid-winter, and boasts a rich technical program with plenty of opportunities for discussion and interaction with colleagues. MUM 2010, which took place in early December in the beautiful town of Limassol in Cyprus, was no exception. Sixty-two high-quality qualifying submissions were received from 20 countries across Europe, North America and Asia and 27 were selected for presentation at MUM. The papers presented covered areas, such as architectures, protocols and algorithms, middleware, interfaces, security, media management, social networks, user studies, field trials, and their application in areas, such as streaming, games, entertainment, transportation, location and context awareness, and intelligent and adaptive environments. This special issue comprises the extended versions of five papers originally submitted at MUM 2010 that have since successfully made it through two additional rounds of reviews. The selection process was particularly tough because a very high number of quality contributions were up for consideration for the special issue. The editor has made no effort to select papers of matching content but rather papers that are representative of the work presented at MUM 2010 and which promote understanding of the wider problems and issues which are pursued by researchers and practitioners working in the field. As a result, some papers of truly high quality had to be omitted from this special issue. Dinesh Babu Jayagopi and Daniel Gatica Perez of the Idiap Research Institute in Switzerland and Taemie Kiim and Alex Pentland of the MIT Media Lab in the USA address recognition of the different group conversational contexts during brainstorming and decision-making interaction using nonverbal behavioural cues and record their data using privacy-sensitive mobile sociometers. Their experiments serve to reveal that group communication patterns and dynamics are significantly different between the two. Andreas Locken, Niels Henze and Susanne Boll of the University of Oldenburg in Germany and Tobias Hesselmann and Martin Pielot of the Research Institute for Information Technology, also in Germany, propose a user-centred process for deriving gestures for controlling music playback. The resulting gesture set can serve as a benchmark for evaluating gesture recognition algorithms. Matthias Rehm of Aalborg University in Denmark and Karin Leichtenstern of Augsburg University in Germany present a gesture-based approach to intercultural training in non-verbal behaviour that makes use of what they call ‘‘enculturated’’ virtual agents, interactive systems that utilise cultural heuristics for interpreting and generating behaviour. A mobile system they have developed adopts the sensory capabilities of smart phones for gesture recognition during user interaction. Yefeng Liu, Todorka Alexandrova and Tatsuo Nakajima of Waseda University in Japan and Vili Lehdonvirta of the Helsinki Institute for Information Technology in Finland present a mobile crowd sourcing platform that is built on top of social media. A resulting crowd sourcing application aims to assist foreign visitors by calling on the locals M. Angelides (&) School of Engineering and Design, Brunel University, Kingston Lane, Uxbridge, Middlesex UB8 3PH, UK e-mail: marios.angelides@brunel.ac.uk

Drawing on mobile crowds via social media

Recent years have witnessed the impact of crowdsourcing model, social media, and pervasive computing. We believe that the more significant impact is latent in the convergence of these ideas on the mobile platform. In this paper, we introduce a mobile crowdsourcing platform that is built on top of social media. A mobile crowdsourcing application called UbiAsk is presented as one study case. UbiAsk is designed for assisting foreign visitors by involving the local crowd to answer their image-based questions at hand in a timely fashion. Existing social media platforms are used to rapidly allocate microtasks to a wide network of local residents. The resulting data are visualized using a mapping tool as well as augmented reality (AR) technology, result in a visual information pool for public use. We ran a controlled field experiment in Japan for 6 weeks with 55 participants. The results demonstrated a reliable performance on response speed and response quantity: half of the requests were answered within 10 min, 75% of requests were answered within 30 min, and on average every request had 4.2 answers. Especially in the afternoon, evening and night, nearly 88% requests were answered in average approximately 10 min, with more than 4 answers per request. In terms of participation motivation, we found the top active crowdworkers were more driven by intrinsic motivations rather than any of the extrinsic incentives (game-based incentives and social incentives) we designed.

MoneyBee: Towards enabling a ubiquitous, efficient, and easy-to-use mobile crowdsourcing service in the emerging market

We present a mobile crowdsourcing platform called MoneyBee that is accessible to mobile phone users in emerging markets through their mobile operators. The operator has a large subscriber base and the ability to pay task workers using prepaid airtime minutes. These advantages allow MoneyBee to reach far more people than conventional Internet crowdsourcing approaches. The mobile operator already has access to information on user demographics, location and presence, and monthly spending patterns. In addition, the MoneyBee platform gathers user information (such as preferences, skills, and reliability) from a user's interaction with the crowdsourcing service. Using this data to intelligently assign crowd-sourcing tasks to relevant workers is a challenging problem. Similarly, efficient dispatching of tasks to minimize the network load on the operator network is equally important. Further, simple interfaces (including speech) are needed to submit as well as execute crowdsourcing tasks on the mobile device. We describe initial solutions to some of these challenges in this paper.