Mechanism design for spatio-temporal request satisfaction in mobile networks

Mobile agents participating in geo-presence-capable crowdsourcing applications should be presumed rational, competitive, and willing to deviate from their routes if given the right incentive. In this paper, we design a mechanism that takes into consideration this rationality for request satisfaction in such applications. We propose the Geo-temporal Request Satisfaction (GRS) problem to be that of finding the optimal assignment of requests with specific spatio-temporal characteristics to competitive mobile agents subject to spatio-temporal constraints. The objective of the GRS problem is to maximize the total profit of the system subject to our rationality assumptions. We define the problem formally, prove that it is NP-Complete, and present a practical solution mechanism, which we prove to be convergent, and which we evaluate experimentally.National Science Foundation (1012798, 0952145, 0820138, 0720604, 0735974

CrowdSC: Building Smart Cities with Large-Scale Citizen Participation

International audienceAn elegant way to make cities smarter would be to design a platform where citizens are given an opportunity to be effectively connected to the governing bodies in their location and to contribute to the general well being. In this paper, we present CrowdSC, a crowdsourcing framework designed for smarter cities. We show that it is possible to combine data collection, data selection and data assessment crowdsourcing activities in a crowdsourcing process to achieve sophisticated goals in a predefined context. We show that depending on the executing strategy of this process, different kind of outcomes can be produced. We present an experimental study that evaluate these process outcomes depending on different execution strategie

CrowdSC: Building Smart Cities with Large Scale Citizen Participation

An elegant way to make cities smarter would be to design a platform where every citizen is given an opportunity to be effectively connected to the governing bodies in their location and to contribute to the general well being. In this paper, we present CrowdSC, an effective crowdsourcing framework designed for smarter cities. We show that it is possible to combine data collection, data selection and data assessment crowdsourcing activities in a crowdsourcing process to achieve sophisticated goals in a predefined context. We propose different strategies for managing this process. We also present an experimental study that evaluate outcomes of the process depending on these execution strategies

Answering Complex Location-Based Queries with Crowdsourcing

International audienceCrowdsourcing platforms provide powerful means to execute queries that require some human knowledge, intelligence and experience instead of just automated machine computation, such as image recognition, data filtering and labeling. With the development of mobile devices and the rapid prevalence of smartphones that boosted mobile Internet access, location-based crowdsourcing is quickly becoming ubiquitous, enabling location-based queries assigned to and performed by humans. In sharp contrast of existing location-based crowdsourcing approaches that focus on simple queries, in this paper, we describe a crowdsourcing process model that supports queries including several crowd activities, and can be applied in a variety of location-based crowdsourcing scenarios. We also propose different strategies for managing this crowdsourcing process. Finally, we describe the architecture of our system, and present an experimental study conducted on pseudo-real dataset that evaluates the process outcomes depending on these execution strategies

CrowdPickUp:task pick-up in the wild

Abstract. This thesis investigates the feasibility and performance of different types of crowdsourcing tasks picked-up in the wild i.e., situated, location-based and general through the implementation and evaluation of the CrowdPickUp crowdsourcing platform. We describe in detail the implementation process of CrowdPickUp, which we then used in a study where workers could earn coins on the basis of task completion and use their earned coins to buy different available items of their own choice using CrowdPickUp’s web shop integrated within our system. During the study, we recorded the average completion time and accuracy of different crowdsourcing tasks. The key findings show that our platform was able to generate high quality contributions in a composite environment. Finally, we conclude the thesis by discussing the importance and usefulness of different crowdsourcing tasks designed for our crowdsourcing system and our possible future work within the area of crowdsourcing task-pickup system.Tiivistelmä. Tämä diplomityö tutkii joukkouttamisen suorituskykyä ja mahdollisuuksia erityyppisten tehtävien avulla. Tehtävät jaetaan työntekijöille luonnollisissa olosuhteissa paikkasidonnaisesti työssä kehitetyn CrowdPickUp-alustan avulla. Työ kuvailee yksityiskohtaisesti kehitetyn alustan sovelluskehitysprosessin. Tämän jälkeen valmista alustaa käytettiin käyttäjäkokeissa, joissa työntekijät pystyivät ansaitsemaan virtuaalivaluuttaa, jolla pystyi ostamaan erilaisia palkintoja. Kokeen aikana tutkimme ja tallensimme monenlaista tietoa, kuten esimerkiksi suoritetun työn tarkkuutta ja keskimääräistä tehokkuutta. Työn päälöydökset osoittavat, että alustamme kykeni tuottamaan korkealaatuista työtä luonnollisissa olosuhteissa ja ilman tutkijoiden jatkuvaa läsnäoloa. Lopuksi diplomityö keskustelee löydösten ja kehitystyön tärkeyttä sekä soveltuvuutta erilaisten tehtävien suorittamisalustaksi. Lisäksi esittelemme ideoita, joilla työtä voi kehittää eteenpäin entistä hyödyllisemmäksi tutkimusinstrumentiksi

Maximizing the number of worker’s self-selected tasks in spatial crowdsourcing

ABSTRACT With the progress of mobile devices and wireless broadband, a new eMarket platform, termed spatial crowdsourcing is emerging, which enables workers (aka crowd) to perform a set of spatial tasks (i.e., tasks related to a geographical location and time) posted by a requester. In this paper, we study a version of the spatial crowdsourcing problem in which the workers autonomously select their tasks, called the worker selected tasks (WST) mode. Towards this end, given a worker, and a set of tasks each of which is associated with a location and an expiration time, we aim to find a schedule for the worker that maximizes the number of performed tasks. We first prove that this problem is NP-hard. Subsequently, for small number of tasks, we propose two exact algorithms based on dynamic programming and branch-and-bound strategies. Since the exact algorithms cannot scale for large number of tasks and/or limited amount of resources on mobile platforms, we also propose approximation and progressive algorithms. We conducted a thorough experimental evaluation on both real-world and synthetic data to compare the performance and accuracy of our proposed approaches