クラウドソーシングによる屋内測位データ収集のインセンティブメカニズムの研究

早大学位記番号:新8551早稲田大

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

TIMCC: On Data Freshness in Privacy-Preserving Incentive Mechanism Design for Continuous Crowdsensing Using Reverse Auction

© 2013 IEEE. As an emerging paradigm that leverages the wisdom and efforts of the crowd, mobile crowdsensing has shown its great potential to collect distributed data. The crowd may incur such costs and risks as energy consumption, memory consumption, and privacy leakage when performing various tasks, so they may not be willing to participate in crowdsensing tasks unless they are well-paid. Hence, a proper privacy-preserving incentive mechanism is of great significance to motivate users to join, which has attracted a lot of research efforts. Most of the existing works regard tasks as one-shot tasks, which may not work very well for the type of tasks that requires continuous monitoring, e.g., WIFI signal sensing, where the WiFi signal may vary over time, and users are required to contribute continuous efforts. The incentive mechanism for continuous crowdsensing has yet to be investigated, where the corresponding tasks need continuous efforts of users, and the freshness of the sensed data is very important. In this paper, we design TIMCC, a privacy-preserving incentive mechanism for continuous crowdsensing. In contrast to most existing studies that treat tasks as one-shot tasks, we consider the tasks that require users to contribute continuous efforts, where the freshness of data is a key factor impacting the value of data, which further determines the rewards. We introduce a metric named age of data that is defined as the amount of time elapsed since the generation of the data to capture the freshness of data. We adopt the reverse auction framework to model the connection between the platform and the users. We prove that the proposed mechanism satisfies individual rationality, computational efficiency, and truthfulness. Simulation results further validate our theoretical analysis and the effectiveness of the proposed mechanism

beacon based context aware architecture for crowd sensing public transportation scheduling and user habits

Abstract: Crowd sourcing and sensing are relatively recent paradigms that, enabled by the pervasiveness of mobile devices, allow users to transparently contribute in complex problem solving. Their effectiveness depends on people voluntarism, and this could limit their adoption. Recent technologies for automating context-awareness could give a significant impulse to spread crowdsourcing paradigms. In this paper, we propose a distributed software system that exploits mobile devices to improve public transportation efficiency. It takes advantage of the large number of deployed personal mobile devices and uses them as both mule sensors, in cooperation with beacon technology for geofecing, and clients for getting information about bus positions and estimated arrival times. The paper discusses the prototype architecture, its basic application for getting dynamic bus information, and the long-term scope in supporting transportation companies and municipalities, reducing costs, improving bus lines, urban mobility and planning