Topic-aware Incentive Mechanism for Task Diffusion in Mobile Crowdsourcing through Social Network

Crowdsourcing has become an efficient paradigm to utilize human intelligence to perform tasks that are challenging for machines. Many incentive mechanisms for crowdsourcing systems have been proposed. However, most of existing incentive mechanisms assume that there are sufficient participants to perform crowdsourcing tasks. In large-scale crowdsourcing scenarios, this assumption may be not applicable. To address this issue, we diffuse the crowdsourcing tasks in social network to increase the number of participants. To make the task diffusion more applicable to crowdsourcing system, we enhance the classic Independent Cascade model so the influence is strongly connected with both the types and topics of tasks. Based on the tailored task diffusion model, we formulate the Budget Feasible Task Diffusion ( BFTD ) problem for maximizing the value function of platform with constrained budget. We design a parameter estimation algorithm based on Expectation Maximization algorithm to estimate the parameters in proposed task diffusion model. Benefitting from the submodular property of the objective function, we apply the budget-feasible incentive mechanism, which satisfies desirable properties of computational efficiency, individual rationality, budget-feasible, truthfulness, and guaranteed approximation, to stimulate the task diffusers. The simulation results based on two real-world datasets show that our incentive mechanism can improve the number of active users and the task completion rate by 9.8% and 11%, on average.

Database management system for mobile crowdsourcing applications

The evaluation of mobile crowdsourcing activities and reports require a viable and large volume of data. These data are gathered in real-time and from a large number of paid or unpaid volunteers over a period. A high volume of quality data from smartphones or mobile devices is pivotal to the accuracy and validity of the results. Therefore, there is a need for a robust and scalable database structure that can effectively manage and store the large volumes of data collected from various volunteers without compromising the integrity of the data. An in-depth review of various database designs to select the most suitable that will meet the needs of a real-time, robust and large volunteer data handling system is presented. A non-relational database was proposed for the mobile- end database: Google Cloud Firestore specifically due to its support for mobile client implementation, this choice also makes the integration of data from the mobile end-users to the cloud-hosted database relatively easier with all proposed services being part of the Google Cloud Platform; although it is not as popular as some other database services. Separate comparative reviews of the Database Management System (DBMS) performance demonstrated that MongoDB (a non-relational database) performed better when reading large datasets and performing full-text queries, while MySQL (relational) and Cassandra (non-relational) performed much better for data insertion. Google BigQuery was proposed as an appropriate data warehouse solution. It will provide continuity and direct integration with Cloud Firestore and its Application Programming Interface (API) for data migration from Cloud Firestore to BigQuery, and the local server. Also Google BigQuery provides machine learning support for data analytics.

Role of Internet of Things (IoT) and Crowdsourcing in Smart City Projects

This paper presents and discusses the role of the Internet of Things (IoT) and crowdsourcing in constructing smart cities. The literature review shows an important and increasing concern of the scientific community for these three issues and their association as support for urban development. Based on an extensive literature review, the paper first presents the smart city concept, emphasizing smart city architecture and the role of data in smart city solutions. The second part presents the Internet of Things, focusing on IoT technology, the use of IoT in smart city applications, and security. Finally, the paper presents crowdsourcing with particular attention to mobile crowdsourcing and its role in smart cities. The paper shows that IoT and crowdsourcing have a crucial role in two fundamental layers of smart city applications, namely, the data collection and services layers. Since these two layers ensure the connection between the physical and digital worlds, they constitute the central pillars of smart city projects. The literature review also shows that the smart city development still requires stronger cooperation between the smart city technology-centered research, mainly based on the IoT, and the smart city citizens-centered research, mainly based on crowdsourcing. This cooperation could beneficiate in recent developments in the field of crowdsensing that combines IoT and crowdsourcing.