Trust Evaluation Mechanism for User Recruitment in Mobile Crowd-Sensing in the Internet of Things
Abstract : Mobile crowd-sensing (MCS) has appeared as a prospective solution for large-scale data collection, leveraging built-in sensors and social applications in mobile devices that enables a variety of Internet of Things (IoT) services. However, the human involvement in MCS results in a high possibility for unintentionally contributing corrupted and falsified data or intentionally spreading disinformation for malevolent purposes, consequently undermining IoT services. Therefore, recruiting trustworthy contributors plays a crucial role in collecting high-quality data and providing a better quality of services while minimizing the vulnerabilities and risks to MCS systems. In this paper, a novel trust model called experience-reputation (E-R) is proposed for evaluating trust relationships between any two mobile device users in an MCS platform. To enable the E-R model, virtual interactions among the users are manipulated by considering an assessment of the quality of contributed data from such users. Based on these interactions, two indicators of trust called experience and reputation are calculated accordingly. By incorporating the experience and reputation trust indicators (TIs), trust relationships between the users are established, evaluated, and maintained. Based on these trust relationships, a novel trust-based recruitment scheme is carried out for selecting the most trustworthy MCS users to contribute to data sensing tasks. In order to evaluate the performance and effectiveness of the proposed trust-based mechanism as well as the E-R trust model, we deploy several recruitment schemes in an MCS testbed, which consists of both normal and malicious users. The results highlight the strength of the trust-based scheme as it delivers a better quality for MCS services while being able to detect malicious users. We believe that the trust-based user recruitment offers an effective capability for selecting trustworthy users for various MCS systems and, importantly, the proposed mechanism is practical to deploy in the real world.
? Internet of Things (IoT) applications and services depend heavily on data collected from sensing campaigns such as sensor networks and crowd sourcing.
? A novel trust evaluation mechanism called Experience-Reputation (E-R) is proposed for evaluating trust relationships between any two mobile device users in an MCS platform.
? Low quality data might cause numerous problems such as deception in decision making, consumer dissatisfaction and distrusting the system.
? Less Accuracy Data .
? Duplicate Recruitment data .
• Now-a-days managing data manually is comparatively difficult than managing it with the help of technology.
• Technology refers to the use of knowledge practically so as to ease one’s work hence we emphasize on making an application that can be used for the recruitment process by Training and Placement Officer and the students.
• For this, the students need to register themselves on the portal, filling basic personal information and academic marks. On the other hand, Training and placement officer uploads drive details and inform principal about the same.
• The notification about the drive will be sent to the particular students who are only eligible for the drive through Mobile App. Monthly timetable for the drive will be present on the home page for all the students to view. Also the selected student’s data will be sent to the principle.
? In the system, fire base data base are used as backend and android studio are used as front end of the Application.
? The application is based on GPS location provider embedded on your Android Smartphone.
? The application was developed to serve mass population. That is why the user interface of the application is kept simple.
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