SACRM: Social Aware Crowdsourcing with Reputation Management in Mobile Sensing

TL;DR

This paper introduces SACRM, a scheme for mobile sensing crowdsourcing that considers social attributes and reputation to select participants, assess report quality, and allocate rewards, thereby improving utility and report quality.

Contribution

The paper proposes a novel social-aware participant selection and reputation management scheme for mobile sensing crowdsourcing, enhancing report quality and trustworthiness.

Findings

Improves crowdsourcing utility through social attribute consideration.

Effectively stimulates participants to enhance report quality.

Demonstrates efficiency via theoretical analysis and simulations.

Abstract

Mobile sensing has become a promising paradigm for mobile users to obtain information by task crowdsourcing. However, due to the social preferences of mobile users, the quality of sensing reports may be impacted by the underlying social attributes and selfishness of individuals. Therefore, it is crucial to consider the social impacts and trustworthiness of mobile users when selecting task participants in mobile sensing. In this paper, we propose a Social Aware Crowdsourcing with Reputation Management (SACRM) scheme to select the well-suited participants and allocate the task rewards in mobile sensing. Specifically, we consider the social attributes, task delay and reputation in crowdsourcing and propose a participant selection scheme to choose the well-suited participants for the sensing task under a fixed task budget. A report assessment and rewarding scheme is also introduced to measure the quality of the sensing reports and allocate the task rewards based the assessed report quality. In addition, we develop a reputation management scheme to evaluate the trustworthiness and cost performance ratio of mobile users for participant selection. Theoretical analysis and extensive simulations demonstrate that SACRM can efficiently improve the crowdsourcing utility and effectively stimulate the participants to improve the quality of their sensing reports.