When Crowdsourcing Meets Mobile Sensing: A Social Network Perspective

TL;DR

This paper explores how social network principles can enhance mobile sensing by leveraging crowdsourcing, demonstrating improved performance through real-world experiments and discussing associated challenges.

Contribution

It introduces a novel social network-inspired framework for mobile sensing that utilizes crowdsourcing to improve data accuracy and system robustness.

Findings

Crowdsourcing improves mobile sensing performance.

Social trust mechanisms enhance data reliability.

Real-world datasets validate the proposed approach.

Abstract

Mobile sensing is an emerging technology that utilizes agent-participatory data for decision making or state estimation, including multimedia applications. This article investigates the structure of mobile sensing schemes and introduces crowdsourcing methods for mobile sensing. Inspired by social network, one can establish trust among participatory agents to leverage the wisdom of crowds for mobile sensing. A prototype of social network inspired mobile multimedia and sensing application is presented for illustrative purpose. Numerical experiments on real-world datasets show improved performance of mobile sensing via crowdsourcing. Challenges for mobile sensing with respect to Internet layers are discussed.