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Integrity of Data in a Mobile Crowdsensing Campaign: A Case Study

Authors:

Saurabh Bagchi,

He WangAuthors Info & Claims

CrowdSenSys '17: Proceedings of the First ACM Workshop on Mobile Crowdsensing Systems and Applications

Pages 50 - 55

https://doi.org/10.1145/3139243.3139255

Published: 06 November 2017 Publication History

Abstract

Mobile crowdsensing (MCS) has a huge potential to provide societal benefits by effectively utilizing the sensing, computing, and networking capability of mobile devices, which have become ubiquitous especially in the developed world. However, many challenges come along with MCS such as energy cost, privacy issues, and the data integrity of crowdsensed data. In this paper, we focus on analyzing the data integrity of mobile crowdsensed data in a user study of 60 people who participate in a crowdsensing campaign that collects barometric pressure data from various locations of our campus. Each set of 20 users runs one of the three different crowdsensing frameworks, one of which, called SENSE-AID, is an energy-efficient framework designed by us. We analyze the characteristics of the data with respect to their integrity. From our analysis, we find that for 90 MCS tasks there is a surprisingly high number of outlier percentage (close to 20%), only about 10% of the participants report data for the entire duration of 7 days of the MCS campaign, and using a reputation system to filter out spurious data values helps in getting within 0.8% of the ground truth barometric pressure, whereas not using the reputation system leads to a discrepancy of about 20%. We hope our analysis will provide a useful reference to MCS researchers and developers in their future work running MCS campaigns.

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Cited By

Perez AZeadally S(2022)Secure and privacy-preserving crowdsensing using smart contractsComputer Science Review10.1016/j.cosrev.2021.10045043:COnline publication date: 9-May-2022
https://dl.acm.org/doi/10.1016/j.cosrev.2021.100450
Zhang ZYum DShin M(2021)PARS: Privacy-Aware Reward System for Mobile Crowdsensing SystemsSensors10.3390/s2121704521:21(7045)Online publication date: 24-Oct-2021
https://doi.org/10.3390/s21217045
Zhang HRoth MPanta RWang HBagchi SValois FJulien CMurphy AGnawali O(2020)CrowdBind: Fairness Enhanced Late Binding Task Scheduling in Mobile CrowdsensingProceedings of the 2020 International Conference on Embedded Wireless Systems and Networks10.5555/3400306.3400314(61-72)Online publication date: 17-Feb-2020
https://dl.acm.org/doi/10.5555/3400306.3400314
Show More Cited By

Index Terms

Integrity of Data in a Mobile Crowdsensing Campaign: A Case Study
1. Networks
  1. Network architectures
  2. Network performance evaluation

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cover image ACM Conferences

CrowdSenSys '17: Proceedings of the First ACM Workshop on Mobile Crowdsensing Systems and Applications

November 2017

81 pages

ISBN:9781450355551

DOI:10.1145/3139243

Editor:
Rasit Eskicioglu
University of Manitoba, Canada

Copyright © 2017 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 06 November 2017

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Division of Computer and Network Systems

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SenSys '17

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SenSys '17: The 15th ACM Conference on Embedded Network Sensor Systems

November 6 - 8, 2017

Delft, Netherlands

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Cited By

Perez AZeadally S(2022)Secure and privacy-preserving crowdsensing using smart contractsComputer Science Review10.1016/j.cosrev.2021.10045043:COnline publication date: 9-May-2022
https://dl.acm.org/doi/10.1016/j.cosrev.2021.100450
Zhang ZYum DShin M(2021)PARS: Privacy-Aware Reward System for Mobile Crowdsensing SystemsSensors10.3390/s2121704521:21(7045)Online publication date: 24-Oct-2021
https://doi.org/10.3390/s21217045
Zhang HRoth MPanta RWang HBagchi SValois FJulien CMurphy AGnawali O(2020)CrowdBind: Fairness Enhanced Late Binding Task Scheduling in Mobile CrowdsensingProceedings of the 2020 International Conference on Embedded Wireless Systems and Networks10.5555/3400306.3400314(61-72)Online publication date: 17-Feb-2020
https://dl.acm.org/doi/10.5555/3400306.3400314
Xu JYang SLu WXu LYang D(2020)Incentivizing for Truth Discovery in Edge-assisted Large-scale Mobile CrowdsensingSensors10.3390/s2003080520:3(805)Online publication date: 2-Feb-2020
https://doi.org/10.3390/s20030805

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