research-article

A Mobile Crowdsensing Framework for Integrating Smartphone and IoT Devices to Cloud Computing Services

Authors:

Waskitho Wibisono,

M. Divi Jaya Nuryanto,

Royyana M. Ijtihadie,

Tohari Ahmad,

Radityo AnggoroAuthors Info & Claims

MobiQuitous 2017: Proceedings of the 14th EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services

Pages 482 - 487

https://doi.org/10.1145/3144457.3145506

Published: 07 November 2017 Publication History

Get Access

Abstract

The proliferations of mobile crowdsensing (MCS) services using smartphones with various embedded sensors have enabled people to involve in public sensing activities for various applications. Nevertheless for more complex applications such as smart cities, traffic monitoring, disaster prevention more variety of sensors are required. Hence combination of smartphone and Internet of Things (IoT) devices to form crowdsensing cluster will give great advantages. This paper proposes design and implementation of mobile crowdsensing framework to support integration of smartphones and IoT devices as a mobile crowdsensing cluster. The prototype of the proposed framework has been successfully implemented and tested using real devices and infrastructure with promising results.

References

[1]

J. An, Z. Gui, Y. Sun J. Yang, and X. He. 2015. Mobile Crowd Sensing for Internet of Things: A Credible Crowdsourcing Model in Mobile-sense Service. In Proceedings of IEEE International Conference on Multimedia Big Data.

Digital Library

Google Scholar

[2]

R. B. Das, N. V. Bozdog, and H. Bal. 2017. Cowbird: A Flexible Cloud-Based Framework for Combining Smartphone Sensors and IoT. In 2017 5th IEEE International Conference on Mobile Cloud Computing, Services, and Engineering (MobileCloud).

Google Scholar

[3]

J.Liono, T. Nguyen, P. Jayaraman, and F.D. Salim. 2016. UTE: A Ubiquitous daTa Exploration platform for mobile sensing experiments. In Proceedings of IEEE 17th International Conference on Mobile Data Management.

Google Scholar

[4]

Y. Kim and K. Lee. 2006. A Quality Measurement Method of Context Information in Ubiquitous Environments. In Proceedings of the International Conference on Hybrid Information Technology.

Digital Library

Google Scholar

[5]

M. Krausse and I. Hochstatter. 2005. Challenges in Modelling and Using Quality of Context (QOC). In Proceedings of the The 2nd International Workshop on Mobility Aware Technologies and Applications (MATA 2005).

Digital Library

Google Scholar

[6]

J. Mantyjarvy, J. Himberg, and Pertti Huuskonen. 2003. Collaborative Context Recognition for Handheld Devices. In First IEEE International Conference on Pervasive Computing and Communications (PerCom'03).

Digital Library

Google Scholar

[7]

A. Rauniya, P. Engelstad, B.Feng, and D.V. Thanh. 2016. Crowdsourcing-based Disaster Management using Fog Computing in Internet of Things Paradigm. In Proceedings of 2nd International Conference on Collaboration and Internet Computing.

Google Scholar

[8]

W. Wibisono, T. Ahmad, R.M. Ijtihadie, and A. Hilman. 2014. A prototype of event-based tracking system for mobile users. In Proceedings of World Conggress on Information and Communication Technologies (WICT).

Google Scholar

[9]

H. Xiong, Y. Huang, L.E Barnes, and M.S. Gerber. 2016. Sensus: a cross-platform, general-purpose system for mobile crowdsensing in human-subject studies. In Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing.

Digital Library

Google Scholar

[10]

M. Yuriyama and T. Kushida. 2010. Sensor-Cloud Infrastructure Physical Sensor Management with Virtualized Sensors on Cloud Computing. In Proceedings of the 13th International Conference on Network-Based Information Systems.

Digital Library

Google Scholar

Cited By

View all

Rosyadi FWibisono WAhmad TIjtihadie RShidiqqi A(2019)An Energy-Aware Computation Offloading Framework for a Mobile Crowdsensing Cluster Using DMIPS Approach2019 3rd International Conference on Informatics and Computational Sciences (ICICoS)10.1109/ICICoS48119.2019.8982480(1-5)Online publication date: Oct-2019
https://doi.org/10.1109/ICICoS48119.2019.8982480

A Mobile Crowdsensing Framework for Integrating Smartphone and IoT Devices to Cloud Computing Services
1. Computer systems organization
  1. Architectures

Recommendations

Deep Reinforcement Scheduling for Mobile Crowdsensing in Fog Computing
Special Issue on Fog, Edge, and Cloud Integration

Mobile crowdsensing becomes a promising technology for the emerging Internet of Things (IoT) applications in smart environments. Fog computing is enabling a new breed of IoT services, which is also a new opportunity for mobile crowdsensing. Thus, in ...
A mobile crowdsensing system enhanced by cloud-based social networking services
MCS '13: Proceedings of the First International Workshop on Middleware for Cloud-enabled Sensing

This paper presents TripleS, a novel mobile crowdsensing system enhanced by social networking services, which enables mobile users to participate and perform mobile crowdsensing tasks in an efficient manner. TripleS provides a flexible and universal ...
Phased Scheduling for Resource-Constrained Mobile Devices in Mobile Cloud Computing

Mobile cloud computing combines wireless access service and cloud computing to improve the performance of mobile applications. Mobile cloud computing can balance the application distribution between the mobile device and the cloud, in order to achieve ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

MobiQuitous 2017: Proceedings of the 14th EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services

November 2017

555 pages

ISBN:9781450353687

DOI:10.1145/3144457

General Chairs:
Tao Gu
RMIT University, Australia
,
Ramamohanarao Kotagiri
University of Melbourne, Australia
,
Publications Chair:
Huai Liu
Victoria University, Australia

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]

In-Cooperation

EAI: The European Alliance for Innovation
SIGMM: ACM Special Interest Group on Multimedia

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 07 November 2017

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article
Research
Refereed limited

Conference

MobiQuitous 2017

MobiQuitous 2017: Computing, Networking and Services

November 7 - 10, 2017

VIC, Melbourne, Australia

Acceptance Rates

Overall Acceptance Rate 26 of 87 submissions, 30%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

1
Total Citations
View Citations
162
Total Downloads

Downloads (Last 12 months)1
Downloads (Last 6 weeks)0

Reflects downloads up to 05 Feb 2025

Other Metrics

View Author Metrics

Citations

Cited By

View all

Rosyadi FWibisono WAhmad TIjtihadie RShidiqqi A(2019)An Energy-Aware Computation Offloading Framework for a Mobile Crowdsensing Cluster Using DMIPS Approach2019 3rd International Conference on Informatics and Computational Sciences (ICICoS)10.1109/ICICoS48119.2019.8982480(1-5)Online publication date: Oct-2019
https://doi.org/10.1109/ICICoS48119.2019.8982480

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Abstract

References

Cited By

Recommendations

Deep Reinforcement Scheduling for Mobile Crowdsensing in Fog Computing

A mobile crowdsensing system enhanced by cloud-based social networking services

Phased Scheduling for Resource-Constrained Mobile Devices in Mobile Cloud Computing

Comments

Information

Published In

In-Cooperation

Publisher

Publication History

Permissions

Check for updates

Author Tags

Qualifiers

Conference

Acceptance Rates

Contributors

Other Metrics

Bibliometrics

Article Metrics

Other Metrics

Citations

Cited By

Login options

Full Access

View options

PDF

eReader

Share

Share this Publication link

Share on social media

Affiliations