research-article

Automated and Reproducible Application Traces Generation for IoT Applications

Authors:

Rémy Grünblatt,

Brandon Foubert,

Aris Leivadeas,

Nathalie MittonAuthors Info & Claims

Q2SWinet '21: Proceedings of the 17th ACM Symposium on QoS and Security for Wireless and Mobile Networks

Pages 17 - 24

https://doi.org/10.1145/3479242.3487321

Published: 22 November 2021 Publication History

Abstract

In this paper, we investigate and present how to generate application traces of IoT (Internet of Things) Applications in an automated, repeatable and reproducible manner. By using the FIT IoT-Lab large scale testbed and relying on state-of-the-art software engineering techniques, we are able to produce, collect and share artifacts and datasets in an automated way. This makes it easy to track the impact of software updates or changes in the radio environment both on a small scale, e.g. during a single day, and on a large scale, e.g. during several weeks. By providing both the source code for the trace generation as well as the resulting datasets, we hope to reduce the learning curve to develop such applications and encourage re-usability as well as pave the way for the replication of our results. While we focus in this work on IoT networks, we believe such an approach could be of used in many other networking domains.

References

[1]

C. Adjih, E. Baccelli, E. Fleury, G. Harter, N. Mitton, T. Noel, R. Pissard-Gibollet, F. Saint-Marcel, G. Schreiner, and J. Vandaele. 2015. FIT IoT-LAB: A large scale open experimental IoT testbed. In IEEE World Forum Internet of Things (WF-IoT) .

Digital Library

[2]

E. Baccelli, O. Hahm, M.s Günes, M.and W"ahlisch, and T. C Schmidt. 2013. RIOT OS: Towards an OS for the Internet of Things. In IEEE conference on computer communications workshops (INFOCOM WKSHPS) .

[3]

I. Bouras, F. Aisopos, J. Violos, G. Kousiouris, A. Psychas, T. Varvarigou, G. Xydas, D. Charilas, and Y. Stavroulas. 2019. Mapping of Quality of Service Requirements to Resource Demands for IaaS. Pages: 270.

[4]

P. Brunisholz, E. Duble, F. Rousseau, and A. Duda. 2016. WalT: A Reproducible Testbed for Reproducible Network Experiments. In IEEE INFOCOM Workshop on Computer and Networking Experimental Research Using Testbeds (CNERT).

[5]

A. Buchholtz-Au. [n.d.]. Visual Studio Codespaces is consolidating into GitHub Codespaces . https://devblogs.microsoft.com/visualstudio/visual-studio-codespaces-is-consolidating-into-github-codespaces/

[6]

M.C. Calzarossa, L. Massari, and D. Tessera. 2016. Workload Characterization: A Survey Revisited . Comput. Surveys, Vol. 48, 3 (Feb. 2016), 48:1--48:43.

Digital Library

[7]

Denise Chan. [n.d.]. Sunsetting Mercurial support in Bitbucket - Bitbucket . https://bitbucket.org/blog/sunsetting-mercurial-support-in-bitbucket

[8]

L. Cherkasova, D. Gupta, and A. Vahdat. 2007. Comparison of the three CPU schedulers in Xen . ACM SIGMETRICS Performance Evaluation Review, Vol. 35, 2 (Sept. 2007), 42--51.

Digital Library

[9]

C. Collberg, T. Proebsting, and A.M. Warren. 2015. Repeatability and benefaction in computer systems research. University of Arizona TR, Vol. 14, 4 (2015).

[10]

R. Di Cosmo and S. Zacchiroli. 2017. Software Heritage: Why and How to Preserve Software Source Code. In (iPRES) Int. Conference on Digital .

[11]

Roberto Di Cosmo, Morane Gruenpeter, and Stefano Zacchiroli. 2019. Referencing Source Code Artifacts: a Separate Concern in Software Citation . Computing in Science and Engineering (Dec. 2019), 1--9. https://doi.org/10.1109/MCSE.2019.2963148

[12]

A. Diro and Naveen C. 2018. Distributed attack detection scheme using deep learning approach for Internet of Things. Future Generation Computer Systems, Vol. 82 (2018), 761--768.

[13]

E. Dolstra. [n.d.]. Nix Flakes, Part 1: An introduction and tutorial - Tweag . https://www.tweag.io/blog/2020-05--25-flakes/

[14]

E. Dolstra. 2006. The purely functional software deployment model .Utrecht University.

[15]

E. Dolstra and A. Löh. 2008. NixOS: A purely functional Linux distribution. In 13th ACM SIGPLAN international conference on Functional programming .

Digital Library

[16]

A. Dunkels, B. Gronvall, and T. Voigt. 2004. Contiki - a lightweight and flexible operating system for tiny networked sensors. In 29th Annual IEEE International Conference on Local Computer Networks .

Digital Library

[17]

M. Hasan and MS Goraya. 2018. Fault tolerance in cloud computing environment: A systematic survey. Computers in Industry, Vol. 99 (Aug. 2018), 156--172.

[18]

E. Hodo, X. Bellekens, A. Hamilton, PL Dubouilh, E. Iorkyase, C. Tachtatzis, and R. Atkinson. 2016. Threat analysis of IoT networks using artificial neural network intrusion detection system. In Int. Symposium on Networks, Computers and Communications (ISNCC) .

[19]

M. Imbert. [n.d.]. InriaForge: Forum: Annoucement: Scheduled INRIA Forge end of life . https://gforge.inria.fr/forum/forum.php?forum_id=11543

[20]

R. Jacob, R. Da Forno, R. Trüb, A. Biri, and L. Thiele. 2019. Dataset: Wireless Link Quality Estimation on FlockLab - and Beyond. In Proceedings of the 2nd Workshop on Data Acquisition To Analysis .

Digital Library

[21]

M. Kuzman, X. d. Toro García, S. Escolar, A. Caruso, S. Chessa, and J. C. López. 2019. A Testbed and an Experimental Public Dataset for Energy-Harvested IoT Solutions. In IEEE Int. Conference on Industrial Informatics (INDIN) .

[22]

Y. LeCun, Y. Bengio, and G. Hinton. 2015. Deep learning. Nature, Vol. 521, 7553 (2015), 436--444. https://doi.org/10.1038/nature14539

[23]

M. Lopez-Martin, B. Carro, and A. Sanchez-Esguevillas. 2019. Neural network architecture based on gradient boosting for IoT traffic prediction. Future Generation Computer Systems, Vol. 100 (2019), 656--673.

Digital Library

[24]

N. Mehrabi, F. Morstatter, N. Saxena, K. Lerman, and A. Galstyan. 2019. A Survey on Bias and Fairness in Machine Learning. CoRR (2019).

[25]

M. Miettinen, S. Marchal, I. Hafeez, T. Frassetto, N. Asokan, A. Sadeghi, and S. Tarkoma. 2017. IoT SENTINEL: Automated Device-Type Identification for Security Enforcement in IoT. In IEEE 37th International Conference on Distributed Computing Systems (ICDCS) .

[26]

D. Minoli, K. Sohraby, and B. Occhiogrosso. 2017. IoT Considerations, Requirements, and Architectures for Smart Buildings-Energy Optimization and Next-Generation Building Management Systems. IEEE Internet of Things Journal, Vol. 4, 1 (2017), 269--283. https://doi.org/10.1109/JIOT.2017.2647881

[27]

J. M Perkel. 2020. Challenge to scientists: does your ten-year-old code still run? Nature, Vol. 584, 7822 (2020).

[28]

T. Pflanzner, Z. Feher, and A. Kertesz. 2019. A Crawling Approach to Facilitate Open IoT Data Archiving and Reuse. In 2019 Sixth International Conference on Internet of Things: Systems, Management and Security (IOTSMS) .

[29]

A. Sivanathan, H.H. Gharakheili, F. Loi, A. Radford, C. Wijenayake, A. Vishwanath, and V. Sivaraman. 2019. Classifying IoT Devices in Smart Environments Using Network Traffic Characteristics. IEEE Transactions on Mobile Computing, Vol. 18, 8 (2019), 1745--1759.

[30]

F. Tang, Z. Md. Fadlullah, B. Mao, and N. Kato. 2018. An Intelligent Traffic Load Prediction-Based Adaptive Channel Assignment Algorithm in SDN-IoT: A Deep Learning Approach. IEEE Internet of Things Journal, Vol. 5, 6 (2018), 5141--5154.

[31]

A. Verma, S. Prakash, V. Srivastava, A. Kumar, and S. C. Mukhopadhyay. 2019. Sensing, Controlling, and IoT Infrastructure in Smart Building: A Review. IEEE Sensors Journal, Vol. 19, 20 (2019), 9036--9046. https://doi.org/10.1109/JSEN.2019.2922409

[32]

A. Verma and V. Ranga. 2020. Machine Learning Based Intrusion Detection Systems for IoT Applications. Wireless Personal Communications, Vol. 111 (2020), 2287--2310.

[33]

J. Violos, E. Psomakelis, K. Tserpes, F. Aisopos, and T. Varvarigou. 2019. Leveraging User Mobility and Mobile App Services Behavior for Optimal Edge Resource Utilization. In International Conference on Omni-Layer Intelligent Systems (COINS '19). Association for Computing Machinery, New York, NY, USA.

Digital Library

[34]

Z. Xiao, F. Li, R. Wu, H. Jiang, Y. Hu, J. Ren, C. Cai, and A. Iyengar. 2020. TrajData: On Vehicle Trajectory Collection With Commodity Plug-and-Play OBU Devices. IEEE Internet of Things Journal, Vol. 7 (2020).

Cited By

Koteich JMitton N(2024)Dataset Collection of Multi-Communication Technologies Monitored in Different Mobility Contexts2024 International Wireless Communications and Mobile Computing (IWCMC)10.1109/IWCMC61514.2024.10592486(0439-0444)Online publication date: 27-May-2024
https://doi.org/10.1109/IWCMC61514.2024.10592486
Hameed AViolos JLeivadeas ASanti NGrunblatt RMitton N(2022)Toward QoS Prediction Based on Temporal Transformers for IoT ApplicationsIEEE Transactions on Network and Service Management10.1109/TNSM.2022.321717019:4(4010-4027)Online publication date: Dec-2022
https://doi.org/10.1109/TNSM.2022.3217170
Violos JTsanakas STheodoropoulos TLeivadeas ATserpes KVarvarigou T(2022)Intelligent Horizontal Autoscaling in Edge Computing using a Double Tower Neural NetworkComputer Networks: The International Journal of Computer and Telecommunications Networking10.1016/j.comnet.2022.109339217:COnline publication date: 9-Nov-2022
https://dl.acm.org/doi/10.1016/j.comnet.2022.109339

Index Terms

Automated and Reproducible Application Traces Generation for IoT Applications
1. Computer systems organization
  1. Embedded and cyber-physical systems
    1. Sensor networks
2. Networks
  1. Network performance evaluation
    1. Network experimentation
  2. Network types
    1. Ad hoc networks

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Information & Contributors

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Published In

cover image ACM Conferences

Q2SWinet '21: Proceedings of the 17th ACM Symposium on QoS and Security for Wireless and Mobile Networks

November 2021

143 pages

ISBN:9781450390804

DOI:10.1145/3479242

General Chair:
Periklis Chatzimisios
International Hellenic University, Greece
,
Program Chairs:
Rodolfo W. L. Coutinho
Concordia University, Canada
,
Mirela Notare
University of Technology in Fly Transportation, Brazil

Copyright © 2021 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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New York, NY, United States

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Published: 22 November 2021

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MSWiM '21

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SIGSIM

MSWiM '21: 24th ACM International Conference on Modeling, Analysis and Simulation of Wireless and Mobile Systems

November 22 - 26, 2021

Alicante, Spain

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Cited By

Koteich JMitton N(2024)Dataset Collection of Multi-Communication Technologies Monitored in Different Mobility Contexts2024 International Wireless Communications and Mobile Computing (IWCMC)10.1109/IWCMC61514.2024.10592486(0439-0444)Online publication date: 27-May-2024
https://doi.org/10.1109/IWCMC61514.2024.10592486
Hameed AViolos JLeivadeas ASanti NGrunblatt RMitton N(2022)Toward QoS Prediction Based on Temporal Transformers for IoT ApplicationsIEEE Transactions on Network and Service Management10.1109/TNSM.2022.321717019:4(4010-4027)Online publication date: Dec-2022
https://doi.org/10.1109/TNSM.2022.3217170
Violos JTsanakas STheodoropoulos TLeivadeas ATserpes KVarvarigou T(2022)Intelligent Horizontal Autoscaling in Edge Computing using a Double Tower Neural NetworkComputer Networks: The International Journal of Computer and Telecommunications Networking10.1016/j.comnet.2022.109339217:COnline publication date: 9-Nov-2022
https://dl.acm.org/doi/10.1016/j.comnet.2022.109339

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