SYSTEMATIC LITERATURE REVIEW OF IOT METRICS

Donatien Koulla Moulla; Ernest Mnkandla; Alain Abran

doi:10.35784/acs-2023-05

SYSTEMATIC LITERATURE REVIEW OF IOT METRICS

Article Sidebar

Open full text

PDF

Published: Mar 31, 2023

DOI: https://doi.org/10.35784/acs-2023-05

Main Article Content

DOI

https://doi.org/10.35784/acs-2023-05

Authors

Donatien Koulla Moulla

moulladonatien@gmail.com

University of South Africa

https://orcid.org/0000-0001-6594-8378

Ernest Mnkandla

mnkane@unisa.ac.za

https://orcid.org/0000-0003-3989-5617

Alain Abran

alain.abran@etsmtl.ca

https://orcid.org/0000-0003-2670-9061

Abstract

The Internet of Things (IoT) touches almost every aspect of modern society and has changed the way people live, work, travel and, do business. Because of its importance, it is essential to ensure that an IoT system is performing well, as desired and expected, and that this can be assessed and managed with an adequate set of IoT performance metrics. The aim of this study was to systematically inventory and classifies recent studies that have investigated IoT metrics. We conducted a literature review based on studies published between January 2010 and December 2021 using a set of five research questions (RQs) on the current knowledge bases for IoT metrics. A total of 158 IoT metrics were identified and classified into 12 categories according to the different parts and aspects of an IoT system. To cover the overall performance of an IoT system, the 12 categories were organized into an ontology. The findings results show that the category of network metrics was the most discussed in 43% of the studies and, with the highest number of metrics at 37%. This study can provide guidelines for researchers and practitioners in selecting metrics for IoT systems and valuable insights into areas for improvement and optimization.

Keywords:

IoT metrics, Performance metrics, Software metrics, Energy consumption, Network metrics, Systematic literature review

References

Ahmed, M. I., & Kannan, G. (2021). Secure and lightweight privacy preserving internet of things integration for remote patient monitoring. Journal of King Saud University - Computer and Information Sciences, 34(9), 1319-1578. https://doi.org/10.1016/j.jksuci.2021.07.016 DOI: https://doi.org/10.1016/j.jksuci.2021.07.016

Ashton, K. (2009). That ‘Internet of Things’ Thing. Retrieved March 31, 2022 from https://www.rfidjournal.com/that-internet-of-things-thing.

Cui, J., Wang, L., Zhao, X., & Zhang, H. (2020). Towards predictive analysis of android vulnerability using statistical codes and machine learning for iot applications. Computer Communications, 155, 125-131. https://doi.org/10.1016/j.comcom.2020.02.078 DOI: https://doi.org/10.1016/j.comcom.2020.02.078

Djam-Doudou, M., Ari, A. A. A., Emati, J. H. M., Njoya, A. N., Thiare, O., Labraoui, N., & Gueroui, A. M. (2022). A certificate-based pairwise key establishment protocol for IoT resource-constrained devices. Proceedings of the 2nd International Conference of Pan-African Artificial Intelligence and Smart Systems (PAAISS) (pp. 3-18). Springer. https://doi.org/10.1007/978-3-031-25271-6_1 DOI: https://doi.org/10.1007/978-3-031-25271-6_1

Enholm, I. M., Papagiannidis, E., Mikalef, P., & Krogstie, J. (2021). Artificial Intelligence and Business Value: a Literature Review. Information Systems Frontiers, 24, 1709–1734. https://doi.org/10.1007/s10796-021-10186-w DOI: https://doi.org/10.1007/s10796-021-10186-w

Filippova, A., Trainer, E., & Herbsleb, J. D. (2017). From diversity by numbers to diversity as process: Supporting inclusiveness in software development teams with brainstorming. Proceedings of the 39th International conference on software engineering (pp. 152–163). IEEE. https://doi.org/10.1109/ICSE.2017.22 DOI: https://doi.org/10.1109/ICSE.2017.22

Fizza, K., Banerjee, A., Mitra, K, Jayaraman, P. P., Ranjan, R., Patel, P., & Georgakopoulos, D. (2021). QoE in IoT: a vision, survey and future directions. Discover Internet Things, 1(4), 1-14. https://doi.org/10.1007/s43926-021-00006-7 DOI: https://doi.org/10.1007/s43926-021-00006-7

Gandotra, P., & Jha, R. K. (2017). A survey on green communication and security challenges in 5G wireless communication networks. Journal of Network and Computer Applications, 96(C), 39-61. https://doi.org/10.1016/j.jnca.2017.07.002 DOI: https://doi.org/10.1016/j.jnca.2017.07.002

Hasan, M., Islam, M. M., Zarif, M. I. I., & Hashem, M. (2019). Attack and anomaly detection in IoT sensors in IoT sites using machine learning approaches. Internet of Things, 7, 100059.

https://doi.org/10.1016/j.iot.2019.100059 DOI: https://doi.org/10.1016/j.iot.2019.100059

Hindle, A. (2015). Green mining: a methodology of relating software change and configuration to power consumption. Empirical Software Engineering, 20(2), 374-409. https://doi.org/10.1007/s10664-013-9276-6 DOI: https://doi.org/10.1007/s10664-013-9276-6

Iwendi, C., Maddikunta, P. K. R., Gadekallu, T. R., Lakshmanna, K., Bashir, A. K., & Piran, M. J. (2020). A metaheuristic optimization approach for energy efficiency in the IoT networks. Software: Practice and Experience, 51(12), 2558– 2571. https://doi.org/10.1002/spe.2797 DOI: https://doi.org/10.1002/spe.2797

Jagroep, E., Broekman, J., van der Werf, J. M. E. M., Lago, P., Brinkkemper, S., Blom, L., & Vliet, R. (2017). Awakening awareness on energy consumption in software engineering. 2017 IEEE/ACM 39th International Conference on Software Engineering: Software Engineering in Society Track (ICSESEIS) ( pp.76–85). IEEE. https://doi.org/10.1109/ICSE-SEIS.2017.10 DOI: https://doi.org/10.1109/ICSE-SEIS.2017.10

Kim, M., Park, J. H., & Lee, N. Y. (2017). A Quality Model for IoT Service. In: J. Park, Y. Pan, G. Yi & V. Loia (Eds.), Advances in Computer Science and Ubiquitous Computing. UCAWSN CUTE CSA 2016. Lecture Notes in Electrical Engineering (vol. 421, pp. 497-504). Springer. https://doi.org/10.1007/978-981-10-3023-9_77 DOI: https://doi.org/10.1007/978-981-10-3023-9_77

Kitchenham, B. A., & Charters, S. (2007). Guidelines for performing systematic literature review in software engineering. Keele University.

Klima, M., Rechtberger, V., Bures, M., Bellekens, X., Hindy, H., & Ahmed, B. S. (2020). Quality and Reliability Metrics for IoT Systems: A Consolidated View. In S. Paiva, S. I. Lopes, R. Zitouni, N. Gupta, S. F. Lopes& T. Yonezawa (Eds.), Science and Technologies for Smart Cities. SmartCity360° 2020. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering (pp. 635-650). Springer. https://doi.org/10.1007/978-3-030-76063-2_42 DOI: https://doi.org/10.1007/978-3-030-76063-2_42

Koçak, S. A. (2021). Software energy consumption prediction using software code metrics (PhD disertation), Environmental Applied Science and Management, Ryerson University, Canada. https://doi.org/10.32920/ryerson.14666424.v1 DOI: https://doi.org/10.32920/ryerson.14666424.v1

Kuemper, D., Iggena, T., Toenjes, R., & Pulvermueller, E. (2018). Valid.IoT: a framework for sensor data quality analysis and interpolation. Proceedings of the 9th ACM Multimedia Systems Conference (pp. 294-303). The ACM Digital Library. https://doi.org/10.1145/3204949.3204972 DOI: https://doi.org/10.1145/3204949.3204972

Kumar, R., & Sharma, R. (2021). Leveraging blockchain for ensuring trust in IoT: A survey. Journal of King Saud University - Computer and Information Sciences, 34(10), 1319-1578. https://doi.org/10.1016/j.jksuci.2021.09.004 DOI: https://doi.org/10.1016/j.jksuci.2021.09.004

Magno, M., Aoudia, F. A., Gautier, M., Berder, O., & Benini, L. (2017). WULoRa: An Energy Efficient IoT End-Node for Energy Harvesting and Heterogeneous Communication. Proceedings of IEEE/ACM

Design, Automation & Test in Europe Conference & Exhibition (pp. 1528-1533). IEEE. https://doi.org/10.23919/DATE.2017.7927233 DOI: https://doi.org/10.23919/DATE.2017.7927233

Roy, S., Mazumdar, N., & Pamula, R. (2021). An energy optimized and QoS concerned data gathering protocol for wireless sensor network using variable dimensional PSO. Ad Hoc Networks, 123(C), 1-19. https://doi.org/10.1016/j.adhoc.2021.102669 DOI: https://doi.org/10.1016/j.adhoc.2021.102669

Savola, R., Abie, H., & Sihvonen, M. (2012). Towards metrics-driven adaptive security management in Ehealth IoT applications. In I. Balasingham (Ed.), Proceedings of the 7th International Conference on Body Area Networks (BodyNets ‘12) (pp. 276–281). The ACM Digital Library. https://dl.acm.org/doi/abs/10.5555/2442691.2442753 DOI: https://doi.org/10.4108/icst.bodynets.2012.250241

Soubra, H., & Abran, A. (2017). Functional Size Measurement for the Internet of Things (IoT): An example using COSMIC and the Arduino open source platform. In M. Staron &W. Meding (Eds.), Proceedings of the International Workshop on Software Measurement and the International Conference on Software Process and Product Measurement (pp. 122-128). The ACM Digital Library. https://doi.org/10.1145/3143434.3143452 DOI: https://doi.org/10.1145/3143434.3143452

Tavakolan, M., & Faridi, I. A. (2020). Applying privacy-aware policies in IoT devices using privacy metrics. Proceedings of the International Conference on Communications, Computing, Cybersecurity, and Informatics (CCCI) (pp.1-5). IEEE. https://doi.org/10.1109/CCCI49893.2020.9256605 DOI: https://doi.org/10.1109/CCCI49893.2020.9256605

Taylor, P. J., Dargahi, T., Dehghantanha, A., & Parizi, R. M. (2020). A systematic literature review of blockchain cyber security. Digital Communications and Networks, 6(2), 147-156. https://doi.org/10.1016/j.dcan.2019.01.005 DOI: https://doi.org/10.1016/j.dcan.2019.01.005

Voas, J., Kuhn, R., & Laplante, P. A. (2018). IoT metrology. IT Professional, 20(3), 6-10. https://doi.org/10.1109/MITP.2018.032501740 DOI: https://doi.org/10.1109/MITP.2018.032501740

Wu, H., Shi, L., Chen, C., Wang, Q., & Boehm, B. (2016). Maintenance Effort Estimation for Open Source Software: A Systematic Literature Review. Proceedings of the International Conference on Software Maintenance and Evolution, (pp. 32-43). IEEE. https://doi.org/10.1109/ICSME.2016.87 DOI: https://doi.org/10.1109/ICSME.2016.87

Yang, Y., Wu, L., Yin, G., Li, L., & Zhao, H. (2017). A Survey on Security and Privacy Issues in Internet-ofThings. IEEE Internet of Things Journal, 4(5), 1250-1258. https://doi.org/10.1109/JIOT.2017.2694844 DOI: https://doi.org/10.1109/JIOT.2017.2694844

Zahoor, S., & Mir, R. N. (2021). Resource management in pervasive Internet of Things: A survey. Journal of King Saud University - Computer and Information Sciences, 33(8), 921-935. https://doi.org/10.1016/j.jksuci.2018.08.014 DOI: https://doi.org/10.1016/j.jksuci.2018.08.014

Zhang, S., Bai, G., Li, H., Liu, P., Zhang, M., & Li S. (2021). Multi-Source Knowledge Reasoning for DataDriven IoT Security. Sensors, 21(22), 7579. https://doi.org/10.3390%2Fs21227579 DOI: https://doi.org/10.3390/s21227579

Zhou, J., Cao, Z., Dong, X., & Vasilakos, A. V. (2017). Security and privacy for cloud-based IoT: challenges. IEEE Communications Magazine, 55(1), 26-33. https://doi.org/10.1109/MCOM.2017.1600363CM DOI: https://doi.org/10.1109/MCOM.2017.1600363CM

Article Details

Moulla, D. K., Mnkandla, E., & Abran, A. (2023). SYSTEMATIC LITERATURE REVIEW OF IOT METRICS. Applied Computer Science, 19(1), 64–81. https://doi.org/10.35784/acs-2023-05

ACM
ACS
APA
ABNT
Chicago
Harvard
IEEE
MLA
Turabian
Vancouver

Endnote/Zotero/Mendeley (RIS)
BibTeX

Abstract views: 467

License

This work is licensed under a Creative Commons Attribution 4.0 International License.

All articles published in Applied Computer Science are open-access and distributed under the terms of the Creative Commons Attribution 4.0 International License.

Author Biographies