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Systematic literature review of IoT metrics

Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
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 is to systematically inventory and classify recent studies that have investigated IoT metrics. The authors 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 results show that the category of network metrics was most frequently 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.
Rocznik
Strony
64--81
Opis fizyczny
Bibliogr. 30 poz., fig., tab.
Twórcy
  • University of South Africa, School of Computing, Department of Computer Science, South Afric
  • University of Maroua, National Advanced School of Mines and Petroleum Industries, Department of Fundamental Sciences, Cameroon
  • University of South Africa, School of Computing, Department of Computer Science, South Africa
autor
  • École de Technologie Supérieure, Department of Software Engineering and Information Technology, Canada
Bibliografia
  • [1] 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
  • [2] Ashton, K. (2009). That ‘Internet of Things’ Thing. Retrieved March 31, 2022 from https://www.rfidjournal.com/that-internet-of-things-thing.
  • [3] 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
  • [4] 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
  • [5] 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
  • [6] 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
  • [7] 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
  • [8] 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
  • [9] 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
  • [10] 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
  • [11] 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
  • [12] 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 (ICSE-SEIS) ( pp.76–85). IEEE. https://doi.org/10.1109/ICSE-SEIS.2017.10
  • [13] 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 2016 2016. Lecture Notes in Electrical Engineering (vol. 421, pp. 497-504). Springer. https://doi.org/10.1007/978-981-10-3023-9_77
  • [14] Kitchenham, B. A., & Charters, S. (2007). Guidelines for performing systematic literature review in software engineering. Keele University.
  • [15] 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
  • [16] 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
  • [17] 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
  • [18] 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
  • [19] 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
  • [20] 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
  • [21] Savola, R., Abie, H., & Sihvonen, M. (2012). Towards metrics-driven adaptive security management in E-health 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
  • [22] 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
  • [23] 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
  • [24] 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
  • [25] Voas, J., Kuhn, R., & Laplante, P. A. (2018). IoT metrology. IT Professional, 20(3), 6-10. https://doi.org/10.1109/MITP.2018.032501740
  • [26] 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
  • [27] Yang, Y., Wu, L., Yin, G., Li, L., & Zhao, H. (2017). A Survey on Security and Privacy Issues in Internet-of-Things. IEEE Internet of Things Journal, 4(5), 1250-1258. https://doi.org/10.1109/JIOT.2017.2694844
  • [28] 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
  • [29] Zhang, S., Bai, G., Li, H., Liu, P., Zhang, M., & Li S. (2021). Multi-Source Knowledge Reasoning for Data-Driven IoT Security. Sensors, 21(22), 7579. https://doi.org/10.3390%2Fs21227579
  • [30] 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
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-0153cd9b-3c0b-4577-b70d-2d259e707c9b
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