An Efficient Systematic Approach for Adaptability Synthesis of IOT's Performance


Mehak Fatima
Hamayun Khan
Irfan uddin
Muhammad Nabeel Amin
Attiq Ur Rehman


The Internet of Things (IoT) has profoundly impacted various facets of contemporary society, transforming the ways in which individuals live, work, travel, and conduct business. Given its significance, it becomes imperative to ensure that IoT systems perform as intended and anticipated. This necessitates the availability of a comprehensive set of IoT performance metrics for assessment and management. This research endeavor's primary objective is to methodically catalog and categorize recent explorations into Internet of Things measurements. The writers executed a review of the literature encompassing research findings published from January 2010 until December 2021, guided by five research questions in all. Through this review, 158 in total distinct IoT measurements were unearthed and systematically grouped into 12 distinct groups, each pertaining to different facets and elements of IoT systems. To holistically assess IoT system performance, these twelve categories were carefully arranged in ontology. The outcomes unveiled the network metrics emerged as the most prevalent category of discussion, appearing 43 percent of the analyzed research, and boasting the greatest percentage of metrics, 37%. This research stands as a valuable resource for both researchers and practitioners, offering guidance when choosing the right metrics for Internet of Things systems. Additionally, it provides priceless insights into topics ripe for enhancement and optimization in the realm of IoT performance evaluation.


How to Cite
Fatima, M., Khan, H., uddin, I., Nabeel Amin, M. and Ur Rehman, A. 2023. An Efficient Systematic Approach for Adaptability Synthesis of IOT’s Performance. Journal of Policy Research. 9, 4 (Dec. 2023), 9–18. DOI:


  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. DOI:
  2. Ashton, K. (2009). That ‘Internet of Things’ Thing. Retrieved March 31, 2022 from
  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. DOI:
  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. DOI:
  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. 021-10186-w DOI:
  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. DOI:
  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. DOI:
  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. DOI:
  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. DOI:
  10. 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. DOI:
  11. 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. DOI:
  12. 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. DOI:
  13. Kitchenham, B. A., & Charters, S. (2007). Guidelines for performing systematic literature review in software engineering. Keele University.
  14. 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. DOI:
  15. Koçak, S. A. (2021). Software energy consumption prediction using software code metrics (PhD disertation), Environmental Applied Science and Management, Ryerson University, Canada. DOI:
  16. 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. DOI:
  17. 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. DOI:
  18. 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. DOI:
  19. 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. DOI:
  20. Hindle, A. (2015). Green mining: a methodology of relating software change and configuration to power consumption. Empirical Software Engineering, 20(2), 374-409. 9276-6 DOI:
  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. DOI:
  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. DOI:
  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. DOI:
  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. DOI:
  25. Voas, J., Kuhn, R., & Laplante, P. A. (2018). IoT metrology. IT Professional, 20(3), 6-10. DOI:
  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. DOI:
  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. DOI:
  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. DOI:
  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. DOI:
  30. Zhou, J., Cao, Z., Dong, X., & Vasilakos, A. V. (2017). Security and privacy for cloud-based IoT: challenges.
  31. IEEE Communications Magazine, 55(1), 26-33. DOI: