MULTI-LAYERED FEATURE MODELS FOR ENHANCED IOT APPLICATION DEPLOYMENT IN EDGE ENVIRONMENTS

Dr. Caroline M. Rivers; Dr. Jared E. Nolan

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MULTI-LAYERED FEATURE MODELS FOR ENHANCED IOT APPLICATION DEPLOYMENT IN EDGE ENVIRONMENTS

Dr. Caroline M. Rivers ¹ , Dr. Jared E. Nolan ²

¹ Department of Communication, Utah State University, Logan, UT, USA

² Department of Political Science, Western Carolina University, Cullowhee, NC, USA

Issue Vol. 1 No. 01 (2024): Volume 01 Issue 01 --- Section Articles --- Published Date: 2024-12-29

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Abstract

The pervasive growth of Internet of Things (IoT) applications necessitates robust and efficient deployment strategies, particularly within the constrained and dynamic environments of edge computing infrastructures. Traditional cloud-centric models often suffer from high latency and bandwidth limitations, making edge computing a crucial paradigm for processing data closer to its source [6, 17, 45, 46]. This article explores the application of multi-layered feature models as a sophisticated approach to support and optimize the deployment of diverse IoT applications on heterogeneous edge-based infrastructures. Feature models, a cornerstone of Software Product Line Engineering (SPLE), provide a structured way to represent commonalities and variabilities within a system [12, 20]. By extending these models to multiple layers, we can capture the intricate interdependencies between IoT application features, underlying edge infrastructure capabilities, and deployment configurations. This approach facilitates automated reasoning, configuration, and optimization of deployment decisions, addressing challenges such as resource allocation, energy efficiency, and latency reduction in dynamic edge environments [24, 25, 34, 49]. We discuss the theoretical foundations, methodological considerations, potential benefits, and future research directions for leveraging multi-layered feature models to achieve flexible, scalable, and performant IoT deployments at the edge.

Keywords

IoT, Edge Computing, Multi-Layered Feature Models, Software Product Lines

References

1. Abbas, A., Farah Siddiqui, I., Lee, S.U., Kashif Bashir, A., Ejaz, W., Qureshi, N.M.F., 2018. Multi-objective optimum solutions for IoT-based feature models of software product line. IEEE Access 6, 12228–12239.

2. Acher, M., Collet, P., Gaignard, A., Lahire, P., Montagnat, J., France, R.B., 2012. Composing multiple variability artifacts to assemble coherent workflows. Softw. Qual. J. 20 (3), 689–734.

3. Acher, M., Collet, P., Lahire, P., France, R.B., 2013. FAMILIAR: A domain-specific language for large scale management of feature models. Sci. Comput. Program. 78 (6), 657–681.

4. Cañete, A., Amor, M., Fuentes, L., 2022. The Journal of Systems & Software 183, 111086.

5. Lettner, M., Rodas, J., Galindo, J.A., Benavides, D., 2019. Automated analysis of two-layered feature models with feature attributes. J. Comput. Lang. 51, 154–172.

6. Liu, F., Tang, G., Li, Y., Cai, Z., Zhang, X., Zhou, T., 2019. A survey on edge computing systems and tools. Proc. IEEE 107 (8), 1537–1562.

7. Mao, Y., You, C., Zhang, J., Huang, K., Letaief, K.B., 2017. A survey on mobile edge computing: The communication perspective. IEEE Commun. Surv. Tutor. 19 (4), 2322–2358.

8. Melendez, S., McGarry, M.P., 2017. Computation offloading decisions for reducing completion time. In: 2017 14th IEEE Annual Consumer Communications Networking Conference (CCNC). pp. 160–164.

9. Niewiadomski, A., Skaruz, J., Penczek, W., Szreter, M., Jarocki, M., 2014. SMT versus genetic and OpenOpt algorithms: Concrete planning in the PlanICS framework. Fund. Inform. 135, 451–466.

10. Özbakir, L., Baykasoğlu, A., Tapkan, P., 2010. Bees algorithm for generalized assignment problem. Appl. Math. Comput. 215 (11), 3782–3795.

11. Plauth, M., Feinbube, L., Polze, A., 2017. A Performance Survey of Lightweight Virtualization Techniques. pp. 34–48.

12. Pohl, K., Böckle, G., Linden, F., 2005. Software Product Line Engineering: Foundations, Principles, and Techniques.

13. Premsankar, G., Di Francesco, M., Taleb, T., 2018. Edge computing for the internet of things: A case study. IEEE Internet Things J. 5 (2), 1275–1284.

14. Rabiser, D., Prähofer, H., Grünbacher, P., Petruzelka, M., Eder, K., Angerer, F., Kromoser, M., Grimmer, A., 2016. Multi-purpose, multi-level feature modeling of large-scale industrial software systems. Softw. Syst. Model. 17.

15. Reiser, M.-O., Weber, M., 2007. Multi-level feature trees. Requir. Eng. 12 (2), 57–75.

16. Rosenmüller, M., Siegmund, N., Thüm, T., Saake, G., 2011. Multi-dimensional variability modeling. In: Fifth International Workshop on Variability Modelling of Software-Intensive Systems, Namur, Belgium, January 27-29, 2011. Proceedings. In: ACM International Conference Proceedings Series, ACM, pp. 11–20.

17. Ai, Y., Peng, M., Zhang, K., 2018. Edge computing technologies for Internet of Things: a primer. Digit. Commun. Netw. 4 (2), 77–86.

18. Al-Shuwaili, A., Simeone, O., 2017. Energy-efficient resource allocation for mobile edge computing-based augmented reality applications. IEEE Wirel. Commun. Lett. 6 (3), 398–401.

19. Bagchi, S., Siddiqui, M.-B., Wood, P., Zhang, H., 2020. Dependability in edge computing. Commun. ACM 63 (1), 58–66.

20. Benavides, D., Trinidad, P., Ruiz-Cortés, A., 2005. Automated reasoning on feature models. In: Advanced Information Systems Engineering. Springer Berlin Heidelberg, pp. 491–503.

21. Bjørner, N., Phan, A.-D., Fleckenstein, L., 2015. νZ - AN optimizing SMT solver. In: Tools and Algorithms for the Construction and Analysis of Systems. Springer Berlin Heidelberg, Berlin, Heidelberg, pp. 194–199.

22. Bratterud, A., Walla, A., Haugerud, H., Engelstad, P.E., Begnum, K., 2015. IncludeOS: A minimal, resource efficient unikernel for cloud services. In: 2015 IEEE 7th International Conference on Cloud Computing Technology and Science (CloudCom). pp. 250–257.

23. Bulej, L., Bures, T., Filandr, A., Hnetynka, P., Hnetynková, I., Pacovsky, J., Sandor, G., Gerostathopoulos, I., 2021. Managing latency in edge-cloud environment. J. Syst. Softw. 172, 110872.

24. Cañete, A., Amor, M., Fuentes, L., 2020. Energy-efficient deployment of IoT applications in edge-based infrastructures: A software product line approach. IEEE Internet Things J. 1.

25. Cañete, A., Amor, M., Fuentes, L., 2019. Optimal assignment of augmented reality tasks for edge-based variable infrastructures. In: 13th Int. Conf. on Ubiquitous Computing and Ambient Intelligence, UCAmI 2019, Toledo, Spain, December 2-5, 2019. In: MDPI Proceedings, vol. 31, MDPI, p. 28.

26. Cañete, A., Horcas, J.-M., Ayala, I., Fuentes, L., 2020. Energy efficient adaptation engines for android applications. Inf. Softw. Technol. 118, 106220.

27. Casalicchio, E., 2019. Container orchestration: A survey. In: Systems Modeling: Methodologies and Tools. Springer International Publishing, pp. 221–235.

28. Cecchinel, C., Mosser, S., Collet, P., 2016. Automated deployment of data collection policies over heterogeneous shared sensing infrastructures. In: 23rd Asia-Pacific Software Engineering Conference, APSEC 2016, Hamilton, New Zealand, December 6-9, 2016. IEEE Computer Society, pp. 329–336.

29. Chen, M., Dong, M., Liang, B., 2016. Joint offloading decision and resource allocation for mobile cloud with computing access point. In: 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). pp. 3516–3520.

30. Chen, M., Yixue, H., 2018. Task offloading for mobile edge computing in software defined ultra-dense network. IEEE J. Sel. Areas Commun. PP, 1.

31. Czarnecki, K., Helsen, S., Eisenecker, U., 2005. Formalizing cardinality-based feature models and their specialization. Softw. Process: Improv. Pract. 10 (1), 7–29.

32. De Moura, L., Bjørner, N., 2008. Z3: An efficient SMT solver. In: Proceedings of the Theory and Practice of Software, 14th International Conference on Tools and Algorithms for the Construction and Analysis of Systems. In: TACAS’08/ETAPS’08, Springer-Verlag, Berlin, Heidelberg, pp. 337–340.

33. Dhungana, D., Grünbacher, P., Rabiser, R., Neumayer, T., 2010. Structuring the modeling space and supporting evolution in software product line engineering. J. Syst. Softw. 83 (7), 1108–1122, SPLC 2008.

34. Dinh, T.Q., Tang, J., La, Q.D., Quek, T.Q.S., 2017. Offloading in mobile edge computing: Task allocation and computational frequency scaling. IEEE Trans. Commun. 65 (8), 3571–3584.

35. Elazhary, H., 2018. Internet of Things (IoT), mobile cloud, cloudlet, mobile IoT, IoT cloud, fog, mobile edge, and edge emerging computing paradigms: Disambiguation and research directions. J. Netw. Comput. Appl. 128, 105–140.

36. Farahani, E., Habibi, J., 2019. Feature model configuration based on two-layer modelling in software product lines. Int. J. Electr. Comput. Eng. 9, 1–11.

37. Gámez, N., Fuentes, L., 2013. Architectural evolution of FamiWare using cardinality-based feature models. Inf. Softw. Technol. 55 (3), 563–580.

38. Geraldi, R.T., Reinehr, S., Malucelli, A., 2020. Software product line applied to the Internet of Things: A systematic literature review. Inf. Softw. Technol. 124, 106293.

39. Guo, J., White, J., Wang, G., Li, J., Wang, Y., 2011. A genetic algorithm for optimized feature selection with resource constraints in software product lines. J. Syst. Softw. 84 (12), 2208–2221.

40. Holl, G., Grünbacher, P., Rabiser, R., 2012. A systematic review and an expert survey on capabilities supporting multi product lines. Inf. Softw. Technol. 54 (8), 828–852.

41. Huang, M., Liu, W., Wang, T., Liu, A., Zhang, S., 2020. A cloud–MEC collaborative task offloading scheme with service orchestration. IEEE Internet Things J. 7 (7), 5792–5805.

42. James, A., Schien, D., 2019. A low carbon kubernetes scheduler. In: Proceedings of the 6th International Conference on ICT for Sustainability, ICT4S 2019, Lappeenranta, Finland, June 10-14, 2019. In: CEUR Workshop Proceedings, vol. 2382, CEUR-WS.org.

43. Köksal, O., Tekinerdogan, B., 2019. Architecture design approach for IoT-based farm management information systems. Precis. Agric. 20 (5), 926–958.

44. Sarker, V.K., Peña Queralta, J., Gia, T.N., Tenhunen, H., Westerlund, T., 2019. Offloading SLAM for indoor mobile robots with edge-fog-cloud computing. In: 2019 1st Int. Conf. on Advances in Science, Engineering and Robotics Technology (ICASERT). pp. 1–6.

45. Satyanarayanan, M., 2017. The emergence of edge computing. Computer 50 (1), 30–39.

46. Shi, W., Pallis, G., Xu, Z., 2019. Edge computing [scanning the issue]. Proc. IEEE 107 (8), 1474–1481.

47. Spinczyk, O., Beuche, D., 2004. Modeling and building software product lines with eclipse. In: Companion to the 19th Annual ACM SIGPLAN Conference on Object-Oriented Programming Systems, Languages, and Applications. In: OOPSLA ’04, ACM, New York, NY, USA, pp. 18–19.

48. Sundermann, C., Thüm, T., Schaefer, I., 2020. Evaluating #SAT solvers on industrial feature models. In: Proceedings of the 14th Int. Conference on Variability Modelling of Software-Intensive Systems. In: VAMOS ’20, ACM, New York, NY, USA.

49. Tran, T.X., Pompili, D., 2019. Joint task offloading and resource allocation for multi-server mobile-edge computing networks. IEEE Trans. Veh. Technol. 68 (1), 856–868.

50. Wang, J., Pan, J., Esposito, F., Calyam, P., Yang, Z., Mohapatra, P., 2019. Edge cloud offloading algorithms: Issues, methods, and perspectives. ACM Comput. Surv. 52 (1), 2:1–2:23.

51. Zhang, K., Mao, Y., Leng, S., Zhao, Q., Li, L., Peng, X., Pan, L., Maharjan, S., Zhang, Y., 2016. Energy-efficient offloading for mobile edge computing in 5G heterogeneous networks. IEEE Access 4, 5896–5907.

52. Zhang, W., Wen, Y., Wu, D.O., 2013. Energy-efficient scheduling policy for collaborative execution in mobile cloud computing. In: 2013 Proceedings IEEE INFOCOM. pp. 190–194.

How to Cite

MULTI-LAYERED FEATURE MODELS FOR ENHANCED IOT APPLICATION DEPLOYMENT IN EDGE ENVIRONMENTS. (2024). European Journal of Emerging Real-Time IoT and Edge Infrastructures, 1(01), 49-68. https://parthenonfrontiers.com/index.php/ejertiotei/article/view/137

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