Abstract
The Internet of Things (IoT) is made up of many linked devices that are dispersed across multiple areas and provide IoT services to service consumers (SCs). These services are heterogeneous in nature and growing very fast with time. The IoT devices generate a huge amount of raw data, which is meaningless or useless. Thus, the contextualisation of these raw data plays a vital role in improving its meaning. Further, context-based IoT services can increase the execution time and memory size in the service domain. There is a lack of an efficient mechanism to extract useful information to achieve different SCs objectives. Therefore, this article presents a service meta-store (SMS) layer between the device and application level. The atomic service at the device layer is moved toward the upper layer based on context. These atomic services establish different groups based on context. These groups have interacted with the help of the service interface. A service clustering algorithm is designed to capture such a scenario. These services are used in different applications to fulfil the SCs requirements. That means the contextual information is only associated at the lower or device level. This information is unnecessary for other service mechanisms and forms a full phased service system. Various parameters and algorithms are expressed for service clustering. A quality-based service clustering approach enable to reduce the service cluster load. The proposed method is mapped into the real-life scenario. Various characteristics are considered to show the efficiency of the proposed approach. The experimental results proved that the execution time and memory size of context-free are much less than the context-based service clustering mechanism.
Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.Data Availability
The data used for this study are included within this article.
References
Matos Ed, Amaral LAR, Tiburski T, Schenfeld MC, de Azevedo DFG, Hessel F (2017) A sensing-as-a-service context-aware system for internet of things environments. In: 2017 14th IEEE annual consumer communications & networking conference (CCNC), pp 724–727. https://doi.org/10.1109/CCNC.2017.7983223
Dey KA (2001) Understanding and using context. Pers Ubiquitous Comput 5:4–7. https://doi.org/10.1007/s007790170019
Perera C, Zaslavsky A, Christen P, Georgakopoulos D (2014) Context aware computing for the internet of things: a survey. IEEE Commun Surv Tutor 16(1):414–454. https://doi.org/10.1109/SURV.2013.042313.00197
Zhu Z, Wang J (2016) Context aware role updating for iot service recommendation. In: Proceedings of S2 international conference on internet of things, pp. 25–33
Lee D, Lee H (2018) Iot service classification and clustering for integration of iot service platforms. J Supercomput 74:6859–6875. https://doi.org/10.1007/s11227-018-2288-7
Song L (2022) Contextual awareness service of internet of things user interaction mode in intelligent environment. Adv Multimedia. https://doi.org/10.1155/2022/2466032
Moradi H, Zamani B, Zamanifar K (2020) Caasset: a framework for model-driven development of context as a service. Future Gener Comput Syst 105:61–95. https://doi.org/10.1016/j.future.2019.11.028
Moore P, Xhafa F, Barolli L (2014) Context-as-a-service: a service model for cloud-based systems. In: 8th international conference on complex, intelligent and software intensive systems. IEEE, Birmingham, pp 379–385. https://doi.org/10.1109/CISIS.2014.53
Hynes G, Reynolds V, Hauswirth M (2009) A context lifecycle for web-based context management services. In: European conference on smart sensing and context (EuroSSC), vol 5741. Springer, Berlin, pp 51–65. https://doi.org/10.1007/978-3-642-04471-7_5
Sim S, Choi H (2020) A study on the service discovery support method in the iot environments. Int J Electr Eng Educ 57(1):85–96. https://doi.org/10.1177/0020720918813824
Li C, Xu L (2016) Research on the tag aided iot service clustering. In: 2016 5th international conference on computer science and network technology (ICCSNT), pp 98–101. https://doi.org/10.1109/ICCSNT.2016.8070127
Wang X, Wang Z, Xu X (2011) Semi-empirical service composition: a clustering based approach. In: 2011 IEEE international conference on web services, pp 219–226. https://doi.org/10.1109/ICWS.2011.15
Chen L, Wang Y, Yu Q, Zheng Z, Wu J (2013) Wt-lda: user tagging augmented lda for web service clustering. In: Lecture notes in computer science, vol 8274. https://doi.org/10.1007/978-3-642-45005-1_12
Lee D-W, Park K (2017) Development of iot service classification algorithm for integrated service platform. Int J Adv Sci Eng Inf Technol 7:1206–1212. https://doi.org/10.18517/IJASEIT.7.4.2672
Schütte J, Brost GS (2018) Lucon: data flow control for message-based iot systems. In: 2018 17th IEEE international conference on trust, security and privacy in computing and communications/12th IEEE international conference on big data science and engineering (TrustCom/BigDataSE), pp 289–299. https://doi.org/10.1109/TrustCom/BigDataSE.2018.00052
Arellanes D, Lau K-K (2019) Decentralized data flows in algebraic service compositions for the scalability of iot systems. In: 2019 IEEE 5th world forum on internet of things (WF-IoT), pp 668–673 . https://doi.org/10.1109/WF-IoT.2019.8767238
Sánchez-Gallegos DD, Luccio DD, Gonzalez-Compean JL, Montella R (2019) Internet of things orchestration using dagon workflow engine. In: 2019 IEEE 5th world forum on internet of things (WF-IoT), pp 95–100. https://doi.org/10.1109/WF-IoT.2019.8767199
Vahdat-Nejad H, Abbasi-Moud Z, Eslami SA, Mansoor W (2021) Survey on context-aware healthcare systems. In: 2021 IEEE 11th annual computing and communication workshop and conference (CCWC), pp 1190–1196. https://doi.org/10.1109/CCWC51732.2021.9376138
Batista E, Moncusi MA, López-Aguilar P, Martínez-Ballesté A, Solanas A (2021) Sensors for context-aware smart healthcare: a security perspective. Sensors. https://doi.org/10.3390/s21206886
Guo B, Lin S, Zhang D (2010) The architecture design of a cross-domain context management system. In: 8th IEEE international conference on pervasive computing and communications workshops (PERCOM Workshops), pp 499–504. https://doi.org/10.1109/PERCOMW.2010.5470618
Visual Studio (2022) https://visualstudio.microsoft.com/vs/preview/. Released 17 Feb 2022
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors declare that they have no conflict of interest
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Mishra, S., Sarkar, A. Context-free dynamic service clustering of IoT-based services. Innovations Syst Softw Eng 20, 455–466 (2024). https://doi.org/10.1007/s11334-022-00469-z
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11334-022-00469-z