Web Service Composition Based on Quality of Service on the Internet of Things Using Bee Colony Optimization Algorithm
محورهای موضوعی : journal of Artificial Intelligence in Electrical Engineering
میهن حسین نژاد گرگری
1
,
علی ولی یان خروانق
2
1 - دانشگاه آزاد اسلامی واحد جلفا، ایران
2 - دانشگاه ازاد اسلامی
کلید واژه: bee colony optimization algorithm, Internet of Things, service composition, service quality,
چکیده مقاله :
Today, the rapid development and growth of hardware, network technology, and various types of smart devices that can connect to the Internet and send and receive data have led to the emergence of a new technology called the Internet of Things (IoT). Using IoT technology, many objects in our environment are connected to the Internet and can be managed and controlled through applications available on smartphones and tablets. Service-oriented architecture has been successfully applied in various fields, including grid computing, cloud computing, wireless sensor networks, automotive networks, and the IoT. Service-oriented architecture is an architecture style that supports the loose coupling of services, enabling business flexibility and interoperability independent of technology. It consists of combining a set of business-based services. Since a single service may sometimes fail to meet user needs, industrial organizations prefer service composition to create more complex composite services. The problem of combining IoT services with respect to their Quality of Service (QoS) involves finding a set of candidate services with different non-functional characteristics that satisfy user-specified constraints and optimize an objective function. Thus, combining IoT services based on their QoS is classified as NP-Hard. This paper uses the bee colony optimization (BCO) algorithm to solve the problem of IoT service composition. The results of the simulation on the QWS dataset in the MATLAB 2019 environment show that the proposed method outperforms the genetic algorithm (GA) and the particle swarm optimization (PSO) algorithm.
Today, the rapid development and growth of hardware, network technology, and various types of smart devices that can connect to the Internet and send and receive data have led to the emergence of a new technology called the Internet of Things (IoT). Using IoT technology, many objects in our environment are connected to the Internet and can be managed and controlled through applications available on smartphones and tablets. Service-oriented architecture has been successfully applied in various fields, including grid computing, cloud computing, wireless sensor networks, automotive networks, and the IoT. Service-oriented architecture is an architecture style that supports the loose coupling of services, enabling business flexibility and interoperability independent of technology. It consists of combining a set of business-based services. Since a single service may sometimes fail to meet user needs, industrial organizations prefer service composition to create more complex composite services. The problem of combining IoT services with respect to their Quality of Service (QoS) involves finding a set of candidate services with different non-functional characteristics that satisfy user-specified constraints and optimize an objective function. Thus, combining IoT services based on their QoS is classified as NP-Hard. This paper uses the bee colony optimization (BCO) algorithm to solve the problem of IoT service composition. The results of the simulation on the QWS dataset in the MATLAB 2019 environment show that the proposed method outperforms the genetic algorithm (GA) and the particle swarm optimization (PSO) algorithm.
[1] Q. Li, R. Dou, F. Chen, G. Nan, "A QoS-oriented Web service composition approach based on multi-population genetic algorithm for Internet of things," International Journal of Computational Intelligence Systems. Vol. 7, No. 2, pp. 26-34, 2014.
[2] Y. Xu, W. Xiao, X. Yang, R. Li, Y. Yin, Z. Jiang, "Towards effective semantic annotation for mobile and edge services for Internet-of-Things ecosystems," Future Generation Computer Systems. Vol. 139, No. 1, pp. 64-73, 2023.
[3] A. Saied, N. Nima Jafari, "Service Composition Mechanisms in the Multi-Cloud Environments: A Survey," International journal of new computer architectures and their applications. Vol. 6, No. 2, pp. 40-48, 2016.
[4] F. Seghir, G. Khababa, "An improved discrete flower pollination algorithm for fuzzy QoS-aware IoT services composition based on skyline operator," The Journal of Supercomputing. Vol. No. pp. 2023.
[5] J. Liu, S. Xu, F. Zhang, L. Wang, "A hybrid Genetic-Ant Colony Optimization Algorithm for the Optimal Path Selection," Intelligent Automation & Soft Computing. Vol. 23, No. 2, pp. 235-242, 2017.
[6] M. A. khelili, S. slatnia, O. kazar, A. merizig, S. mirjalili, "Deep learning and metaheuristics application in internet of things: A literature review," Microprocessors and Microsystems. Vol. 98, No. 2, pp. 104792, 2023.
[7] A. Mousa, J. Bentahar, "An Efficient QoS-aware Web Services Selection Using Social Spider Algorithm," Procedia Computer Science. Vol. 94, No. 1, pp. 176-182, 2016.
[8] Y. Zhang, D. Xi, H. Yang, F. Tao, Z. Wang, "Cloud manufacturing based service encapsulation and optimal configuration method for injection molding machine," Journal of Intelligent Manufacturing. Vol. 30, No. 7, pp. 2681-2699, 2019.
[9] J. Jiang, G. Han, C. Lin, "A survey on opportunistic routing protocols in the Internet of Underwater Things," Computer Networks. Vol. 225, No. 1, pp. 109658, 2023.
[10] S. Nayak, N. Ahmed, S. Misra, "Deep Learning-Based Reliable Routing Attack Detection Mechanism for Industrial Internet of Things," Ad Hoc Networks. Vol. 123, No. 2, pp. 102661, 2021.
[11] G. Sambasivam, J. Amudhavel, T. Vengattaraman, P. Dhavachelvan, "An QoS based multifaceted matchmaking framework for web services discovery," Future Computing and Informatics Journal. Vol. 3, No. 2, pp. 371-383, 2018.
[12] B. Andrei, B. A. Elena, "Quality Control in Logistics Activities through Internet of Things Technology," Scientific Bulletin of Naval Academy. Vol. 19, No. 1, pp. 27-30, 2016.
[13] L.-l. Shi, L. Liu, L. Jiang, R. Zhu, J. Panneerselvam, "QoS prediction for smart service management and recommendation based on the location of mobile users," Neurocomputing. Vol. 471, No. 1, pp. 12-20, 2022.
[14] P. Asghari, A. M. Rahmani, H. H. S. Javadi, "Privacy-aware cloud service composition based on QoS optimization in Internet of Things," Journal of Ambient Intelligence and Humanized Computing. Vol. 13, No. 11, pp. 5295-5320, 2022.
[15] A. Kouicem, M. E. Khanouche, A. Tari, "Novel bat algorithm for QoS-aware services composition in large scale internet of things," Cluster Computing. Vol. 25, No. 5, pp. 3683-3697, 2022.
[16] Q. Yu, L. Chen, B. Li, "Ant colony optimization applied to web service compositions in cloud computing," Computers & Electrical Engineering. Vol. 41, No. 2, pp. 18-27, 2015.
[17] H. Kurdi, A. Al-Anazi, C. Campbell, A. Al Faries, "A combinatorial optimization algorithm for multiple cloud service composition," Computers & Electrical Engineering. Vol. 42, No. 2, pp. 107-113, 2015.
[18] Z. Guobing, C. Yixin, X. Yang, H. Ruoyun, X. You, AI Planning and Combinatorial Optimization for Web Service Composition in Cloud Computing, in A nnu al Internatio nal Co nf erence o n Clo u d Co m p u t ing a nd Virtu a lizatio n (CCV 2010). 2010. p. 1-8.
[19] D. Wang, Y. Yang, Z. Mi, "A genetic-based approach to web service composition in geo-distributed cloud environment," Computers & Electrical Engineering. Vol. 43, No. 1, pp. 129-141, 2015.
[20] R. Narges Hesami, K. Esmaeil, J. Mehrdad, "An Optimized Semantic Web Service Composition Method Based on Clustering and Ant Colony Algorithm," ArXiv. Vol. abs/1402.2271, No. pp. 2014.
[21] S. A. Ludwig. Applying Particle Swarm Optimization to Quality-of-Service-Driven Web Service Composition. in 2012 IEEE 26th International Conference on Advanced Information Networking and Applications. 2012.
[22] H. W. Kuhn, "The Hungarian method for the assignment problem," Naval Research Logistics Quarterly. Vol. 2, No. 1-2, pp. 83-97, 1955.
[23] J. Zhou, X. Yao, "A hybrid artificial bee colony algorithm for optimal selection of QoS-based cloud manufacturing service composition," The International Journal of Advanced Manufacturing Technology. Vol. 88, No. 9, pp. 3371-3387, 2017.
[24] E. Al-Masri, Q. H. Mahmoud, Investigating Web Services on the World Wide Web, in Proceedings of the 17th International Conference on World Wide Web. 2008, Association for Computing Machinery. p. 795–804 , numpages = 10.
[25] E. Al-Masri, Q. H. Mahmoud. QoS-based Discovery and Ranking of Web Services. in 2007 16th International Conference on Computer Communications and Networks. 2007.
[26] E. Al-Masri, Q. H. Mahmoud, Discovering the Best Web Service, in Proceedings of the 16th International Conference on World Wide Web. 2007, Association for Computing Machinery. p. 1257–1258 , numpages = 2.
[27] M. S. Das, A. Govardhan, D. V. Lakshmi. A classification approach for web and cloud based applications. in 2016 International Conference on Engineering & MIS (ICEMIS). 2016.
[28] R. Boucetti, O. Hioual, S. M. Hemam, "An approach based on genetic algorithms and neural networks for QoS-aware IoT services composition," Journal of King Saud University - Computer and Information Sciences. Vol. 34, No. 8, Part B, pp. 5619-5632, 2022.
