Abstract
The data gathering and composite the services through the IoT devices are the significant need in current scenario. There are many existing systems which gather the data from IoT devices and provide as an analysis based on the service linked with them. The objective of this paper is to build a middleware for IOT tech stack, which can recognize different services and features and categorize them via a ranking solution similar to the page ranking algorithm. The service ranking algorithm (SRA) are linked with HMM to fixates on domain specific requirements and controls services based on said fixated domain. Hence, this algorithm has to be dynamic in nature and should be able to accommodate different domain schemas as possible, where the parameters of distinction for each domain is to be pre specified and the algorithm is to be tuned accordingly. Before ranking begins, selecting the relevant service based on the availability of services, the Service Provider has to decide the kind of services to be offered for the clients based on the weight’s reliability, completeness and energy availability. For implementing this, many intelligent systems are suggested to choose the low cost and high reliable services. In this chapter, a fuzzy rule-based K Nearest Neighbour Classifier is used for categorize the IoT service based on user request. In addition, XGBoost (Extreme Gradient Boosting) is used for dynamic service selection from the available categorized services based on response time and cost. Hidden Markov Model (HMM) is the prediction model used in this proposed work to solve the energy prediction problem.
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References
Perera, C., Zaslavsky, A., Christen, P., Compton, M., Georgakopoulos, D. (2013). Context-aware sensor search, selection and ranking model for internet of things middleware. In 2013 IEEE 14th international conference on mobile data management (vol 1, pp 314–322). IEEE.
Stelmach P. (2013). Service composition scenarios in the internet of things paradigm. In Doctoral conference on computing, electrical and industrial systems (pp. 53–60), Springer, Berlin, Heidelberg.
Shaoshuai, F., Wenxiao, S., Nan, W., & Yan, L. (2011). MODM-based evaluation model of service quality in the internet of things. Procedia Environmental Sciences, 11, 63–69.
Shehu, U. G., Safdar, G. A., & Epiphaniou, G. (2015). Network aware composition for internet of thing services. Transactions on Networks and Communications, 3(1), 45–45.
Esposito, C., Ficco, M., Palmieri, F., & Castiglione, A. (2015). Smart cloud storage service selection based on fuzzy logic, theory of evidence and game theory. IEEE Transactions on Computers, 65(8), 2348–2362.
Li, L., Li, S., & Zhao, S. (2014). QoS-aware scheduling of services-oriented internet of things. IEEE Transactions on Industrial Informatics, 10(2), 1497–1505.
Balasubramaniam, S., Jagannath, R. (2015). A service oriented iot using cluster controlled decision making. In: 2015 IEEE international advance computing conference (IACC) (pp. 558–563) IEEE
Gungor, V. C., Sahin, D., Kocak, T., Ergut, S., Buccella, C., Cecati, C., & Hancke, G. P. (2013). A survey on smart grid potential applications and communication requirements. IEEE Transactions on Industrial Informatics, 9(1), 28–42.
Xiang, C., Panlong, Y., Xuangou, W., Hong, H., & Shucheng, X. (2015). QoS-based service selection with lightweight description for large-scale service-oriented internet of things. Tsinghua Science and Technology, 20(4), 336–347.
Eisa, M., Muhammad, Y., Kashinath, B., & Hong, Z. (2016) Trends and directions in cloud service selection. In: 2016 IEEE symposium on service-oriented system engineering (SOSE) (pp. 423–432) IEEE.
Angelakis, V., Ioannis, A., Nikolaos, P., Emma, F., & Di, Y. (2016). Allocation of heterogeneous resources of an IoT device to flexible services. IEEE Internet of Things Journal, 3(5), 691–700.
Han, D. M., & Lim, J. H. (2010). Design and implementation of smart home energy management systems based on zigbee. IEEE Transactions on Consumer Electronics, 56(3), 1417–1425.
Rodríguez-Valenzuela, S., Holgado-Terriza, J., Muros-Cobos, J.L., & Gutiérrez-Guerrero, J.M. (2012). Data fusion mechanism based on a service composition model for the internet of things. Actas de las III Jornadas de Computación Empotrada (JCE), Septiembre, 19–21.
Khanouche, M. E., Amirat, Y., Chibani, A., Kerkar, M., & Yachir, A. (2016). Energy-centered and QoS-aware services selection for Internet of Things. IEEE Transactions on Automation Science and Engineering, 13(3), 1256–1269.
Wang, C. (2011). A QoS-aware middleware for dynamic and adaptive service execution.
Corno, F., & Razzak, F. (2012). Intelligent energy optimization for user intelligible goals in smart home environments. IEEE Transactions on Smart Grid, 3(4), 2128–2135.
Rault, T., Bouabdallah, A., & Challal, Y. (2014). Energy efficiency in wireless sensor networks: a top-down survey. Computer Networks, 67, 104–122.
Akkaya, K., Guvenc, I., Aygun, R., Pala, N., & Kadri, A. (2015). IoT-based occupancy monitoring techniques for energy-efficient smart buildings. In Wireless communications and networking conference workshops (WCNCW), 2015 IEEE (pp. 58–63). IEEE.
Machado, K., Rosário, D., Cerqueira, E., Loureiro, A. A., Neto, A., & de Souza, J. N. (2013). A routing protocol based on energy and link quality for internet of things applications. Sensors, 13(2), 1942–1964.
Jahn, M., Jentsch, M., Prause, C.R., Pramudianto, F., AlAkkad, A., & Reiners, R. (2010). The energy aware smart home. In Future information technology (FutureTech), 2010 5th international conference on (pp. 1–8). IEEE.
Byun, J., & Park, S., (2011). Development of a self-adapting intelligent system for building energy saving and context-aware smart services. IEEE Transactions on Consumer Electronics, 57(1).
Jaithunbi, A. K., Sabena, S., & SaiRamesh, L.: Trust evaluation of public cloud service providers using genetic algorithm with intelligent rules. Wireless Personal Communications (2021): 1–15.
Deng, Z., Xiaoshu, Z., Debo, C., Ming, Z., & Shichao, Z. (2016). Efficient kNN classification algorithm for big data. Neurocomputing, 195, 143–148.
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SaiRamesh, L., Sabena, S. & Selvakumar, K. Energy Efficient Service Selection from IoT Based on QoS Using HMM with KNN and XGBoost. Wireless Pers Commun 124, 3591–3602 (2022). https://doi.org/10.1007/s11277-022-09527-y
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DOI: https://doi.org/10.1007/s11277-022-09527-y