Provisioning for Sensory Data Using Enterprise Service Bus: A Middleware Epitome

  • Robin Singh BhadoriaEmail author
  • Narendra S. Chaudhari
Conference paper


The importance of sensory data is associated with computing which starts with the characteristics of middleware that has been recognized with sharing and utilization of resources. However, an architectural characteristic for supporting this feature of sharing depends on interaction patterns that utilize different data formats. These patterns also help in establishing communication between multiple services and its associated components. The main goal of these services is to carry the data which are sensed/captured from different sensor motes (nodes) and have been forwarded through specified gateway to the global repository. This computing methodology could be achieved by adopting a logical separation of services from the actual mechanism of resource assignment and allotment. This separability is best handled with enterprise service bus (ESB), which is an architectural framework for managing services with its data. This paper discusses the methodology for handling sensory data and making the overall system as stable by removing the noise or error received data during sensing process from sensor mote.


Service-oriented systems Middleware Sensory data Enterprise service bus Data processing 


  1. 1.
    Romero, D., Vernadat, F.: Enterprise information systems state of the art: past, present and future trends. Comput. Indus. 79, 3–13 (2016)CrossRefGoogle Scholar
  2. 2.
    Martínez-Carreras, M.A., García Jimenez, F.J., Gómez Skarmeta, A.F.: Building integrated business environments: analysing open-source ESB. Enterp. Inf. Syst. 9(4), 401–435 (2015)CrossRefGoogle Scholar
  3. 3.
    Qiu, X., Luo, H., Xu, G., Zhong, R., Huang, G.Q.: Physical assets and service sharing for IoT-enabled Supply Hub in Industrial Park (SHIP). Int. J. Prod. Econ. 159, 4–15 (2015)CrossRefGoogle Scholar
  4. 4.
    Lucas-Martínez, N., Martínez, J.F., Hernández-Díaz, V.: Virtualization of event sources in wireless sensor networks for the internet of things. Sensors. 14(12), 22737–22753 (2014)CrossRefGoogle Scholar
  5. 5.
    Chang, C., Srirama, S.N., Buyya, R.: Mobile cloud business process management system for the internet of things: review, challenges and blueprint. ACM Comput. Surv. (CSUR). 49(4), 70 (2015)Google Scholar
  6. 6.
    Alena, R., Ossenfort, J., Stone, T., Baldwin, J.: Wireless Space Plug-and-Play Architecture (SPA-Z). In: Aerospace Conference, 2014 IEEE, pp. 1–17 (2014)Google Scholar
  7. 7.
    Kanagaraj, E., Kamarudin, L.M., Zakaria, A., Gunasagaran, R., Shakaff, A.Y.M.: Cloud-based remote environmental monitoring system with distributed WSN weather stations. In: SENSORS, 2015 IEEE, pp. 1–4 (2015)Google Scholar
  8. 8.
    Rodríguez-Molina, J., Martínez, J.F., Castillejo, P., Rubio, G.: Development of middleware applied to microgrids by means of an open source enterprise service bus. Energies. 10(2), 172 (2017)CrossRefGoogle Scholar
  9. 9.
    Bhadoria, R.S., Chaudhari, N.S., Samanta, S.: Uncertainty in sensor data acquisition for SOA system. Neural Comput. Appl. 1–11 (2017)Google Scholar
  10. 10.
    Palumbo, F., Ullberg, J., Štimec, A., Furfari, F., Karlsson, L., Coradeschi, S.: Sensor network infrastructure for a home care monitoring system. Sensors. 14(3), 3833–3860 (2014)CrossRefGoogle Scholar
  11. 11.
    Lee, W.T., Ma, S.P.: Process modeling and analysis of service-oriented architecture–based wireless sensor network applications using multiple-domain matrix. Int. J. Distrib. Sens. Netw. 12(11), 1–15 (2016)Google Scholar
  12. 12.
    Sahni, Y., Cao, J., Liu, X.: MidSHM: a middleware for WSN-based SHM application using service-oriented architecture. Futur. Gener. Comput. Syst. 80, 263–274 (2018)CrossRefGoogle Scholar
  13. 13.
    UltraESB: Light-Weight and Lean Enterprise Integration, AdroitLogic. Accessed 25 Feb 2018
  14. 14.
    Bhadoria, R.S., Chaudhari, N.S., Vidanagama, T.N.: Analyzing the role of interfaces in enterprise service bus: a middleware epitome for service-oriented systems. Comput. Stand. Interf. 56, 146–155 (2018)CrossRefGoogle Scholar
  15. 15.
    Stoimenov, L., Bogdanovic, M., Bogdanovic-Dinic, S.: ESB-based sensor web integration for the prediction of electric power supply system vulnerability. Sensors. 13(8), 10623–10658 (2013)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Indian Institute of TechnologyIndoreIndia
  2. 2.Visvesvaraya National Institute of TechnologyNagpurIndia

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