Skip to main content
Log in

A multi-layer trust-based middleware framework for handling interoperability issues in heterogeneous IOTs

  • Published:
Cluster Computing Aims and scope Submit manuscript

Abstract

Traditional wireless technologies have evolutionary converged to Internet of Thing (IoT) for devices and service interactions. In the past decade, the academia, industry 4.0 and end-user interest has also grown drastically in IoT applications and their services. However, this increase in IoT services demand has witnessed a new challenge of seamless interaction among heterogeneous devices that are varied, diverse and dynamic in nature. In this connection, another challenge is tracing the footprint of these IoT interactions for trust-based interactions, that further becomes complex with the introduction of new applications. For instance, in many situations, IoT services are generally not self-contained and sufficient. They need to coordinate and interact with other IoT services held in the surroundings. The large-scale deployment of IoT based services is not conceivable without addressing interoperability and services coordination related challenges. In this regard, a comprehensive set of tools and techniques associated with IoT heterogeneity and interoperability have been explored. This article proposes a middleware framework to consider IoT heterogeneity and interoperability issues in different service interactions. Later, the article investigates experimentally, the trust measurements among IOTs, their decay, and the effectiveness of dynamic selection of trust parameters along with their thresholds. Appropriateness of time intervals have also been tested in various types of service interactions. To demonstrate middleware applicability, the trust-based algorithm has been applied in a service-oriented environment along with different types of services.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18

Similar content being viewed by others

References

  1. Zhang, P., Zheng Yan, A.V.V.: A security and trust framework for virtualized networks and software-defined networking. Secur. Commun. Netw. 5, 422–437 (2015)

    Article  Google Scholar 

  2. Pereira, C., Rocha, P., Santiago, F., Sousa, J.: IoT Interoperability for Actuating Applications through Standardised M2M Communications (2016)

  3. Ahmed, E., et al.: The role of big data analytics in Internet of Things. Comput. Networks 129(December), 459–471 (2017). https://doi.org/10.1016/j.comnet.2017.06.013

    Article  Google Scholar 

  4. Rahmani, A.M., Babaei, Z., Souri, A.: Event-driven IoT architecture for data analysis of reliable healthcare application using complex event processing. Cluster Comput. 0123456789, 1–14 (2020). https://doi.org/10.1007/s10586-020-03189-w

    Article  Google Scholar 

  5. Hizam, S.M., Ahmed, W.: A conceptual paper on SERVQUAL-framework for assessing quality of Internet of Things (IoT) services. Int. J. Financ. Res. 10(5), 387–397 (2019). https://doi.org/10.5430/ijfr.v10n5p387

    Article  Google Scholar 

  6. Sheng, Z., Yang, S., Yu, Y., Vasilakos, A., McCann, J., Leung, K.: A survey on the ietf protocol suite for the internet of things: standards, challenges, and opportunities. IEEE Wirel. Commun. 20(6), 91–98 (2013). https://doi.org/10.1109/MWC.2013.6704479

    Article  Google Scholar 

  7. Satam, P., Satam, S., Hariri, S., Alshawi, A.: Anomaly behavior analysis of IoT protocols. Model. Des. Secur. Internet Things. (2020). https://doi.org/10.1002/9781119593386.ch13

    Article  Google Scholar 

  8. Rehman, E., Sher, M., Naqvi, S.H.A., Badar Khan, K., Ullah, K.: Energy efficient secure trust based clustering algorithm for mobile wireless sensor network. J. Comput. Netw. Commun. (2017). https://doi.org/10.1155/2017/1630673

    Article  Google Scholar 

  9. Noura, M., Atiquzzaman, M., Gaedke, M.: Interoperability in Internet of Things: taxonomies and open challenges. Mob. Netw. Appl. 24(3), 796–809 (2019). https://doi.org/10.1007/s11036-018-1089-9

    Article  Google Scholar 

  10. Rizvi, N., Ramesh, D.: Fair budget constrained workflow scheduling approach for heterogeneous clouds. Clust. Comput. 23(4), 3185–3201 (2020). https://doi.org/10.1007/s10586-020-03079-1

    Article  Google Scholar 

  11. Forti, S., Ferrari, G.L., Brogi, A.: Secure cloud-edge deployments, with trust. Futur. Gener. Comput. Syst. 102, 775–788 (2020). https://doi.org/10.1016/j.future.2019.08.020

    Article  Google Scholar 

  12. Masdari, M., Khezri, H.: Efficient VM migrations using forecasting techniques in cloud computing: a comprehensive review. Clust. Comput. 23(4), 2629–2658 (2020). https://doi.org/10.1007/s10586-019-03032-x

    Article  Google Scholar 

  13. Truong, N.B., Lee, H., Askwith, B., Lee, G.M.: Toward a trust evaluation mechanism in the social Internet of Things. Sensors 17(6), 1346 (2017). https://doi.org/10.3390/s17061346

    Article  Google Scholar 

  14. Nitti, M., Girau, R., Atzori, L.: Trustworthiness management in the social internet of things. IEEE Trans. Knowl. Data Eng. 26(5), 1253–1266 (2014). https://doi.org/10.1109/TKDE.2013.105

    Article  Google Scholar 

  15. Alshehri, M., Hussain, F.K.: A centralized trust management mechanism for the internet of things a centralized trust management mechanism for the Internet of Things ( CTM-IoT). https://doi.org/10.1007/978-3-319-69811-3 (2018)

  16. Chen, Z., Tian, L., Lin, C.: Trust model of wireless sensor networks and its application in data fusion. Sensors 17(4), 703 (2017). https://doi.org/10.3390/s17040703

    Article  Google Scholar 

  17. Girau, R., Martis, S., Atzori, L.: Lysis: a platform for IoT distributed applications over socially connected objects. IEEE Internet Things J. 4(1), 40–51 (2017). https://doi.org/10.1109/JIOT.2016.2616022

    Article  Google Scholar 

  18. Chen, J., Tian, Z., Cui, X., Yin, L., Wang, X.: Trust architecture and reputation evaluation for internet of things. J. Ambient Intell. Humaniz. Comput. (2018). https://doi.org/10.1007/s12652-018-0887-z

    Article  Google Scholar 

  19. Fortino, G., Russo, W., Savaglio, C., Viroli, M., Zhou, M.: Modeling opportunistic IoT services in open IoT ecosystems. CEUR Workshop Proc. 1867, 90–95 (2017)

    Google Scholar 

  20. Souri, A., Rahmani, A.M., Navimipour, N.J., Rezaei, R.: A hybrid formal verification approach for QoS-aware multi-cloud service composition. Clust. Comput. 23(4), 2453–2470 (2020). https://doi.org/10.1007/s10586-019-03018-9

    Article  Google Scholar 

  21. Liu, X., Zhao, S., Liu, A., Xiong, N., Vasilakos, A.V.: Knowledge-aware Proactive Nodes Selection approach for energy management in Internet of Things. Futur. Gener. Comput. Syst. 92, 1142–1156 (2019). https://doi.org/10.1016/j.future.2017.07.022

    Article  Google Scholar 

  22. Bera, S., Misra, S., Vasilakos, A.V.: Software-defined networking for Internet of Things: a survey. IEEE Internet Things J. 4(6), 1994–2008 (2017). https://doi.org/10.1109/JIOT.2017.2746186

    Article  Google Scholar 

  23. Grace, P., Pickering, B., Surridge, M.: Model-driven interoperability: engineering heterogeneous IoT systems. Ann. Telecommun. 71(3–4), 141–150 (2015). https://doi.org/10.1007/s12243-015-0487-2

    Article  Google Scholar 

  24. Amaral, L.A., Tiburski, R.T., De Matos, E., Hessel, F.: Cooperative middleware platform as a service for Internet of Things Applications. In: Proceedings of the 30th Annual ACM Symposium on Applied Computing (2015)

  25. Boman, J., Taylor, J., Ngu, A.H.: Flexible IoT middleware for integration of things and applications. In: 10th IEEE International Conference on Collaborative Computing: Networking, Applications and Worksharing, pp. 481–488 (2014). https://doi.org/10.4108/icst.collaboratecom.2014.257533.

  26. Fersi, G.: Middleware for internet of things: a study. In: Proceedings of IEEE International Conference on Distributed Computing in Sensor Systems DCOSS 2015, pp. 230–235 (2015). https://doi.org/10.1109/DCOSS.2015.43

  27. Koo, J., Oh, S.R., Kim, Y.G.: Device identification interoperability in heterogeneous IoT platforms. Sensors 19(6), 1433 (2019). https://doi.org/10.3390/s19061433

    Article  Google Scholar 

  28. Chaturvedi, K., Kolbe, T.H.: Towards establishing cross-platform interoperability for sensors in smart cities. Sensors 19(3), 562 (2019). https://doi.org/10.3390/s19030562

    Article  Google Scholar 

  29. Derhamy, H., Eliasson, J., Delsing, J.: IoT interoperability—on-demand and low latency transparent multiprotocol translator. IEEE Internet Things J. 4(5), 1754–1763 (2017). https://doi.org/10.1109/JIOT.2017.2697718

    Article  Google Scholar 

  30. Shafagh, H., Burkhalter, L., Duquennoy, S.: Towards Blockchain-based auditable storage and sharing of IoT Data, pp. 25–30 (2017)

  31. Gravina, R., Palau, C., Manso, M., Liotta, A., Fortino, G.: Integration, interconnection, and interoperability of IoT systems (2017)

  32. Desai, P., Sheth, A., Anantharam, P.: Semantic gateway as a service architecture for IoT interoperability. In: Proceedings of the 2015 IEEE 3rd International Conference on Mobile Services, pp. 313–319 (2015) https://doi.org/10.1109/MobServ.2015.51

  33. Bouloukakis, G., Georgantas, N., Ntumba, P., Issarny, V.: Automated synthesis of mediators for middleware-layer protocol interoperability in the IoT. Futur. Gener. Comput. Syst. 101, 1271–1294 (2019). https://doi.org/10.1016/j.future.2019.05.064

    Article  Google Scholar 

  34. Pan, Z., Hariri, S., Pacheco, J.: Context aware intrusion detection for building automation systems. Comput. Secur. 85, 181–201 (2019). https://doi.org/10.1016/j.cose.2019.04.011

    Article  Google Scholar 

  35. Chen, N., Yang, Y., Li, J., Zhang, T.: A Fog-based service enablement architecture for cross-domain IoT applications. In: 2017 IEEE Fog World Congr. FWC 2017, pp. 1–6 (2018)https://doi.org/10.1109/FWC.2017.8368533

  36. Fernandez-Gago, C., Moyano, F., Lopez, J.: Modelling trust dynamics in the Internet of Things. Inf. Sci. 396, 72–82 (2017). https://doi.org/10.1016/j.ins.2017.02.039

    Article  Google Scholar 

  37. Alce, G., Espinoza, A., Hartzell, T., Olsson, S., Samuelsson, D., Wallergård, M.: UbiCompass: AN IoT interaction concept (2018)

  38. Andrei, A., Siobhan, S., Razzaque, M.A., Milojevic-jevric, M., Palade, A.: Research repository middleware for Internet of Things: a survey middleware for Internet of Things: a survey (2018)

  39. Gyrard, A., Datta, S.K., Bonnet, C., Boudaoud, K., Cross-domain Internet of Things application development: M3 framework and evaluation. In: Proceedings of the 2015 International Conference on Future Internet Things Cloud, FiCloud 2015, 2015 International Conference on Open Big Data, OBD 2015, pp. 9–16 (2015) https://doi.org/10.1109/FiCloud.2015.10

  40. Lelli, F.: Interoperability of the time of industry 4.0 and the Internet of Things. Futur. Internet 11(2), 36 (2019). https://doi.org/10.3390/fi11020036

    Article  MathSciNet  Google Scholar 

  41. Al-fuqaha, A., Member, S., Guizani, M., Mohammadi, M., Member, S.: Internet of Things: a survey on enabling technologies, protocols, and applications. IEEE Commun. Surv. Tutor. 17(4), 2347–2376 (2015)

    Article  Google Scholar 

  42. Eliasson, J., Delsing, J., Derhamy, H., Salcic, Z., Wang, K.: Towards industrial Internet of Things: an efficient and interoperable communication framework, pp. 2198–2204 (2015)

  43. Tiburski, T., Amaral, L.A., De Matos, E.: The importance of a standard security architecture for SOA-based IoT middleware, pp. 1–10 (2015)

  44. Nilsson, J., Sandin, F., Delsing, J.: Interoperability and machine-to-machine translation model with mappings to machine learning tasks. IEEE Int. Conf. Ind. Informatics, vol. 2019-July, pp. 284–289 (2019) https://doi.org/10.1109/INDIN41052.2019.8972085

  45. Jarwar, M.A., Ali, S., Kibria, M.G., Kumar, S., Chong, I.: Exploiting interoperable microservices in web objects enabled Internet of Things. In: International Conference on Ubiquitous Future Networks, ICUFN, pp. 49–54 (2017). https://doi.org/10.1109/ICUFN.2017.7993746

  46. Soursos, S., Žarko, IP., Zwickl, P., Gojmerac, I., Bianchi, G., Carrozzo, G.: Towards the cross-domain interoperability of IoT platforms, pp. 1–5 (2016)

  47. Bär, S., Reinhold, O., Alt, R.: The role of cross-domain use cases in IoT—a case analysis. In: Proc. 52nd Hawaii International Conference on System and Sciences, vol. 6, pp. 390–399 (2019). https://doi.org/10.24251/hicss.2019.047

  48. da Cruz, M.A.A., Rodrigues, J.J.P.C., Lorenz, P., Solic, P., Al-Muhtadi, J., Albuquerque, V.H.C.: A proposal for bridging application layer protocols to HTTP on IoT solutions. Futur. Gener. Comput. Syst. 97, 145–152 (2019). https://doi.org/10.1016/j.future.2019.02.009

    Article  Google Scholar 

  49. Khaled, A.E., Helal, S.: Interoperable communication framework for bridging RESTful and topic-based communication in IoT. Futur. Gener. Comput. Syst. 92, 628–643 (2019). https://doi.org/10.1016/j.future.2017.12.042

    Article  Google Scholar 

  50. Nasri, F., Mtibaa, A.: IoT platform for healthcare system: protocols interoperability. Int. J. Appl. Eng. Res. 12(22), 12510–12518 (2017)

    Google Scholar 

  51. Wollschlaeger, M., Sauter, T., Jasperneite, J.: The future of industrial communication: automation networks in the era of the internet of things and industry 4.0. IEEE Ind. Electron. Mag. 11(1), 17–27 (2017). https://doi.org/10.1109/MIE.2017.2649104

    Article  Google Scholar 

  52. Zhu, Q., Wang, R., Chen, Q., Liu, Y., Qin, W.: IOT gateway: BridgingWireless sensor networks into Internet of Things. In: 2010 IEEE/IFIP International Conference on Embedded Ubiquitous Computing, pp. 347–352 (2010) https://doi.org/10.1109/EUC.2010.58

  53. Lin, C., He, D., Huang, X., Choo, K.K.R., Vasilakos, A.V.: BSeIn: A blockchain-based secure mutual authentication with fine-grained access control system for industry 4.0. J. Netw. Comput. Appl. 116(May), 42–52 (2018). https://doi.org/10.1016/j.jnca.2018.05.005

    Article  Google Scholar 

  54. Wazid, M., Das, A.K., Bhat, V., Vasilakos, A.V.: LAM-CIoT: lightweight authentication mechanism in cloud-based IoT environment. J. Netw. Comput. Appl. 150, 102496 (2020). https://doi.org/10.1016/j.jnca.2019.102496

    Article  Google Scholar 

  55. Zhou, A.V.V.J., Cao, Z., Dong, X.: Security and privacy for cloud-based IoT: challenges, countermeasures, and future directions. IEEE Cloud Comput. 55(1), 26–33 (2017)

    Google Scholar 

  56. Porambage, P., Ylianttila, M., Schmitt, C., Kumar, P., Gurtov, A., Vasilakos, A.V.: The quest for privacy in the Internet of Things. IEEE Cloud Comput. 3(2), 36–45 (2016). https://doi.org/10.1109/MCC.2016.28

    Article  Google Scholar 

  57. Jing, Q., Vasilakos, A.V., Wan, J., Lu, J., Qiu, D.: Security of the Internet of Things: perspectives and challenges. Wirel. Netw. 20(8), 2481–2501 (2014). https://doi.org/10.1007/s11276-014-0761-7

    Article  Google Scholar 

  58. Hoogendoorn, M., Klein, M.C.A., Memon, Z.A., Treur, J.: Formal specification and analysis of intelligent agents for model-based medicine usage management. Comput. Biol. Med. 43(5), 444–457 (2013). https://doi.org/10.1016/j.compbiomed.2013.01.021

    Article  Google Scholar 

  59. Memon, Z., Treur, J.: Cognitive and biological agent models for emotion reading. In: Proceedings of the 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology, vol. 02, pp. 308-313 (2008). https://doi.org/10.1109/WIIAT.2008.311

    Article  Google Scholar 

  60. Memon, Z.A., Treur, J.: An agent model for cognitive and affective empathic understanding of other agents. Trans Comput. Collectiv. Intell. (TCCI) 6, 56–83 (2012). https://doi.org/10.1007/978-3-642-29356-6_3

    Article  Google Scholar 

  61. Memon, Z.A., Treur, J.: On the reciprocal interaction between believing and feeling: an adaptive agent modelling perspective. Cognit. Neurodyn. J. 4(4), 377–394 (2010). https://doi.org/10.1007/s11571-010-9136-7

    Article  Google Scholar 

  62. Kashif, L., Memon, Z.A.: Scavenge: an intelligent multi-agent based voice-enabled virtual assistant for LMS. Interact. Learn. Environ. NILE. (2019). https://doi.org/10.1080/10494820.2019.1614634

    Article  Google Scholar 

  63. Laeeq, K., Memon, Z.A.: Strengthening virtual learning environments by incorporating modern technologies. In: Intelligent Computing-Proceedings of the Computing Conference, pp. 994–1008. Springer, Cham (2019)

    Google Scholar 

  64. Asad, A., Memon, Z.A., Durrani, M.: An interoperable trust-based middleware for heterogeneous IoT. Front. Inf. Technol. Electron. Eng. (2020). https://doi.org/10.1631/FITEE.2000084

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Zulfiqar A. Memon.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Abbasi, M.A., Memon, Z.A., Durrani, N.M. et al. A multi-layer trust-based middleware framework for handling interoperability issues in heterogeneous IOTs. Cluster Comput 24, 2133–2160 (2021). https://doi.org/10.1007/s10586-021-03243-1

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10586-021-03243-1

Keywords

Navigation