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World Wide Web

, Volume 21, Issue 1, pp 185–200 | Cite as

Trustworthy service composition with secure data transmission in sensor networks

  • Tao Zhang
  • Lele Zheng
  • Yongzhi Wang
  • Yulong Shen
  • Ning Xi
  • Jianfeng Ma
  • Jianming Yong
Article
  • 310 Downloads
Part of the following topical collections:
  1. Special Issue on Security and Privacy of IoT

Abstract

As the basis of the Internet of Things (IoT), sensor networks have materialized its computation and communication capability into anything in our modern lives. Service composition provides us a promising way to cooperate various sensors to build more powerful IoT applications over sensor networks. However, the limited capability of sensor node poses great challenges not only to trustworthy service composition but also to secure data aggregation. The complex composite structure, computation-intensive evaluation, and massive data transmission become burdens for service composition in sensor networks. To overcome these issues, this paper proposes a distributed approach to enable efficient trustworthy service composition with secure data transmission in sensor networks. By analyzing dependency relationships, the rules for computing service trust and data trust are proposed based a multi-level trust model. Then, each target component service can be evaluated independently through a model checker. Moreover, an identity-based aggregate signature is introduced in the composite evaluation to guarantee the secure data transmission among different components. The experimental results show that our approach not only achieves efficient trustworthy service composition with complex invocation structures, but also reduces the costs in the secure data transmission.

Keywords

Service composition Data transmission Trust Security Sensor networks 

Notes

Acknowledgements

We wish to thank the anonymous reviewers for their highly valuable and constructive comments. This paper is supported by the National Natural Science Foundation of China (No. 61602365, U1536202, 61571352, 61373173 and 61602364), the Open Fund of the Chinese Key Laboratory of the Grain Information Processing and Control (No. KFJJ-2015-202), and the Fundamental Research Funds for the Central Universities (No. XJS15075).

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Copyright information

© Springer Science+Business Media New York 2017

Authors and Affiliations

  • Tao Zhang
    • 1
  • Lele Zheng
    • 1
  • Yongzhi Wang
    • 1
  • Yulong Shen
    • 1
  • Ning Xi
    • 1
  • Jianfeng Ma
    • 1
  • Jianming Yong
    • 2
  1. 1.School of Computer Science and TechnologyXidian UniversityXidianChina
  2. 2.School of Management and EnterpriseUniversity of Southern QueenslandToowoombaAustralia

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