TARE: Topology Adaptive Re-kEying scheme for secure group communication in IoT networks

  • Anshul Anand
  • Mauro Conti
  • Pallavi Kaliyar
  • Chhagan LalEmail author


Internet of Things (IoT) employs a large number of sensors and actuators to collect and act upon data for its smart functionalities. These devices are considered as a part of the Low-power and Lossy Networks due to their use of low power embedded hardware and computationally constrained nature. For synchronization and utility, these devices are often clubbed together logically to form groups. To maintain data confidentiality within a group, a shared symmetric key called the Group Key (GK) is used by all the group members. The GK must be redistributed upon joining and leaving of a group member to maintain forward and backward secrecy. However, the key management (i.e., generation and distribution) process causes overhead which consumes the scarce network resources. In this paper, we propose TARE, a novel Topology Adaptive Re-kEying (TARE) scheme for lightweight and secure group communication. TARE integrates the principles of routing tree mapped logical key tree and local derivation of the key over the an IPv6 Routing Protocol for low-power and Lossy networks in an original way. TARE takes into consideration the current routing topology and makes maximum energy reduction as the premise for its choice of key derivation and distribution methods, thus, it reduces the network energy consumption while maintaining key secrecy and data confidentiality. In particular, TARE provides the following advantages: (1) lower network overhead and bandwidth utilization in key management and re-distribution operations, (2) effective against the network mobility in scalable IoT networks, (3) secure group communications in network against attacks such as man-in-the-middle and eavesdropping, and (4) data confidentiality by ensuring backward and forward secrecy in key distribution method. We evaluate the performance of TARE and compare it with existing schemes. Our results show the effectiveness of TARE regarding energy consumption, bandwidth utilization, and the number of encrypted message transmissions during the re-keying operations.


Internet of Things Wireless Sensor Nodes Group key management Group communication RPL Multicast routing 



Mauro Conti is supported by a Marie Curie Fellowship funded by the European Commission (Agreement PCIG11-GA-2012-321980). This work is also partially supported by the EU TagItSmart! Project (Agreement H2020-ICT30-2015-688061), and by the projects “Physical-Layer Security for Wireless Communication”, and “Content Centric Networking: Security and Privacy Issues” funded by the University of Padua.


  1. 1.
    Conti, M., Kaliyar, P., & Lal, C. (2017). REMI: A reliable and secure multicast routing protocol for IoT networks. In Proceedings of the 12th international conference on availability, reliability and security, ser. ARES ’17 (pp. 84:1–84:8). ACM.Google Scholar
  2. 2.
    Kaur, N., & Sood, S. K. (2017). An energy-efficient architecture for the internet of things (IoT). IEEE Systems Journal, 11(2), 796–805.CrossRefGoogle Scholar
  3. 3.
    Chin, W. L., Li, W., & Chen, H. H. (2017). Energy big data security threats in IoT-based smart grid communications. IEEE Communications Magazine, 55(10), 70–75.CrossRefGoogle Scholar
  4. 4.
    Matsemela, G., Rimer, S., Ouahada, K., Ndjiongue, R., & Mngomezulu, Z. (2017). Internet of things data integrity. In 2017 IST-Africa week conference (IST-Africa) (pp. 1–9).Google Scholar
  5. 5.
    Schukat, M., & Cortijo, P. (2015). Public key infrastructures and digital certificates for the internet of things. In 2015 26th Irish signals and systems conference (ISSC) (pp. 1–5).Google Scholar
  6. 6.
    Tiloca, M., Nikitin, K., & Raza, S. (2017). Axiom: DTLS-based secure IoT group communication. ACM Transactions on Embedded Computing Systems, 16(3), 66:1–66:29.CrossRefGoogle Scholar
  7. 7.
    Rafaeli, S., & Hutchison, D. (2003). A survey of key management for secure group communication. ACM Computing Surveys, 35(3), 309–329. Scholar
  8. 8.
    Wong, C. K., Gouda, M., & Lam, S. S. (2000). Secure group communications using key graphs. IEEE/ACM Transactions on Networking, 8(1), 16–30.CrossRefGoogle Scholar
  9. 9.
    Dini, G., & Savino, I. M. (2011). LARK: A lightweight authenticated rekeying scheme for clustered wireless sensor networks. ACM Transactions on Embedded Computing Systems, 10(4), 41:1–41:35.CrossRefGoogle Scholar
  10. 10.
    Lin, J.-C., Lai, F., & Lee, H.-C. (2005). Efficient group key management protocol with one-way key derivation. In The IEEE conference on local computer networks 30th anniversary (LCN’05)l (pp. 336–343).Google Scholar
  11. 11.
    Ghafoor, A., Sher, M., Imran, M., & Saleem, K. (2015). A lightweight key freshness scheme for wireless sensor networks. In 2015 12th international conference on information technology—New generations (pp. 169–173).Google Scholar
  12. 12.
    Pietro, R. D., Mancini, L. V., Law, Y. W., Etalle, S., & Havinga, P. (2003). LKHW: A directed diffusion-based secure multicast scheme for wireless sensor networks. In 2003 international conference on parallel processing workshops, 2003. Proceedings (pp. 397–406).Google Scholar
  13. 13.
    Lazos, L., & Poovendran, R. (2003). Energy-aware secure multicast communication in ad-hoc networks using geographic location information. In 2003 IEEE international conference on acoustics, speech, and signal processing, 2003. Proceedings. (ICASSP ’03) (Vol. 4, pp. IV–201–4).Google Scholar
  14. 14.
    Son, J.-H., Lee, J.-S., & Seo, S.-W. (2009). Topological key hierarchy for energy-efficient group key management in wireless sensor networks. Wireless Personal Communications, 52(2), 359.CrossRefGoogle Scholar
  15. 15.
    Romdhani, I., Al-Dubai, A., Qasem, M., Thomson, C., Ghaleb, B., & Wadhaj, I. (2016). Cooja simulator manual. Technical Report [Online]. Accessed 15 July 2016.
  16. 16.
    Klaoudatou, E., Konstantinou, E., Kambourakis, G., & Gritzalis, S. (2011). A survey on cluster-based group key agreement protocols for WSNs. IEEE Communications Surveys Tutorials, 13(3), 429–442.CrossRefGoogle Scholar
  17. 17.
    Gandino, F., Ferrero, R., & Rebaudengo, M. (2017). A key distribution scheme for mobile wireless sensor networks: \(q-s\) -composite. IEEE Transactions on Information Forensics and Security, 12(1), 34–47.CrossRefGoogle Scholar
  18. 18.
    Halford, T. R., Courtade, T. A., Chugg, K. M., Li, X., & Thatte, G. (2015). Energy-efficient group key agreement for wireless networks. IEEE Transactions on Wireless Communications, 14(10), 5552–5564.CrossRefGoogle Scholar
  19. 19.
    Zheng, X., Wang, H., Chen, Y., Liu, H., & Liu, R. (2010). A decentralized key management scheme via neighborhood prediction in mobile wireless networks. In The 7th IEEE international conference on mobile ad-hoc and sensor systems (IEEE MASS 2010) (pp. 51–60).Google Scholar
  20. 20.
    Cheikhrouhou, O., Koubâa, A., Dini, G., & Abid, M. (2011). RiSeG: A ring based secure group communication protocol for resource-constrained wireless sensor networks. Personal and Ubiquitous Computing, 15, 783–797.CrossRefGoogle Scholar
  21. 21.
    Ganesan, V. C., Periyakaruppan, A., & Lavanya, R. (2016). Cost-effective polynomial-based multicast-unicast key distribution framework for secure group communication in IPv6 multicast networks. IET Information Security, 10(5), 252–261.CrossRefGoogle Scholar
  22. 22.
    Visconti, A., Bossi, S., Ragab, H., & Calò, A. (2016). On the weaknesses of pbkdf2. IACR Cryptology ePrint Archive, 2016, 273.Google Scholar
  23. 23.
    Shelby, Z., Chakrabarti, S., & Nordmark, E. (2010). Neighbor discovery optimization for IPv6 over low-power wireless personal area networks (6LoWPANs). [Online]. Accessed 21 Nov 2012.
  24. 24.
    Oikonomouoi, G., & Phillips, I. (2012). Stateless multicast forwarding with RPL in 6LowPAN sensor networks. In IEEE international conference on pervasive computing and communications workshops, (PERCOM Workshops) (pp. 272–277).Google Scholar
  25. 25.
    Kim, H. S., Ko, J., Culler, D. E., & Paek, J. (2017). Challenging the ipv6 routing protocol for low-power and lossy networks (rpl): A survey. IEEE Communications Surveys Tutorials, 19(4), 2502–2525.CrossRefGoogle Scholar
  26. 26.
    Dunkels, A. (2013). Contiki 2.7 [Online]. Accessed 15 Nov 2013.
  27. 27.
    Velinov, A., & Mileva, A. (2016). Running and testing applications for contiki OS using cooja simulator. In International conference on information technology and development of education – ITRO 2016, Zrenjanin, Republic of Serbia.Google Scholar
  28. 28.
    Dunkels, A., Osterlind, F., Tsiftes, N., & He, Z. (2007). Software-based on-line energy estimation for sensor nodes. In Proceedings of the 4th workshop on embedded networked sensors (pp. 28–32). ACM.Google Scholar
  29. 29.
    MSP-EXP430F5438 Experimenter Board, Texas Instruments, 11 2013, rev. 1. [Online].
  30. 30.
    CC2420 2.4 GHz IEEE 802.15.4 / ZigBee-ready RF Transceiver, Texas Instruments. [Online].

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Information TechnologyIndian Institute of Information TechnologyAllahabadIndia
  2. 2.Department of MathematicsUniversity of PadovaPaduaItaly
  3. 3.Department of Computer EngineeringManipal University JaipurJaipurIndia

Personalised recommendations