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Towards trusted and energy-efficient data collection in unattended wireless sensor networks


The Internet of Things (IoT), including wireless sensors, is one of the highly anticipated contributors to big data; therefore, avoiding misleading or forged data gathering in cases of sensitive and critical data through secure communication is vital. Wireless sensor networks are relatively simple, scalable networks with many applications in research. They can provide many benefits, including ad hoc distribution, lower costs, and higher flexibility. In a scenario where time is of the essence and dedicated base stations cannot be established, mobile sinks must be used to gather data. IoT systems are based on a collective organization in which devices collaborate to provide better and more accurate decisions. It is important to ensure that the information being shared is legitimate to avoid any significant degradation in system performance because of false or inaccurate information. Building trust—the “assurance” between two devices that the information being shared can be used with confidence that it is accurate—will create a trustworthy, secure system in which all devices are identified and no information is accepted from any unauthorized device. The key contribution of this work is a new, dynamic, trust-based clustering mechanism by which nodes can securely connect to one another and begin transmitting data to a sink while it is available. To demonstrate the utility of this mechanism, we examine two possible attacks on a trust-based network and present a heuristic solution for minimizing the negative effects of such attacks in an energy-efficient way. Our results show improved network performance through reduction of the number of cycles required to isolate or mitigate the effect of malicious nodes in the network, thus reducing the energy consumption in the network with a concomitant increase in its lifespan. Our cluster methodology also has the effect of spreading energy consumption among nodes, thereby reducing early fall-off of nodes and network holes.

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  1. Destroying the trust is carried through malicious node(s) by setting the trust value to zero.

  2. Note that authenticating the mobile sink in the network is beyond the scope of this paper.

  3. Adjusting sensors transmission power to reduce energy consumption is addressed in the literature [31,32,33].

  4. CM’s broadcast beacons to their neighbors; when a sufficient number of CM’s are no longer transmitting, then the sensor network goes into an inoperative state.


  1. Stankovic, J. A. (2008). Wireless sensor networks. IEEE Computer, 41(10), 92–95.

    Article  Google Scholar 

  2. Lin, J., Jung, H., Chang, Y. J., Jung, J. W., & Weitnauer, M. A. (2015). On cooperative transmission range extension in multi-hop wireless ad-hoc and sensor networks: A review. Ad Hoc Networks, 29, 117–134.

    Article  Google Scholar 

  3. Yaacoub, E., & Abu-Dayya, A. (2012). Multihop routing for energy efficiency in wireless sensor networks. In M. A. Matin (Ed.), Wireless sensor networks—Technology and protocols, chapter 08. Rijeka: InTech.

    Google Scholar 

  4. Wang, W., Srinivasan, V., & Chua K. -C. (2005). Using mobile relays to prolong the lifetime of wireless sensor networks. In Proceedings of the 11th Annual International Conference on Mobile Computing and Networking (pp. 270–283). ACM.

  5. Liang, J., Liu, M., & Kui, X. (2014). A survey of coverage problems in wireless sensor networks. Sensors & Transducers, 163(1), 240–246.

    Google Scholar 

  6. Mainwaring, A., Culler, D., Polastre, J., Szewczyk, R., & Anderson, J. (2002). Wireless sensor networks for habitat monitoring. In Proceedings of the 1st ACM International Workshop on Wireless Sensor Networks and Applications, WSNA ’02 (pp. 88–97). ACM, New York, NY, USA.

  7. Ahmed, N., Kanhere, S. S., & Jha, S. (2005). The holes problem in wireless sensor networks: A survey. SIGMOBILE Mobile Computing and Communications Review, 9(2), 4–18.

    Article  Google Scholar 

  8. Butun, I., Morgera, S. D., & Sankar, R. (2014). A survey of intrusion detection systems in wireless sensor networks. IEEE Communications Surveys & Tutorials, 16(1), 266–282.

    Article  Google Scholar 

  9. Patil, H. K., & Chen, T. M. (2017). Chapter 18—Wireless sensor network security: The internet of things. In J. R. Vacca (Ed.), Computer and Information Security Handbook (Third ed., pp. 317–337). Boston: Morgan Kaufmann.

    Chapter  Google Scholar 

  10. Chen, Y., & Yang, J. (2012). Chapter 8—Defending against identity-based attacks in wireless networks. In S. K. Das, K. Kant, & N. Zhang (Eds.), Handbook on Securing Cyber-Physical Critical Infrastructure (pp. 191–222). Boston: Morgan Kaufmann.

    Chapter  Google Scholar 

  11. Ho, J.-W., & Das, S. K. (2012). Chapter 11—Node compromise detection in wireless sensor networks. In S. K. Das, K. Kant, & N. Zhang (Eds.), Handbook on Securing Cyber-Physical Critical Infrastructure (pp. 281–300). Boston: Morgan Kaufmann.

    Chapter  Google Scholar 

  12. Kandah, F., Cancelleri, J., Reising, D., Altarawneh, A., & Skjellum, A. (2019). A hardware-software codesign approach to identity, trust, and resilience for iot/cps at scale. In 2019 International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData) (pp. 1125–1134).

  13. Kandah, F., Huber, B., Altarawneh, A., Medury, S., & Skjellum, A. (2019). BLAST: Blockchain-based trust management in smart cities and connected vehicles setup. In 2019 IEEE High Performance Extreme Computing Conference (HPEC) (pp. 1–7).

  14. Momani, M., & Challa, S. (2010). Survey of trust models in different network domains. CoRR, abs/1010.0168.

  15. Jiang, J., Han, G., Wang, F., Shu, L., & Guizani, M. (2014). An efficient distributed trust model for wireless sensor networks. IEEE Transactions on Parallel and Distributed Systems, PP(99), 1–1.

    Google Scholar 

  16. Feng, R., Xu, X., Zhou, X., & Wan, J. (2011). A trust evaluation algorithm for wireless sensor networks based on node behaviors and d-s evidence theory. Sensors, 11, 1345–1360.

    Article  Google Scholar 

  17. Zhan, G., Shi, W., & Deng, J. (2012). Design and implementation of tarf: A trust-aware routing framework for wsns. IEEE Transactions on Dependable and Secure Computing, 9(2), 184–197.

    Article  Google Scholar 

  18. Ren, Y., Zadorozhny, V. I., Oleshchuk, V. A., & Li, F. Y. (2014). A novel approach to trust management in unattended wireless sensor networks. IEEE Transactions on Mobile Computing, 13(7), 1409–1423.

    Article  Google Scholar 

  19. Kalpakis, K., Dasgupta, K., & Namjoshi, P. (2002). Maximum lifetime data gathering and aggregation in wireless sensor networks. In IEEE International Conference on Networking (pp. 685–696).

  20. Lin, K., Lai, C.-F., Liu, X., & Guan, X. (2012). Energy efficiency routing with node compromised resistance in wireless sensor networks. Mobile Networks and Applications, 17(1), 75–89.

    Article  Google Scholar 

  21. Rasheed, A., & Mahapatra, R. N. (2012). The three-tier security scheme in wireless sensor networks with mobile sinks. IEEE Transactions on Parallel and Distributed Systems, 23(5), 958–965.

    Article  Google Scholar 

  22. Crosby, G. V., Pissinou, N., & Gadze, J. (2006). A framework for trust-based cluster head election in wireless sensor networks. In Second IEEE Workshop on Dependability and Security in Sensor Networks and Systems (pp. 10–22).

  23. Pissinou, N., & Crosby, G. V. (2007). Cluster-based reputation and trust for wireless sensor networks. In 2007 4th IEEE Consumer Communications and Networking Conference (pp. 604–608).

  24. Heinzelman, W. B., Chandrakasan, A. P., & Balakrishnan, H. (2002). An application-specific protocol architecture for wireless microsensor networks. IEEE Transactions on Wireless Communications, 1(4), 660–670.

    Article  Google Scholar 

  25. Virrankoski, R., & Savvidees, A. (2005). Tasc: Topology adaptive spatial clustering for sensor networks. In IEEE International Conference on Mobile Adhoc and Sensor Systems Conference, 2005 (pp. 10–614).

  26. Manisekaran, S. V., Akila, I. S., & Venkatesan, R. (2017). Modern clustering techniques in wireless sensor networks. London: IntechOpen.

    Google Scholar 

  27. Sambo, Damien Wohwe, Yenke, Blaise Omer, Förster, Anna, & Dayang, Paul. (2019). Optimized clustering algorithms for large wireless sensor networks: A review. Sensors, 19(2), 322.

    Article  Google Scholar 

  28. Manjeshwar, A., & Agrawal, D. P. (2001). Teen: A routing protocol for enhanced efficiency in wireless sensor networks. In Proceedings 15th International Parallel and Distributed Processing Symposium. IPDPS 2001 (pp. 2009–2015).

  29. Ma, Y., Guo, Y., Tian, X., & Ghanem, M. M. (2011). Distributed clustering-based aggregation algorithm for spatial correlated sensor networks. IEEE Sensors Journal, 11, 641–648.

    Article  Google Scholar 

  30. Ghosh, N., & Banerjee, I. (2018). Application of mobile sink in wireless sensor networks. In 2018 10th International Conference on Communication Systems Networks (COMSNETS) (pp. 507–509).

  31. Chen, X., & Rowe, N. C. (2011). Saving energy by adjusting transmission power in wireless sensor networks. In 2011 IEEE Global Telecommunications Conference—GLOBECOM 2011 (pp. 1–5).

  32. Elshrkawey, M., Elsherif, S. M., & Wahed, M. E. (2018). An enhancement approach for reducing the energy consumption in wireless sensor networks. Journal of King Saud University—Computer and Information Sciences, 30(2), 259–267.

    Article  Google Scholar 

  33. Lee, C. J., Jung, J. Y., & Lee, J. R. (2017). Bio-inspired distributed transmission power control considering QoS fairness in wireless body area sensor networks. Sensors, 17(10), 2344.

    Article  Google Scholar 

  34. The Network Simulator—ns-2.

  35. Akyildiz, I. F., Weilian, S., Sankarasubramaniam, Y., & Cayirci, E. (2002). Wireless sensor networks: A survey. Computer Networks, 38(4), 393–422.

    Article  Google Scholar 

  36. Jung, S., Han, Y., & Chung, T. (2007). The concentric clustering scheme for efficient energy consumption in the pegasis. In The 9th International Conference on Advanced Communication Technology (vol. 1, pp. 260–265).

  37. Wei, D., Jin, Y., Vural, S., Moessner, K., & Tafazolli, R. (2011). An energy-efficient clustering solution for wireless sensor networks. IEEE Transactions on Wireless Communications, 10, 3973–3983.

    Article  Google Scholar 

  38. Ren, P., Qian, J., Li, L., Zhao, Z., & Li, X. (2010). Unequal clustering scheme based leach for wireless sensor networks. In 2010 Fourth International Conference on Genetic and Evolutionary Computing (pp. 90–93).

  39. Gupta, I., Riordan, D., & Sampalli, S. (2005). Cluster-head election using fuzzy logic for wireless sensor networks. In 3rd Annual Communication Networks and Services Research Conference (CNSR’05) (pp. 255–260).

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Correspondence to Farah Kandah.

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The authors acknowledge the support of the University of Tennessee at Chattanooga. Research reported in this publication was supported by the 2020 Center of Excellence for Applied Computational Science and Engineering competition.

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Kandah, F., Whitehead, J. & Ball, P. Towards trusted and energy-efficient data collection in unattended wireless sensor networks. Wireless Netw 26, 5455–5471 (2020).

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