Abstract
Distributed Wireless Sensor Networks (DWSNs) comprised of set of sensor nodes that are geographically distributed in harsh environments. However, a centralized WSN does not consider the network scalability and also leads to high energy consumption. Due to these open issues, distributed computing approach is considered since it resolves scalability issue and several paths are established for data transmission. To achieve higher energy efficiency, scalability and security, in this paper we propose a distributed protocol called Secure Coronas-Based Zone Clustering and Routing (SC-ZCR). The proposed SC-ZCR aim at addressing the number of issues in DWSN and it support for long-term deployment. In SC-ZCR, we will pursue several processes including Zone Clustering, Energy Efficient Routing and Data Encryption and Security Verification. Zone clustering is carried out using Adaptive Neuro-Fuzzy System, where we consider four parameters: node angle, distance between sensor node to the sink node, node residual energy and belief value. Belief value of each sensor node is computed using Principal Component Analysis. Then energy efficient routing is established by Q-Hop Routing Protocol, which finds optimum and shortest path using Whale Optimization Algorithm. For data packets encryption, RC6 is used and then security level of data packets are verified using potential weight factor \(\delta\), which is computed using key size \(K\), block size \(B\), and number of rounds \(R\). Experiments conducted using NS3.26 simulator and the simulation result show that the proposed SC-ZCR outperforms in terms of Coverage Ratio, Residual Energy, Network Lifetime, Delay, Packet Drop Rate, and Security Strength.
Similar content being viewed by others
References
Salim, A., & Osamy, W. (2015). Distributed multi-chain compressive sensing based routing algorithm for wireless sensor networks. Wireless Networks,21(4), 1379–1390.
Zhao, Z., Kaida, X., Hui, G., & Liqin, H. (2018). An energy efficient clustering routing protocol for wireless sensor networks based on AGNES with balanced energy consumption optimization. Sensors,18(11), 1–27.
Zaman, N., Tang Jung, L., & Yasin, M. M. (2016). Enhancing energy efficiency of wireless sensor network through the design of energy efficient routing protocol. Journal of Sensors,2016, 1–16.
Arapoglu, O., Akram, V. K., & Dagdeviren, O. (2019). An energy-efficient, self-stabilizing and distributed algorithm for maximal independent set construction in wireless sensor networks. Computer Standards and Interfaces,62, 32–42.
Al-Baz, A., & El-Sayed, A. (2017). A new algorithm for cluster head selection in LEACH protocol for wireless sensor networks. International Journal of Communication Systems,31(1), 1–13.
Kumar, A., Ilango, P., & Dinesh, G. H. (2016). A modified LEACH protocol for increasing lifetime of the wireless sensor network. Cybernetics and Information Technologies,16(3), 154–164.
Behera, T. M., Mohapatra, S. K., Samal, U. C., Khan, M. S., Daneshmand, M., Gandomi, A. H. (2019). Residual energy based cluster-head selection in WSNs for IoT application (pp. 1–8).
Kang, J., Sohn, I., & Lee, S. H. (2019). Enhanced message-passing based LEACH protocol for wireless sensor networks. Sensors,19(1), 1–17.
Huang, J., Zhao, Z., Yuan, Y., & Hong, Y. (2017). Multi-factor and distributed clustering routing protocol in wireless sensor networks. Wireless Personal Communications,95(3), 2127–2142.
Nokhanji, N., Hanapi, Z. M., Subramaniam, S., & Mohammed, M. A. (2015). An energy aware distributed clustering algorithm using fuzzy logic for wireless sensor networks with non-uniform node distribution. Wireless Personal Communications,84(1), 395–419.
Dohare, U., Lobiyal, D. K., & Kumar, S. (2014). Energy balanced model for lifetime maximization in randomly distributed wireless sensor networks. Wireless Personal Communications,78(1), 407–428.
Chen, D.-R., Chen, L.-C., Chen, M.-Y., & Hsu, M.-Y. (2019). A coverage-aware and energy-efficient protocol for the distributed wireless sensor networks. Computer Communications,137, 15–31.
Lakshmi, N. V. S. S. R., Babu, S., & Bhalaji, N. (2019). Analysis of clustered QoS routing protocol for distributed wireless sensor network. Computers and Electrical Engineering,64, 173–181.
Liu, Y., & Wu, Y. (2019). A novel sub-regional key distribution scheme for distributed wireless sensor networks. International Journal of Wireless Information Networks,26, 1–6.
Das, A. K. (2015). A secure and efficient user anonymity preserving three-factor authentication protocol for large-scale distributed wireless sensor networks. Wireless Personal Communications,82(3), 1344–1404.
Nisha, U. B., Maheswari, N. U., Venkatesh, R., & Abdullah, R. Y. (2016). Fuzzy-based flat anomaly diagnosis and relief measures in distributed wireless sensor networks. International Journal of Fuzzy Systems,19(5), 1–18.
Kim, H., & Han, S.-w. (2015). An efficient sensor deployment scheme for large-scale wireless sensor networks. IEEE Communications Letters,19(1), 98–101.
Pachlor, R., & Shrimankar, D. (2017). VCH-ECCR: A centralized routing protocol for wireless sensor networks. Journal of Sensors,2017, 1–10.
Lin, Y.-H., Chou, Z.-T., Chun-Wei, Yu., & Jan, R.-H. (2015). Optimal and maximized configurable power saving protocols for corona-based wireless sensor networks. IEEE Transactions on Mobile Computing,14(2), 2544–2559.
Khabiri, M., & Ghaffari, A. (2018). Energy-aware clustering-based routing in wireless sensor networks using cuckoo optimization algorithm. Wireless Personal Communications,98(3), 2473–2495.
Ayati, M., Ghayyoumi, M. H., & Keshavarz-Mohammadiyan, A. (2018). A fuzzy three-level clustering method for lifetime improvement of wireless sensor networks. Annals of Telecommunications,73(7–8), 535–546.
Khoulalene, N., Bouallouche-Medjkoune, L., Aissani, D., Mani, A., & Ariouat, H. (2018). Clustering with load balancing-based routing protocol for wireless sensor networks. Wireless Personal Communications,103, 1–21.
Tall, H., Chalhoub, G., Hakem, N., & Misson, M. (2017). Load balancing routing with queue overflow prediction for wireless sensor networks. Wireless Networks,25, 1–11.
Luo, C., Chen, W., Yu, J., Wang, Y., & Li, D. (2018). A novel centralized algorithm for constructing virtual backbones in wireless sensor networks. EURASIP Journal on Wireless Communications and Networking,55, 1–12.
Ragavan, P. S., & Ramasamy, K. (2018). Optimized routing in wireless sensor networks by establishing dynamic topologies based on genetic algorithm. Cluster Computing,22, 1–7.
Soni, V., & Mallick, D. K. (2018). Fuzzy logic based multihop topology control routing protocol in wireless sensor networks. Microsystem Technologies,24(5), 2357–2369.
Mittal, N., Singh, U., & Sohi, B. S. (2018). An energy-aware cluster-based stable protocol for wireless sensor networks. Neural Computing and Applications,31, 1–18.
Dener, M. (2018). A new energy efficient hierarchical routing protocol for wireless sensor networks. Wireless Personal Communications,101(1), 269–286.
Akila, I. S., & Venkatesan, R. (2018). An energy balanced geo-cluster head set based multi-hop routing for wireless sensor networks. Cluster Computing,22, 1–10.
AlFarraj, O., AlZubi, A., & Tolba, A. (2018). Trust-based neighbor selection using activation function for secure routing in wireless sensor networks. Journal of Ambient Intelligence and Humanized Computing 1–11.
Hamidzadeh, J., & Ghomanjani, M. H. (2018). An unequal cluster-radius approach based on node density in clustering for wireless sensor networks. Wireless Personal Communications,101(3), 1619–1637.
Qin, D., Ji, P., Yang, S., & Berhane, T. M. (2018). An efficient data collection and load balance algorithm in wireless sensor networks. Wireless Networks,25, 1–12.
Wang, S., Yu, J., Atiquzzaman, M., Chen, H., & Ni, L. (2018). CRPD: A novel clustering routing protocol for dynamic wireless sensor networks. Personal and Ubiquitous Computing,22(3), 545–559.
Li, L., & Li, D. (2018). An energy-balanced routing protocol for a wireless sensor network. Journal of Sensors,2018, 1–13.
Dhand, G., & Tyagi, S. S. (2019). SMEER: Secure multi-tier energy-efficient routing protocol for hierarchical wireless sensor networks. Wireless Personal Communications,105(1), 17–35.
Selvi, M., Thangaramya, K., Ganapathy, S., Kulothungan, K., Nehemiah, H., & Kannan, A. (2019). An energy aware trust based secure routing algorithm for effective communication in wireless sensor networks. Wireless Personal Communications,105(4), 1475–1490.
Kalidoss, T., Rajasekaran, L., Kanagasabai, K., Sannasi, G., & Kannan, A. (2019). QoS aware trust-based routing algorithm for wireless sensor networks. Wireless Personal Communications 1–22.
Zakariayi, S., & Babaie, S. (2018). DEHCIC: A distributed energy-aware hexagon based clustering algorithm to improve coverage in wireless sensor networks. Peer-to-Peer Networking and Applications,12, 1–16.
Lai, W. K., & Fan, C.-S. (2017). Novel node deployment strategies in corona structure for wireless sensor networks. IEEE Access,5, 3889–3899.
Chatterjee, P., Ghosh, S. C., & Das, N. (2017). Load balanced coverage with graded node deployment in wireless sensor networks. IEEE Transactions on Multi-Scale Computing Systems,3(2), 100–112.
Rahman, A. U., Alharby, A., Hasbullah, H., & Almuzaini, K. (2016). Corona based deployment strategies in wireless sensor network: a survey. Journal of Network and Computer Applications,64, 176–193.
Mazinani, A., Mazinami, S. M., & Mirzaie, M. (2018). FMCR-CT: An energy-efficient fuzzy multi-cluster—based routing with a constant threashold in wireless sensor network. Alexandria Engineering Journal,58, 127–141.
Robinson, Y. H., Julie, E. G., Balaji, S., & Ayyasamy, A. (2016). Energy-aware clustering scheme in wireless sensor network using neuro-fuzzy approach. Wireless Personal Communications,95, 703–721.
Rewadkar, D., & Doye, D. (2018). Multi-objective auto-regressive whale optimization for traffic-aware routing in urban VANET. IET Information Security,12, 1–12.
Mirjalili, S., & Lewis, A. (2016). The whale optimization algorithm. Advances in Engineering Software,95, 51–67.
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Revanesh, M., Sridhar, V. & Acken, J.M. Secure Coronas Based Zone Clustering and Routing Model for Distributed Wireless Sensor Networks. Wireless Pers Commun 112, 1829–1857 (2020). https://doi.org/10.1007/s11277-020-07129-0
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11277-020-07129-0