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Wireless Personal Communications
Published in: Wireless Personal Communications 3/2020

20-06-2020

SecDL: QoS-Aware Secure Deep Learning Approach for Dynamic Cluster-Based Routing in WSN Assisted IoT

Authors: S. Sujanthi, S. Nithya Kalyani

Published in: Wireless Personal Communications | Issue 3/2020

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Abstract

In WSN-assisted IoT, energy efficiency and security which play pivotal role in Quality of Service (QoS) are still challenging due to its open and resource constrained nature. Although many research works have been held on WSN-IoT, none of them is able to provide high-level security with energy efficiency. This paper resolves this problem by designing a novel Secure Deep Learning (SecDL) approach for dynamic cluster-based WSN-IoT networks. To improve energy efficiency, the network is designed to be Bi-Concentric Hexagons along with Mobile Sink technology. Dynamic clusters are formed within Bi-Hex network and optimal cluster heads are selected by Quality Prediction Phenomenon (QP2) that ensure QoS and also energy efficiency. Data aggregation is enabled in each cluster and handled with a Two-way Data Elimination then Reduction scheme. A new One Time-PRESENT (OT-PRESENT) cryptography algorithm is designed to achieve high-level security for aggregated data. Then, the ciphertext is transmitted to mobile sink through optimal route to ensure high-level QoS. For optimal route selection, a novel Crossover based Fitted Deep Neural Network (Co-FitDNN) is presented. This work also concentrates on IoT-user security since the sensory data can be accessed by IoT users. This work utilizes the concept of data mining to authenticate the IoT users. All IoT users are authenticated by Apriori based Robust Multi-factor Validation algorithm which maps the ideal authentication feature set for each user. In this way, the proposed SecDL approach achieves security, QoS and energy efficiency. Finally, the network is modeled in ns-3.26 and the results show betterment in network lifetime, throughput, packet delivery ratio, delay and encryption time.
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Literature
1.
SalehiPanahi, M., & Abbaszadeh, M. (2018). Proposing a method to solve energy hole problem in wireless sensor networks. Alexandria Engineering Journal, 57, 1585–1590.
2.
Kalyani, S. N., & Sujanthi, S. (2018). Dynamic clustering approach in wireless sensor networks. International Journal of Management, Technology and Engineering, 8(XI), 781–785.
3.
Abidoye, A. P., & Obagbuwa, I. C. (2017). Models for integrating wireless sensor networks into the Internet of Things. IET Wireless Sensor Systems, 7, 65–72.
4.
Movva, P., & Rao, P. T. (2019). Novel two-fold data aggregation and mac scheduling to support energy efficient routing in wireless sensor network. IEEE Access, 7, 1260–1274.
5.
Ali, A., Ming, Y., Si, T., Iram, S., & Chakraborty, S. (2018). Enhancement of RWSN lifetime via firework clustering algorithm validated by ANN. Information, 9, 60.
6.
Shen, J., Wang, A., Wang, C., Hung, P. C., & Lai, C. (2017). An efficient centroid-based routing protocol for energy management in WSN-assisted IoT. IEEE Access, 5, 18469–18479.
7.
Priyadarshi, R., Singh, L., Kumar, S., Sharma, I., & Randheer, (2018). A hexagonal network division approach for reducing energy hole issue in WSN. International Journal of Pure and Applied Mathematics, 118(20), 1003–1008.
8.
Shrivastav, K., & Kulat, K. D. (2018). Energy efficient scalability of three level hexagonal heterogeneous broad transmission distance protocol (3L-HEXA-HTBTDP) for WSN-IoT networks. International Journal of Communication Systems, 31, 1–27.
9.
Shrivastav, K., & Kulat, K. D. (2020). Scalable energy efficient hexagonal heterogeneous broad transmission distance protocol in WSN-IoT Networks. Journal of Electrical Engineering & Technology, 15, 95–120.
10.
Hanif, S., Khedr, A. M., Aghbari, Z. A., & Agrawal, D. P. (2018). Opportunistically exploiting internet of things for wireless sensor network routing in smart cities. Journal of Sensor and Actuator Networks, 7, 46.
11.
Jabbar, W. A., Saad, W. K., & Ismail, M. (2018). MEQSA-OLSRv2: A multicriteria-based hybrid multipath protocol for energy-efficient and QoS-aware data routing in MANET-WSN convergence scenarios of IoT. IEEE Access, 6, 76546–76572.
12.
Ahmed, A., Pasha, M.A., Ahmad, Z., Masud, S., & Sikora, A. (2017). Energy efficient sensor network routing (EESNR) protocol for large distributed environmental monitoring applications. In 2017 9th IEEE international conference on intelligent data acquisition and advanced computing systems: Technology and applications (IDAACS), Vol. 2, pp. 740–745.
13.
14.
Buchanan, William J., Li, Shancang, & Asif, Rameez. (2017). Lightweight cryptography methods. Journal of Cyber Security Technology, 1(3–4), 187–201.
15.
Tsai, K., Leu, F., Su, T., & Chang, Y. (2017). A light weight data encryption method for WSN communication. In BWCCA.
16.
Hung, C., & Hsu, W. (2018). Power consumption and calculation requirement analysis of AES for WSN IoT. Sensors, 18, 1675.
17.
Hammi, M. T., Livolant, E., Bellot, P., Serhrouchni, A., & Minet, P. (2017). A lightweight IoT security protocol. In 2017 1st cyber security in networking conference (CSNet), pp. 1–8.
18.
Xiao, L., Wan, X., Lu, X., Zhang, Y., & Wu, D. (2018). IoT security techniques based on machine learning: How do IoT devices use AI to enhance security? IEEE Signal Processing Magazine, 35, 41–49.
19.
Hammi, M. T., Livolant, E., Bellot, P., Serhrouchni, A., & Minet, P. (2017). A lightweight mutual authentication protocol for the IoT.
20.
El-hajj, M., Fadlallah, A., Chamoun, M., & Serhrouchni, A. (2019). A survey of internet of things (IoT) authentication schemes. Sensors, 19, 1141.
21.
Mishra, D., Vijayakumar, P., Sureshkumar, V., Amin, R., Islam, S. H., & Gope, P. (2017). Efficient authentication protocol for secure multimedia communications in IoT-enabled wireless sensor networks. Multimedia Tools and Applications, 77, 18295–18325.
22.
Ang, K. L., & Seng, J. K. P. (2019). Application specific internet of things (ASIoTs): Taxonomy, applications, use case and future directions. IEEE Access, 7, 56577–56590.
23.
Ray, P. P. (2018). A survey on internet of things architectures. Journal of King Saud University Computer and Information Sciences, 30, 291–319.
24.
Boubiche, S., Boubiche, D. E., Bilami, A., & Toral-Cruz, H. (2018). Big data challenges and data aggregation strategies in wireless sensor networks. IEEE Access, 6, 20558–20571.
25.
Čolaković, Alem, & Hadzialic, Mesud. (2018). Internet of things (IoT): A review of enabling technologies, challenges, and open research issues. Computer Networks, 144, 17–39.
26.
Kouicem, D. E., Bouabdallah, A., & Lakhlef, H. (2018). Internet of things security: A top-down survey. Computer Networks, 141, 199–221.
27.
Kulkarni, S., Vani, R. M., & Hunagund, P. V. (2018). Review on IoT based case study: Applications and challenges. In ICICI-2018.
28.
Khan, F. I., & Hameed, S. (2018). Understanding security requirements and challenges in internet of things (IoTs): A review. Journal of Computer Networks and Communications.
29.
Kumar, S., Lal, N., & Chaurasiya, V. K. (2018). A forwarding strategy based on ANFIS in internet-of-things-oriented wireless sensor network (WSN) using a novel fuzzy-based cluster head protocol. Annals of Telecommunications, 73, 627–638.
30.
Elappila, M., Chinara, S., & Parhi, D. R. (2018). Survivable path routing in WSN for IoT applications. Pervasive and Mobile Computing, 43, 49–63.
31.
Amjad, M., Afzal, M., Umer, T., & Kim, B. (2017). QoS-aware and heterogeneously clustered routing protocol for wireless sensor networks. IEEE Access, 5, 10250–10262.
32.
Zhang, W., Liu, Y., Han, G., Feng, Y., & Zhao, Y. (2018). An energy efficient and QoS aware routing algorithm based on data classification for industrial wireless sensor networks. IEEE Access, 6, 46495–46504.
33.
Deepa, O. S., & Suguna, J. (2017). An optimized QoS-based clustering with multipath routing protocol for Wireless Sensor Networks. Journal of King Saud University – Computer and Information Sciences.
34.
Preeth, S., Dhanalakshmi, R. V., Kumar, R., & Shakeel, P. M. (2018). An adaptive fuzzy rule based energy efficient clustering and immune-inspired routing protocol for WSN-assisted IoT system. Journal of Ambient Intelligence and Humanized Computing, 1–13.
35.
Ullah, M. F., Imtiaz, J., & Maqbool, K. Q. (2019). Enhanced three layer hybrid clustering mechanism for energy efficient routing in IoT. Sensors, 19, 829.
36.
Rani, S., Ahmed, S. H., & Rastogi, R. (2019). Dynamic clustering approach based on wireless sensor networks genetic algorithm for IoT applications. Wireless Networks.
37.
Xu, C., Xiong, Z., Zhao, G., & Yu, S. (2019). An energy-efficient region source routing protocol for lifetime maximization in WSN. IEEE Access, 7, 135277–135289.
38.
Srinidhi, N. N., Lakshmi, J., & Kumar, S. M. (2019). Hybrid energy efficient and QoS aware algorithm to prolong IoT network lifetime.
39.
Li, X., Niu, J., Kumari, S., Wu, F., Sangaiah, A. K., & Choo, K. R. (2018). A three-factor anonymous authentication scheme for wireless sensor networks in internet of things environments. Journal of Network and Computer Applications, 103, 194–204.
40.
Sathyadevan, S., Achuthan, K., Doss, R., & Pan, L. (2019). Protean authentication scheme—A time-bound dynamic KeyGen authentication technique for IoT edge nodes in outdoor deployments. IEEE Access, 7, 92419–92435.
41.
Ali, B. A., Abdulsalam, H. M., & AlGhemlas, A. (2018). Trust based scheme for IoT enabled wireless sensor networks. Wireless Personal Communications, 99, 1061–1080.
42.
Mughal, M. A., Shi, P., Ullah, A., Mahmood, K., Abid, M., & Luo, X. (2019). Logical tree based secure rekeying management for smart devices groups in IoT enabled WSN. IEEE Access, 7, 76699–76711.
43.
Tabassum, A., Sadaf, S., Sinha, D., & Das, A. K. (2020). Secure anti-void energy-efficient routing (SAVEER) protocol for WSN-based IoT network.
44.
Jain, J. K. (2019). Secure and energy-efficient route adjustment model for internet of things. Wireless Personal Communications, 108, 633–657.
45.
Boakye-Boateng, K., Kuada, E., Antwi-Boasiako, E., & Djaba, E. (2019). Encryption protocol for resource-constrained devices in fog-based IoT using one-time pads. IEEE Internet of Things Journal, 6, 3925–3933.
46.
Thangaramya, K., Kulothungan, K., Logambigai, R., Lavina, L. S., Ganapathy, S., & Kannan, A. (2019). Energy aware cluster and neuro-fuzzy based routing algorithm for wireless sensor networks in IoT. Computer Networks, 151, 211–223.
47.
Shah, S. B., Chen, Z., Yin, F., Khan, I. U., & Ahmad, N. (2018). Energy and interoperable aware routing for throughput optimization in clustered IoT-wireless sensor networks. Future Generation Computing Systems, 81, 372–381.
48.
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. IEEE Internet of Things Journal, 6, 5132–5139.
49.
Liu, H., Wang, Y., Liu, J., Yang, J., Chen, Y., & Poor, H. V. (2018). Authenticating users through fine-grained channel information. IEEE Transactions on Mobile Computing, 17(2), 251–264.
50.
Kalyani, S. N., Sasikala, E., & Gopinath, B. (2015). Collaborative data processing in WSN using Voronoi fuzzy clustering. International Journal of Computers Communications & Control, 10(3), 348–356.
51.
52.
Kalyani, S. N., & Mary, P. R. (2018). Energy efficient by piezoelectric effect synchronous multicast protocol (PESM) in wireless sensor networks. Journal of Electrical Engineering, 18(1), 1–7.
53.
Zeng, B., & Dong, Y. (2016). An improved harmony search based energy-efficient routing algorithm for wireless sensor networks. Applied Soft Computing, 41, 135–147.
54.
Tan, J., Liu, W., Xie, M., Song, H., Liu, A., Zhao, M., et al. (2019). A low redundancy data collection scheme to maximize lifetime using matrix completion technique. EURASIP Journal on Wireless Communications and Networking, 2019, 1–29.
55.
Singh, P., & Chauhan, R. K. (2017). A survey on comparisons of cryptographic algorithms using certain parameters in WSN. International Journal of Electrical and Computer Engineering, 7(4), 2232–2240.
56.
Wang, L., & Xia, K. (2019). Data fusion algorithms for wireless sensor networks based on deep learning model. In HP3C ‘19.
57.
Yang, J., Akyurek, A. S., Tilak, S., & Rosing, T. S. (2018). Design of transmission manager in heterogeneous WSNs. IEEE Transactions on Emerging Topics in Computing, 6, 395–408.
58.
Bogdanov, A., Knudsen, L. R., Leander, G., Paar, C., Poschmann, A., Robshaw, M. J., Seurin, Y., & Vikkelsoe, C. (2007). PRESENT: An ultra-lightweight block cipher. In CHES.
Metadata
Title
SecDL: QoS-Aware Secure Deep Learning Approach for Dynamic Cluster-Based Routing in WSN Assisted IoT
Authors
S. Sujanthi
S. Nithya Kalyani
Publication date
20-06-2020
Publisher
Springer US
Published in
Wireless Personal Communications / Issue 3/2020
Print ISSN: 0929-6212
Electronic ISSN: 1572-834X
DOI
https://doi.org/10.1007/s11277-020-07469-x

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