Journal Home > Volume 5 , Issue 1
Intelligent and Converged Networks 2024, 5(1): 68-80 https://doi.org/10.23919/ICN.2024.0006
Open Access | Issue | Published: 28 March 2024

Fuzzy and IRLNC-based routing approach to improve data storage and system reliability in IoT

Show Author's Information U. Indumathi1( ),  A. R. Arunachalam1
Department of Computer Science and Engineering, Dr. M.G.R. Educational and Research Institute, Chennai 600095, India
Keywords:
Internet of Things (IoT), energy utilization, system reliability, data storage management, fuzzy system, improved random linear network coding
Cite this article:
Indumathi U, Arunachalam AR. Fuzzy and IRLNC-based routing approach to improve data storage and system reliability in IoT. Intelligent and Converged Networks, 2024, 5(1): 68-80. https://doi.org/10.23919/ICN.2024.0006
Download citation
EndNote(RIS)
BibTeX

37

Views

2

Downloads

Citations

0

Crossref

N/A

WoS

0

Scopus

N/A

CSCD

Abstract Full text About this article

Internet of Things (IoT) based sensor network is largely utilized in various field for transmitting huge amount of data due to their ease and cheaper installation. While performing this entire process, there is a high possibility for data corruption in the mid of transmission. On the other hand, the network performance is also affected due to various attacks. To address these issues, an efficient algorithm that jointly offers improved data storage and reliable routing is proposed. Initially, after the deployment of sensor nodes, the election of the storage node is achieved based on a fuzzy expert system. Improved Random Linear Network Coding (IRLNC) is used to create an encoded packet. This encoded packet from the source and neighboring nodes is transmitted to the storage node. Finally, to transmit the encoded packet from the storage node to the destination shortest path is found using the Destination Sequenced Distance Vector (DSDV) algorithm. Experimental analysis of the proposed work is carried out by evaluating some of the statistical metrics. Average residual energy, packet delivery ratio, compression ratio and storage time achieved for the proposed work are 8.8%, 0.92%, 0.82%, and 69 s. Based on this analysis, it is revealed that better data storage system and system reliability is attained using this proposed work.


menu
Abstract
Full text
Outline
About this article

Fuzzy and IRLNC-based routing approach to improve data storage and system reliability in IoT

Show Author's information U. Indumathi1( ), A. R. Arunachalam1
Department of Computer Science and Engineering, Dr. M.G.R. Educational and Research Institute, Chennai 600095, India

Abstract

Internet of Things (IoT) based sensor network is largely utilized in various field for transmitting huge amount of data due to their ease and cheaper installation. While performing this entire process, there is a high possibility for data corruption in the mid of transmission. On the other hand, the network performance is also affected due to various attacks. To address these issues, an efficient algorithm that jointly offers improved data storage and reliable routing is proposed. Initially, after the deployment of sensor nodes, the election of the storage node is achieved based on a fuzzy expert system. Improved Random Linear Network Coding (IRLNC) is used to create an encoded packet. This encoded packet from the source and neighboring nodes is transmitted to the storage node. Finally, to transmit the encoded packet from the storage node to the destination shortest path is found using the Destination Sequenced Distance Vector (DSDV) algorithm. Experimental analysis of the proposed work is carried out by evaluating some of the statistical metrics. Average residual energy, packet delivery ratio, compression ratio and storage time achieved for the proposed work are 8.8%, 0.92%, 0.82%, and 69 s. Based on this analysis, it is revealed that better data storage system and system reliability is attained using this proposed work.

Keywords: Internet of Things (IoT), energy utilization, system reliability, data storage management, fuzzy system, improved random linear network coding

References(29)

[1]

K. Haseeb, I. Ud Din, A. Almogren, and N. Islam, An energy efficient and secure IoT-based WSN framework: An application to smart agriculture, Sensors, vol. 20, no. 7, p. 2081, 2020.
DOI Google Scholar
[2]

K. Haseeb, A. Almogren, N. Islam, I. Ud Din, and Z. Jan, An energy-efficient and secure routing protocol for intrusion avoidance in IoT-based WSN, Energies, vol. 12, no. 21, p. 4174, 2019.
DOI Google Scholar
[3]

T. M. Behera, S. K. Mohapatra, U. C. Samal, M. S. Khan, M. Daneshmand, and A. H. Gandomi, I-SEP: An improved routing protocol for heterogeneous WSN for IoT-based environmental monitoring, IEEE Internet Things J., vol. 7, no. 1, pp. 710–717, 2020.
DOI Google Scholar
[4]

A. H. Bagdadee, M. Z. Hoque, and L. Zhang, IoT based wireless sensor network for power quality control in smart grid, Procedia Comput. Sci., vol. 167, pp. 1148–1160, 2020.
DOI Google Scholar
[5]

K. Haseeb, N. Islam, A. Almogren, I. Ud Din, H. N. Almajed, and N. Guizani, Secret sharing-based energy-aware and multi-hop routing protocol for IoT based WSNs, IEEE Access, vol. 7, pp. 79980–79988, 2019.
DOI Google Scholar
[6]

S. Kumar and V. K. Chaurasiya, A strategy for elimination of data redundancy in Internet of Things (IoT) based wireless sensor network (WSN), IEEE Syst. J., vol. 13, no. 2, pp. 1650–1657, 2019.
DOI Google Scholar
[7]

O. I. Khalaf and G. M. Abdulsahib, Optimized dynamic storage of data (ODSD) in IoT based on blockchain for wireless sensor networks, Peer Peer Netw. Appl., vol. 14, no. 5, pp. 2858–2873, 2021.
DOI Google Scholar
[8]
S. P. Sasirekha, A. Priya, T. Anita, and P. Sherubha, Data processing and management in IoT and wireless sensor network, J . Phys .: Conf . Ser ., vol. 1712, no. 1, p. 012002, 2020.
DOI
[9]

O. Diallo, J. J. P. C. Rodrigues, and M. Sene, Real-time data management on wireless sensor networks: A survey, J. Netw. Comput. Appl., vol. 35, no. 3, pp. 1013–1021, 2012.
DOI Google Scholar
[10]
B. D. Deebak and F. Al-Turjman, A hybrid secure routing and monitoring mechanism in IoT-based wireless sensor networks, Ad Hoc Netw ., vol. 97, p. 102022, 2020.
DOI
[11]

S. Sankar, P. Srinivasan, S. Ramasubbareddy, and B. Balamurugan, Energy-aware multipath routing protocol for Internet of Things using network coding techniques, Int. J. Grid Util. Comput., vol. 11, no. 6, pp. 838–846, 2020.
DOI Google Scholar
[12]
C. H. S. Oliveira, Y. Ghamri-Doudane, C. E. F. Brito, and S. Lohier, Optimal network coding-based In-network data storage and data retrieval for IoT/WSNs, in Proc. IEEE 14th Int. Symp. Network Computing and Applications, Cambridge, MA, USA, 2015, pp. 208–215.
DOI
[13]

S. Malathy, V. Porkodi, A. Sampathkumar, M. H. D. N. Hindia, K. Dimyati, V. Tilwari, F. Qamar, and I. S. Amiri, An optimal network coding based backpressure routing approach for massive IoT network, Wirel . Netw ., vol. 26, no. 5, pp. 3657–3674, 2020.
DOI Google Scholar
[14]

C. Chen, L. Zhang, and R. L. K. Tiong, A new lossy compression algorithm for wireless sensor networks using Bayesian predictive coding, Wirel. Netw., vol. 26, no. 8, pp. 5981–5995, 2020.
DOI Google Scholar
[15]

Z. Li, M. Xu, T. Liu, and L. Yu, A network coding-based braided multipath routing protocol for wireless sensor networks, Wirel. Commun. Mob. Comput., vol. 2019, p. 2757601, 2019.
DOI Google Scholar
[16]
T. Ho, M. Medard, J. Shi, M. Effros, and D. R. Karger, On randomized network coding, in Proc. 41th Annu. Allerton Conf. Communication, Control, and Computing, Monticello, IL, USA, 2003, pp. 11–20.
[17]
T. Ho, R. Koetter, M. Medard, D. R. Karger, and M. Effros, The benefits of coding over routing in a randomized setting, in Proc. IEEE Int. Symp. Information Theory, Yokohama, Japan, 2003, p. 442.
DOI
[18]

N. Chervyakov, M. Babenko, A. Tchernykh, N. Kucherov, V. Miranda-López, and J. M. Cortés-Mendoza, AR-RRNS: Configurable reliable distributed data storage systems for Internet of Things to ensure security, Future Gener. Comput. Syst., vol. 92, pp. 1080–1092, 2019.
DOI Google Scholar
[19]

H. Yang, F. Li, D. Yu, Y. Zou, and J. Yu, Reliable data storage in heterogeneous wireless sensor networks by jointly optimizing routing and storage node deployment, Tsinghua Science and Technology, vol. 26, no. 2, pp. 230–238, 2021.
DOI Google Scholar
[20]

P. Rafiee and G. Mirjalily, Distributed network coding-aware routing protocol incorporating fuzzy-logic-based forwarders in wireless ad hoc networks, J. Netw. Syst. Manag., vol. 28, no. 4, pp. 1279–1315, 2020.
DOI Google Scholar
[21]

H. N. Noura, R. Melki, M. Malli, and A. Chehab, Design and realization of efficient & secure multi-homed systems based on random linear network coding, Comput. Netw., vol. 163, p. 106886, 2019.
DOI Google Scholar
[22]
G. S. Paschos, G. Iosifidis, M. Tao, D. Towsley, and G. Caire, The role of caching in future communication systems and networks, IEEE J . Sel . Areas Commun ., vol. 36, no. 6, pp. 1111–1125, 2018.
DOI
[23]

H. Alshaheen and H. Takruri-Rizk, Energy saving and reliability for wireless body sensor networks (WBSN), IEEE Access, vol. 6, pp. 16678–16695, 2018.
DOI Google Scholar
[24]

D. Sinwar, N. Sharma, S. K. Maakar, and S. Kumar, Analysis and comparison of ant colony optimization algorithm with DSDV, AODV, and AOMDV based on shortest path in MANET, J. Inf. Optim. Sci., vol. 41, no. 2, pp. 621–632, 2020.
DOI Google Scholar
[25]

J. Chen, K. He, R. Du, M. Zheng, Y. Xiang, and Q. Yuan, Dominating set and network coding-based routing in wireless mesh networks, IEEE Trans. Parallel Distrib. Syst., vol. 26, no. 2, pp. 423–433, 2015.
DOI Google Scholar
[26]

Z. Mei and Z. Yang, Active intersession network coding-aware routing, Wirel. Netw., vol. 23, no. 4, pp. 1161–1168, 2017.
DOI Google Scholar
[27]
G. Vasanthi and N. Prabakaran, TCELR: Trusted cluster based energy and lifetime aware routing protocol for wireless sensor network using hybrid bird swarm-differential search algorithm, Int . J . Adv . Sci . Technol ., vol. 29, no. 12s, pp. 892–913, 2020.
[28]

D. Kalaimani, Z. Zah, and S. Vashist, Energy-efficient density-based Fuzzy C-means clustering in WSN for smart grids, Aust. J. Multi-Discipl. Eng., vol. 17, no. 1, pp. 23–38, 2021.
DOI Google Scholar
[29]

R. Maheswar, P. Jayarajan, A. Sampathkumar, G. R. Kanagachidambaresan, M. H. D. Nour Hindia, V. Tilwari, K. Dimyati, H. Ojukwu, and I. S. Amiri, CBPR: A cluster-based backpressure routing for the Internet of Things, Wirel. Pers. Commun., vol. 118, pp. 3167–3185, 2021.
DOI Google Scholar
Publication history
Copyright
Rights and permissions

Publication history

Received: 09 December 2022
Revised: 04 July 2023
Accepted: 20 August 2023
Published: 28 March 2024
Issue date: March 2024

Copyright

© All articles included in the journal are copyrighted to the ITU and TUP.

Rights and permissions

This work is available under the CC BY-NC-ND 3.0 IGO license:https://creativecommons.org/licenses/by-nc-nd/3.0/igo/

Return