International Journal of Computer Networks and Applications

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Fractional Gaussian Firefly Algorithm and Darwinian Chicken Swarm Optimization for IoT Multipath Fault-Tolerant Routing


Affiliations
1 Computer Science Department, Rathinam College of Arts and Science, Bharathiar University, Coimbatore, India
2 Department of Information Technology, Sri Krishna Adithya College of Arts and Science, Bharathiar University, Coimbatore, India
 

Wireless Sensor Networks (WSN) based Internet-of- Things (IoT) systems offer high efficient data transmission with enhanced Quality of Service (QoS). A multi-constraint based energy-efficient and fault-tolerant routing algorithm using Fractional Gaussian Firefly Algorithm (FGFA) and Darwinian Chicken Swarm Optimization (DCSO) are presented for performing optimal multipath communication. FGFA is an improved Firefly Algorithm in which the fractional theory and Gaussian function are incorporated to improve the convergence speed with higher efficiency. Likewise, the DCSO is an improved model of CSO based on the survival theory of Darwin to decrease the computation time and improve the convergence by eliminating the local optimal challenges. Initially, the network is clustered and the cluster heads (CH) are chosen optimally by FGFA based on the objective function with multiple QoS constraints. Then the best routing paths are chosen by DCSO through similar objective function with inter-cluster and intracluster delay additionally included. The optimal paths are sorted in a hierarchical order from which multiple paths are utilized for data communication. The FGFA+DCSO routing protocol is assessed in NS-2 simulator and the outcomes shown the proficiency of the suggested approach with 6.3% reduced delay, 6% improved throughput, 26.7% minimized energy, 11% increased lifetime, 20% higher PSNR, and hop count reduced by 1.

Keywords

Internet-of-Things, Wireless Sensor Networks, Fault Tolerance, Energy Constraint Problem, Fractional Gaussian Firefly Algorithm, Darwinian Chicken Swarm Optimization.
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  • J. Gubbi, R. Buyya, S. Marusic and M. Palaniswami, “Internet of Things (IoT): A vision, architectural elements, and future directions,” Future generation computer systems, vol. 29, no. 7, pp. 1645-1660, 2013.

  • S. Park, N. Crespi, H. Park and S. H. Kim, “IoT routing architecture with autonomous systems of things,” In Proc. 2014 IEEE World Forum on Internet of Things (WF-IoT), pp. 442-445, March 2014.

  • R. Fantacci, T. Pecorella, R. Viti and C. Carlini, “A network architecture solution for efficient IoT WSN backhauling: challenges and opportunities,” IEEE Wireless Communications, vol. 21, no. 4, pp. 113-119, 2014.

  • K. Tejasvit, “Challenges in integrating wireless sensor networks into the internet,” International Journal of Engineering and Management Sciences, vol. 5, no. 1, pp. 7-11, 2014.

  • P. Rajpoot, S. H. Singh, R. Verma, K. Dubey, S. K. Pandey and S. Verma, “Multi-factor-Based Energy-Efficient Clustering and Routing Algorithm for WSN,” In Soft Computing: Theories and Applications, Springer, Singapore, pp. 571-581, 2020.

  • S. Mohapatra and P. Kanungo, “Performance analysis of AODV, DSR, OLSR and DSDV routing protocols using NS2 Simulator,” Procedia Engineering, vol. 30, pp. 69-76, 2012.

  • Z. Fei, B. Li, S. Yang, C. Xing, H. Chen and L. Hanzo, “A survey of multi-objective optimization in wireless sensor networks: Metrics, algorithms, and open problems,” IEEE Communications Surveys and Tutorials, vol. 19, no. 1, pp. 550-586, 2016.

  • M. Elhoseny and A. E. Hassanien, “Optimizing cluster head selection in WSN to prolong its existence,” In Dynamic Wireless Sensor Networks, Springer, Cham, pp. 93-111, 2019.

  • M. Radi, B. Dezfouli, K. A. Bakar and M. Lee, “Multipath routing in wireless sensor networks: survey and research challenges,” Sensors, vol. 12, no. 1, pp. 650-685, 2012.

  • S. S. A. B. Hmaid and V. Vasanthi, “Multipath Data Transmission in IoT Networks Using Fractional Firefly Algorithm and Chicken Swarm Optimization,” International Journal of Intelligent Engineering and Systems, vol. 13, no. 3, pp. 373-383, 2020.

  • P. Lalwani and S. Das, “Bacterial foraging optimization algorithm for CH selection and routing in wireless sensor networks,” In Proc. 2016 3rd international conference on recent advances in information technology (RAIT), IEEE, pp. 95-100, March 2016.

  • M. Z. Hasan and F. Al-Turjman, “Optimizing multipath routing with guaranteed fault tolerance in Internet of Things,” IEEE Sensors Journal, vol. 17, no. 19, pp. 6463-6473, 2017.

  • K. Haseeb, K. A. Bakar, A. H. Abdullah, A. Ahmed, T. Darwish and F. Ullah, “A dynamic Energy-aware fault-tolerant routing protocol for wireless sensor networks,” Computers & Electrical Engineering, vol. 56, no. 1, pp. 557-575, 2016.

  • J. W. Lin, P. R. Chelliah, M. C. Hsu and J. X. Hou, “Efficient fault-tolerant routing in IoT wireless sensor networks based on bipartite-flow graph modelling,” IEEE Access, vol. 7, no. 1, pp. 14022-14034, 2019.

  • T. Muhammed, R. Mehmood, A. Albeshri and A. Alzahrani, “HCDSR: A hierarchical clustered fault-tolerant routing technique for IoT-based smart societies,” In Smart Infrastructure and Applications, Springer, Cham, pp. 609-628, 2020.

  • L. Rui, X. Wang, Y. Zhang, X. Wang and X. Qiu, “A self-adaptive and fault-tolerant routing algorithm for wireless sensor networks in microgrids,” Future Generation Computer Systems, vol. 100, no. 1, pp. 35-45, 2019.

  • P. Lalwani, H. Banka and C. Kumar, “BERA: a biogeography-based energy-saving routing architecture for wireless sensor networks,” Soft Computing, vol. 22, no. 5, pp. 1651-1667, 2018.

  • S. S. L. Preeth, R. Dhanalakshmi, R. Kumar and P. M. Shakeel, “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, pp. 1-13, 2018.

  • K. Thangaramya, K. Kulothungan, R. Logambigai, M. Selvi, S. Ganapathy and A. Kannan, “Energy-aware cluster and neuro-fuzzy based routing algorithm for wireless sensor networks in IoT,” Computer Networks, vol. 151, pp. 211-223, 2019.

  • K. Vijayalakshmi and P. Anandan, “A multi-objective Tabu particle swarm optimization for effective cluster head selection in WSN,” Cluster computing, vol. 22, no. 5, pp. 12275-12282, 2019.

  • N. Mittal, “Moth flame optimization-based energy efficient stable clustered routing approach for wireless sensor networks,” Wireless Personal Communications, vol. 104, no. 2, pp. 677-694, 2019.

  • K. M. Awan, H. H. R. Sherazi, A. Ali, R. Iqbal, Z. A. Khan and M. Mukherjee, “Energy‐aware cluster‐based routing optimization for WSNs in the livestock industry,” Transactions on Emerging Telecommunications Technologies, p. e3816, 2019.

  • A. Vinitha and M. S. S. Rukmini, “Secure and energy-aware multi-hop routing protocol in WSN using Taylor-based hybrid optimization algorithm,” Journal of King Saud University-Computer and Information Sciences, In Press, 2019.

  • S. Pattnaik and P. K. Sahu, “Assimilation of fuzzy clustering approach and EHO‐Greedy algorithm for efficient routing in WSN,” International Journal of Communication Systems, p. e4354, 2020.

  • A. Kavitha, K. Guravaiah and R. L. Velusamy, “A Cluster-Based Routing Strategy Using Gravitational Search Algorithm for WSN,” Journal of Computing Science and Engineering, vol. 14, no. 1, pp. 26-39, 2020.

  • R. Vinodhini and C. Gomathy, “MOMHR: A Dynamic Multi-hop Routing Protocol for WSN Using Heuristic Based Multi-objective Function,” Wireless Personal Communications, vol. 111, no. 2, pp. 883-907, 2020.

  • R. Kumar, D. Kumar and D. Kumar, “Exponential Ant Colony Optimization and Fractional Artificial Bee Colony to Multi-Path Data Transmission in Wireless Sensor Networks,” IET Communications, vol. 11, no. 4, pp. 522-530, 2017.

  • A. V. Dhumane and R. S. Prasad, “Fractional Gravitational Grey Wolf Optimization to Multi-Path Data Transmission in IoT,” Wireless Personal Communications, vol. 102, no. 1, pp. 411-436, 2018.

  • S. M. Farahani, A. A. Abshouri, B. Nasiri and M. R. Meybodi, “A Gaussian firefly algorithm,” International Journal of Machine Learning and Computing, vol. 1, no. 5, pp. 448-453, 2011.

  • J. Tillett, T. Rao, F. Sahin and R. Rao, “Darwinian particle swarm optimization,” In Proc. 2nd Indian International Conference on Artificial Intelligence, Pune, India, 2005.


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  • Fractional Gaussian Firefly Algorithm and Darwinian Chicken Swarm Optimization for IoT Multipath Fault-Tolerant Routing

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Authors

Salem Abdulla Awadh Ba hmaid
Computer Science Department, Rathinam College of Arts and Science, Bharathiar University, Coimbatore, India
V. Vasanthi
Department of Information Technology, Sri Krishna Adithya College of Arts and Science, Bharathiar University, Coimbatore, India

Abstract


Wireless Sensor Networks (WSN) based Internet-of- Things (IoT) systems offer high efficient data transmission with enhanced Quality of Service (QoS). A multi-constraint based energy-efficient and fault-tolerant routing algorithm using Fractional Gaussian Firefly Algorithm (FGFA) and Darwinian Chicken Swarm Optimization (DCSO) are presented for performing optimal multipath communication. FGFA is an improved Firefly Algorithm in which the fractional theory and Gaussian function are incorporated to improve the convergence speed with higher efficiency. Likewise, the DCSO is an improved model of CSO based on the survival theory of Darwin to decrease the computation time and improve the convergence by eliminating the local optimal challenges. Initially, the network is clustered and the cluster heads (CH) are chosen optimally by FGFA based on the objective function with multiple QoS constraints. Then the best routing paths are chosen by DCSO through similar objective function with inter-cluster and intracluster delay additionally included. The optimal paths are sorted in a hierarchical order from which multiple paths are utilized for data communication. The FGFA+DCSO routing protocol is assessed in NS-2 simulator and the outcomes shown the proficiency of the suggested approach with 6.3% reduced delay, 6% improved throughput, 26.7% minimized energy, 11% increased lifetime, 20% higher PSNR, and hop count reduced by 1.

Keywords


Internet-of-Things, Wireless Sensor Networks, Fault Tolerance, Energy Constraint Problem, Fractional Gaussian Firefly Algorithm, Darwinian Chicken Swarm Optimization.

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DOI: https://doi.org/10.22247/ijcna%2F2020%2F205318