JNACSISSN:2582-3817

Multipath Transmission in IoT using Hybrid Salp Swarm-Differential Evolution Algorithm

Abstract

The improvements in technology in the field of communication did Wireless Sensor Network (WSN) on the basis of the IoT desirable and appropriate to several regions. It is encompassed that IoT nodes perform on restricted battery provisions. Therefore, a maximum-routing protocol performance is necessary for routing in such networks to surmount the energy constraint issues. For multipath data transmission, an energy-efficient routing algorithm named Hybrid Salp Swarm and Simulated Algorithm (Hybrid SS and SA) is adopted in this work. The adopted approach enhanced the routing procedure in a dual-phase procedure. Initially, the cluster heads are chosen by exploiting the Salp Swarm Algorithm. Subsequently, multiple paths are produced from the sender to the receive r by exploiting DE. Here, inter and intra-cluster distance, energy, delay and lifetime is considered as the objective function. In the fitness model, an optimal path for the transmission is presented. Finally, the investigational outcomes exhibit that the performance of the proposed model shows maximum performance with respect to the alive nodes and energy in contrast with the conventional approaches.

References

  • Jafar A. Alzubi, Ramachandran Manikandan, Omar A. Alzubi, Issa Qiqieh, Ashish Khanna, "Hashed Needham Schroeder Industrial IoT based Cost Optimized Deep Secured data transmission in cloud", Measurement, Volume 150, January 2020.

  • Debasish Ghose, Anders Frøytlog, Frank Y. Li, "Enabling early sleeping and early data transmission in wake -up radio-enabled IoT networks", Computer Networks, Volume 153, 22 April 2019, Pages 132-144.

  • Nasir N. Hurrah, Shabir A. Parah, Javaid A. Sheikh, Fadi Al-Turjman, Khan Muhammad, "Secure data transmission framework for confidentiality in IoTs", Ad Hoc Networks, Volume 95, December 2019.

  • Arezou Ostad-Sharif, Hamed Arshad, Morteza Nikooghadam, Dariush Abbasinezhad-Mood, "Three party secure data transmission in IoT networks through design of a lightweight authenticated key agreement scheme", Future Generation Computer Systems, Volume 100, November 2019, Pages 882-892.

  • Rihab Boussada, Balkis Hamdane, Mohamed Elhoucine Elhdhili, Leila Azouz Saidane, "Privacy-preserving aware data transmission for IoT-based e-health", Computer Networks, Volume 162, 24 October 2019.

  • X. Chen et al., "iDiSC: A New Approach to IoT-Data-Intensive Service Components Deployment in Edge-CloudHybrid System," IEEE Access, vol. 7, pp. 59172-59184, 2019.

  • Y. Inagaki, R. Shinkuma, T. Sato and E. Oki, "Prioritization of Mobile IoT Data Transmission Based on Data Importance Extracted From Machine Learning Model," IEEE Access, vol. 7, pp. 93611-93620, 2019.

  • C. Song, Y. Qi and M. Liu, "One-Request Scheme for M2P Data Transmissions in Software-Defined IoT Networks," IEEE Access, vol. 6, pp. 13090-13100, 2018.

  • Rajeev Kumar and Dilip Kumar, "Multi-objective fractional artificial bee colony algorithm to energyaware routing protocol in wireless sensor network," Wireless Networks, vol. 22, no. 5, pp. 1461–1474, July, 2016.

  • Ajay Kumar Yadav and Sachin Tripathi, “QMRPRNS: Design of QoS multicast routing protocol using reliable node selection scheme for MANETs,” Peer-to-Peer Networking and Applications, vol. 10, no. 4, pp. 897-909, July, 2017.

  • Amol V. Dhumane, Rajesh S. Prasad, "Multi-objective fractional gravitational search algorithm for energy efficient Routing in IoT," Wireless network, pp. 1-15, August, 2017.

  • Amol V. Dhumane and Rajesh S. Prasad, "Fractional Gravitational Grey Wolf Optimization to Multi-Path Data Transmission in IoT", Wireless Personal Communications, September 2018, Volume 102, Issue 1, pp 411–436.

  • S. Mirjalili, A.H. Gandomi, S.Z. Mirjalili, S. Saremi, H. Faris, S.M. Mirjalili, Salp Swarm Algorithm: A bio - inspired optimizer for engineering design problems, Adv. Eng. Softw. 114 (2017) 163–191.

  • R. Storn, K. Price, Differential Evolution: A Simple and Efficient Heuristic for global Optimization over Continuous Spaces, J. Glob. Optim. 11 (1997) 341-359.

  • S. Mirjalili, S. Saremi, S.M. Mirjalili, L.S. Coelho, Multi-objective grey wolf optimizer: A novel algorithm for multi-criterion optimization, Expert Syst. Appl. 47 (2015)106119.

  • K. Deb, S. Agrawal, A. Pratap, T. Meyarivan, A Fast Elitist Non-Dominated Sorting Genetic Algorithm for Multi-Objective Optimization: NSGA-II, in: Int. Conf. Parallel Probl. Solving from Nat., 2000: pp. 849-858.

  • Christin D, Reinhardt A, Mogre PS, Steinmetz R., "Wireless sensor networks and the internet of things: selected challenges," in Proceedings of the 8th GI/ITG KuVS Fachgesprach Drahtlose Sensornetze, pp. 31-33, August, 2009.

  • Alessandro Di Stefano, Aurelio La Corte, Marco Leotta, Pietro Lio, Marialisa Scata, "It measures like me: An IoTs algorithm in WSNs based on heuristics behavior and clustering methods,” Ad Hoc Networks, Vol. 11, no. 8, pp. 2637–2647, November, 2013.