International Journal of Computer Networks and Applications

Open Access Open Access  Restricted Access Subscription Access

Optimizing Ad-Hoc Routing Protocols in WSN to Enhance QoS Parameters Using Evolutionary Computation Algorithms


Affiliations
1 School Of Electronics and Electrical Engineering, Lovely Professional University, Phagwara, Punjab, India
2 Faculty of Medicine, Research Unit of Health Sciences and Technology, University of Oulu, Finland
 

Wireless Sensor Networks (WSNs) have garnered considerable attention within the research community focused on fraternity due to their extensive utilization in healthcare, environmental surveillance, disaster avoidance, farming methods, wildfire detection, and other practical applications. Enormous applications have been developed in the Internet of Things (IoT) era resulting in an ever-increasing number of connected WSN devices. As a result, WSNs consistently face challenges in delivering the required quality of service (QoS) affecting the average end-to-end delay, energy utilization, and packet loss throughout the transmission process. An efficient routing protocol must be designed to address these constraints and improve the operational efficiency of WSNs regarding Quality of Service (QoS) metrics. Motivated by these challenges, this paper presents an advanced routing algorithm by integrating optimization in the AODV routing protocol for ad hoc networks employing Particle Swarm Optimization (PSO). The proposed multipath protocol is termed the EPSO-AODV algorithm. The proposed algorithm is assessed through numerous simulations carried out with varied system setups and parameters. Additionally, the efficiency of the proposed protocol is assessed in comparison to conventional routing protocols including AODV, Dynamic Source Routing (DSR), Destination-Sequenced Distance Vector (DSDV), and Optimized Link State Routing (OLSR) protocols. It is observed from the experimental findings that the proposed approach outperforms existing algorithms and offers several benefits including better energy efficiency, ensuring high packet delivery ratio, throughput, and minimal end-to-end delay delay, reduced normalization load. The proposed protocol efficiently distributes energy usage to enhance throughput and enhance the performance of wireless sensor networks. As per the simulation results, the packet delivery ratio has improved from 81.58% to 91.60% whereas the throughput is observed to be 36.32 kbps for conventional AODV and 74.21 kbps for the proposed algorithm. The routing overhead is lowered by approximately 40% and the AE2E delay was found to be 0.04 lower in comparison to AODV. The residual energy in the context of the EPSO-AODV proposal is less (4981 Joules) than AODV (6344 Joules) which proves the superior efficiency of the proposed algorithm.

Keywords

Ad Hoc On-Demand Distance Vector Routing, Particle Swarm Optimization, Machine Learning, Network Lifespan, Energy Balancing, Localization, Clustering, Routing Overhead, Throughput, End-to-End Delay.
User
Notifications
Font Size

  • Sung-Jin Choi, Kyung Tae Kim, and Hee Yong Youn, “An energy-efficient key pre-distribution scheme for wireless sensor networks using eigenvector”, College of Information and Communication Engineering, Sungkyunkwan University, Vol 1, pp. 440-746, 2013.

  • M. A. Ouamri, G. Barb, D. Singh, A. Adam, M. S. A. Muthanna, and X. Li, “Nonlinear Energy-Harvesting for D2D Networks Underlaying UAV with SWIPT Using MADQN,” IEEE Communications Letters, vol. 27, no. 7, pp. 1804-1808, 2023.

  • A. Nandi, B. Sonowal, D. Rabha, and A. Vaibhav, “Centered sink LEACH protocol for enhanced performance of wireless sensor network,” in International Conference on Automation, Computational and Technology Management (ICACTM), pp. 436–440, London, United Kingdom, 2019.

  • M. A. Ouamri, G. Barb, D. Singh, and F. Alexa, “Load balancing optimization in software-defined wide area networking (SD-WAN) using deep reinforcement learning,” in 2022 International Symposium on Electronics and Telecommunications (ISETC), pp. 1-6. IEEE, 2022.

  • V. Kapoor and D. Singh, “FBESSM: An Fuzzy Based Energy Efficient Sleep Scheduling Mechanism for Convergecast in Wireless Sensor Networks,” International Journal of Intelligent Systems and Applications in Engineering, vol. 11, no. 9s, pp. 767-781, 2023.

  • M. Sharawi, I. A. Saroit, H. El-Mahdy, and E. Emary, “Routing wireless sensor networks based on soft computing paradigms: survey,” International Journal on Soft Computing, Artificial Intelligence and Applications (IJSCAI), vol. 2, no. 4, pp. 21–36, 2013.

  • J. Wang, C. Ju, Y. Gao, A. K. Sangaiah, and G. J. Kim, “A PSO based energy efficient coverage control algorithm for wireless sensor networks,” Computers, Materials & Continua, vol. 56, no. 3, pp. 433–446, 2018

  • O. M. Amine, R. Alkanhel, D. Singh, E. M. Kenaway, and S. Ghoneim, “Double deep q-network method for energy efficiency and throughput in a uav-assisted terrestrial network,” International Journal of Computer Systems Science & Engineering, vol. 46, no. 1, pp. 73-92, 2023.

  • B. Guruprakash, C. Balasubramanian, and R. Sukumar, “An approach by adopting multi-objective clustering and data collection along with node sleep scheduling for energy-efficient and delay aware WSN,” Peer-to-Peer Networking and Applications, vol. 13, no. 1, pp. 304–319, 2020.

  • S. H. Liu, W. Zeng, Y. Lou, and J. Zhai, “A reliable multi-path routing approach for medical wireless sensor networks,” in International Conference on Identification, Information, and Knowledge in the Internet of Things (IIKI), Beijing, Oct. 2015

  • J. Wang, Y. Gao, C. Zhou, R. S. Sherratt, and L. Wang, “Optimal coverage multi-path scheduling scheme with multiple mobile sinks for WSNs,” Computers, Materials & Continua, vol. 62, no. 2, pp. 695–711, 2020.

  • V. K. Kashyap, R. Astya, P. Nand and G. Pandey, “Comparative study of AODV and DSR routing protocols in wireless sensor network using NS-2 simulator,” in Proc. ICCCA, Greater Noida, India, pp. 687–690, 2017.

  • A. A. Chavan, D. S. Kurule and P. U. Dere, “Performance analysis of AODV and DSDV routing protocol in MANET and modifications in AODV against black hole attack,” Procedia Computer Science, vol. 79, pp. 835–844, 2016.

  • P. Chanak, I. Banerjee and R. S. Sherratt, “A green cluster-based routing scheme for large-scale wireless sensor networks,” International Journal of Communication Systems, vol. 33, no. 9, pp. e4375, 2020.

  • M. Ouamri, Y. Machter, D. Singh, D. Alkama, and X. Li, “Joint Energy Efficiency and Throughput Optimization for UAV-WPT Integrated Ground Network using DDPG,” IEEE Communications Letters, 2023.

  • P. Joshi, G. Singh, and A. S. Raghuvanshi, “Comparative study of different routing protocols for IEEE 802.15.4- enabled mobile sink wireless sensor network,” Lecture Notes in Electrical Engineering, vol. 587, pp. 161–170, 2020.

  • N. Shabbir and S. R. Hassan, Routing protocols for wireless sensor networks (WSNs). in Wireless Sensor Networks-Insights and Innovations, 1st ed., vol. 1. London, U.K: Intech Open, pp. 21–37, 2017.

  • T. Wang, J. Liu, and L. Cheng, “Robust collaborative mesh networking with large-scale distributed wireless heterogeneous terminals in industrial cyber-physical systems,” International Journal of Distributed Sensor Networks, vol. 13, Article ID 1550147717729640, 2017.

  • R. Datla, Y. Mai, and N. Wang, Neighbor coverage multipath DSDV, California State University, Fresno Fresno. CA. USA, 2018.

  • N. Muruganantham and H. El-Ocla, “Routing using genetic algorithm in a wireless sensor network,” Wireless Personal Communications, vol. 111, no. 4, pp. 2703–2732, 2020.

  • S. V. Purkar and R. S. Deshpande, “Energy-efficient clustering protocol to enhance the performance of heterogeneous wireless sensor network: EECPEP-HWSN,” Journal of Computer Networks and Communications, vol. 2018, no. 2078627, pp. 1–12, 2018.

  • K. Jaiswal and V. Anand, EOMR: an energy-efficient optimal multi-path routing. Wireless Personal Communications, Springer Science+Business Media, LLC, part of Springer Nature, 2019.

  • T. Qiu, R. Qiao, M. Han, A. K. Sangaiah, and I. Lee, “A lifetime-enhanced data collecting scheme for the Internet of things,” IEEE Communications Magazine, vol. 55, no. 11, pp. 132–137, 2017

  • M. M. Warrier and A. Kumar, “An energy-efficient approach for routing in wireless sensor networks,” Procedia Technology, vol. 25, pp. 520–527, 2016.

  • P. Maratha, K. Gupta, and P. Kuila, “Energy balanced delay aware multi-path routing using particle swarm optimization in wireless sensor networks,” International Journal of Sensor Networks, vol. 35, no. 1, pp. 10–22, 2021.

  • A. Sajedi, V. Derhami, L. Mohammad, and A. Mohammad, “Energy-aware multicast routing in manet based on particle swarm optimization,” Procedia Technology, vol. 1, pp. 434– 438, 2012.

  • F. L. Benmansour and N. Labraoui, “A comprehensive review on swarm intelligence-based routing protocols in wireless multimedia sensor networks,” International Journal of Wireless Information Networks, vol. 28, no. 2, pp. 175–198, 2021.

  • B. Moussaoui, S. Djahel, M. Smati, and J. Murphy, “A cross-layer approach for efficient multimedia data dissemination in VANETs,” Veh. Commun., vol. 9, no. May, pp. 127–134, 2017, doi: 10.1016/j.vehcom.2017.05.002

  • Bilgin, Z., Khan, B. (2010). A dynamic route optimization mechanism for AODV in MANETs. In 2010 IEEE international conference on communications. doi: 10.1109/icc.2010.5502381.

  • Yen, Y.-S., Chang, H.-C., Chang, R.-S., & Chao, H.-C. (2010). Routing with adaptive path and limited flooding for mobile ad hoc networks. Computers & Electrical Engineering, 36, 280–290. doi:10.1016/j. compeleceng.2009.03.002.

  • P.Agarkar,M.chawhan,R.Nawkhare, D.Singh, N.Giradkar, P.Patil “ An Efficient Restricted Flooding Based Route Discovery (RFBRD) Scheme for AODV Routing,” International Journal of Computer Networks and Applications (IJCNA), vol. 35, no. 5, pp. 792–805, 2023

  • G. Mujica, J. Portilla, and T. Riesgo, “Performance evaluation of an AODV-based routing protocol implementation by using a novel in-field WSN diagnosis tool,” Microprocessors and Microsystems, vol. 39, no. 8, pp. 920–938, 2015.

  • J. Kennedy and R. Eberhart, ‘‘Particle swarm optimization,’’ in Proc. Int. Conf. Neural Netw. (ICNN), vol. 4, Nov. 1995, pp. 1942–1948.

  • J. Kennedy, ‘‘Swarm intelligence,’’ in Handbook of Nature-Inspired and Innovative Computing. Springer, 2006, pp. 187–219.

  • D. W. van der Merwe and A. P. Engelbrecht, ‘‘Data clustering using particle swarm optimization,’’ in Proc. Congr. Evol. Comput. (CEC), 2003, pp. 215–220.


Abstract Views: 111

PDF Views: 1




  • Optimizing Ad-Hoc Routing Protocols in WSN to Enhance QoS Parameters Using Evolutionary Computation Algorithms

Abstract Views: 111  |  PDF Views: 1

Authors

Rahul Nawkhare
School Of Electronics and Electrical Engineering, Lovely Professional University, Phagwara, Punjab, India
Daljeet Singh
Faculty of Medicine, Research Unit of Health Sciences and Technology, University of Oulu, Finland

Abstract


Wireless Sensor Networks (WSNs) have garnered considerable attention within the research community focused on fraternity due to their extensive utilization in healthcare, environmental surveillance, disaster avoidance, farming methods, wildfire detection, and other practical applications. Enormous applications have been developed in the Internet of Things (IoT) era resulting in an ever-increasing number of connected WSN devices. As a result, WSNs consistently face challenges in delivering the required quality of service (QoS) affecting the average end-to-end delay, energy utilization, and packet loss throughout the transmission process. An efficient routing protocol must be designed to address these constraints and improve the operational efficiency of WSNs regarding Quality of Service (QoS) metrics. Motivated by these challenges, this paper presents an advanced routing algorithm by integrating optimization in the AODV routing protocol for ad hoc networks employing Particle Swarm Optimization (PSO). The proposed multipath protocol is termed the EPSO-AODV algorithm. The proposed algorithm is assessed through numerous simulations carried out with varied system setups and parameters. Additionally, the efficiency of the proposed protocol is assessed in comparison to conventional routing protocols including AODV, Dynamic Source Routing (DSR), Destination-Sequenced Distance Vector (DSDV), and Optimized Link State Routing (OLSR) protocols. It is observed from the experimental findings that the proposed approach outperforms existing algorithms and offers several benefits including better energy efficiency, ensuring high packet delivery ratio, throughput, and minimal end-to-end delay delay, reduced normalization load. The proposed protocol efficiently distributes energy usage to enhance throughput and enhance the performance of wireless sensor networks. As per the simulation results, the packet delivery ratio has improved from 81.58% to 91.60% whereas the throughput is observed to be 36.32 kbps for conventional AODV and 74.21 kbps for the proposed algorithm. The routing overhead is lowered by approximately 40% and the AE2E delay was found to be 0.04 lower in comparison to AODV. The residual energy in the context of the EPSO-AODV proposal is less (4981 Joules) than AODV (6344 Joules) which proves the superior efficiency of the proposed algorithm.

Keywords


Ad Hoc On-Demand Distance Vector Routing, Particle Swarm Optimization, Machine Learning, Network Lifespan, Energy Balancing, Localization, Clustering, Routing Overhead, Throughput, End-to-End Delay.

References





DOI: https://doi.org/10.22247/ijcna%2F2024%2F224448