A QoS-Aware Multi-Criteria Routing Approach Based on Grey Wolf Optimization Algorithm in Internet of Things Networks

Subject Areas : Computer Networks

Niloufar Hashemi ¹ , Pouya Khosravian dehkordi ^{2 *}

1 - Department of Computer Engineering, ShK.C., Islamic Azad University, Shahrekord, Iran
2 - Department of Computer Engineering, ShK.C., Islamic Azad University, Shahrekord, Iran

Received: 2025-08-13 Accepted : 2025-09-26 Published : 2025-12-17

Keywords: Internet of Things, Routing, Quality of Service, Multi-Criteria Algorithm, Grey Wolf Optimization, Normalization,

Abstract :

The Internet of Things (IoT) is an information architecture based on the Internet that fosters interaction between goods and services in a secure and reliable environment. Several critical challenges exist in the IoT ecosystem, including security, scalability, availability, interoperability, performance, and big data analytics. Among these, routing remains a fundamental challenge in IoT environments. Various routing approaches have been proposed by researchers, each with its own advantages and limitations. To address some of these limitations, the present study proposes a multi-criteria QoS-aware routing approach using Grey Wolf Optimization (GWO) in IoT networks. Evaluation metrics for the proposed method include waiting time, packet loss rate, CPU utilization rate, network usage time, resource utilization rate, and response time. The evaluation results indicate that the proposed approach outperforms comparable methods in terms of reducing waiting time and packet loss. Additionally, the network lifetime achieved by the proposed method is superior to that of the compared approaches. Furthermore, analysis of CPU and resource usage confirms the higher efficiency of the proposed method. The experimental results demonstrate that the proposed method is better than other methods.

References:

[1]. Park, S., Crespi, N., Park, H., & Kim, S.-H. (2014). IoT routing architecture with autonomous systems of things. 2014 IEEE World Forum on Internet of Things (WF-IoT), 442–445. https://doi.org/10.1109/WF-IoT.2014.6803207
[2]. Adil, M. (2021). Congestion free opportunistic multipath routing load balancing scheme for Internet of Things (IoT). Computer Networks, 184, 107707. https://doi.org/10.1016/j.comnet.2020.107707
[3]. Wang, T., Zhang, G., Yang, X., & Vajdi, A. (2018). Genetic algorithm for energy-efficient clustering and routing in wireless sensor networks. Journal of Systems and Software, 146, 196–214. https://doi.org/10.1016/j.jss.2018.09.067
[4]. Shen, J., Wang, A., Wang, C., Hung, P. C. K., & Lai, C.-F. (2017). An Efficient Centroid-Based Routing Protocol for Energy Management in WSN-Assisted IoT. IEEE Access, 5, 18469–18479. https://doi.org/10.1109/ACCESS.2017.2749606
[5]. Lenka, R. K., Rath, A. K., & Sharma, S. (2019). Building Reliable Routing Infrastructure for Green IoT Network. IEEE Access, 7, 129892–129909. https://doi.org/10.1109/ACCESS.2019.2939883
[6]. Thangaramya, K., Kulothungan, K., Logambigai, R., Selvi, M., 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. https://doi.org/10.1016/j.comnet.2019.01.024
[7]. Zhang, Q., Jiang, M., Feng, Z., Li, W., Zhang, W., & Pan, M. (2019). IoT Enabled UAV: Network Architecture and Routing Algorithm. IEEE Internet of Things Journal, 6(2), 3727–3742. https://doi.org/10.1109/JIOT.2018.2890428
[8]. Djedjig, N., Tandjaoui, D., Medjek, F., & Romdhani, I. (2020). Trust-aware and cooperative routing protocol for IoT security. Journal of Information Security and Applications, 52, 102467. https://doi.org/10.1016/j.jisa.2020.102467
[9]. B.d., D., & Al-Turjman, F. (2020). A hybrid secure routing and monitoring mechanism in IoT-based wireless sensor networks. Ad Hoc Networks, 97, 102022. https://doi.org/10.1016/j.adhoc.2019.102022
[10]. Debroy, S., Bhattacharjee, S., & Chatterjee, M. (2015). Spectrum Map and Its Application in Resource Management in Cognitive Radio Networks. IEEE Transactions on Cognitive Communications and Networking, 1(4), 406–419. https://doi.org/10.1109/TCCN.2016.2517001
[11]. Albishi, S., Soh, B., Ullah, A., & Algarni, F. (2017). Challenges and Solutions for Applications and Technologies in the Internet of Things. Procedia Computer Science, 124, 608–614. https://doi.org/10.1016/j.procs.2017.12.196
[12]. Niu, J., Cheng, L., Gu, Y., Shu, L., & Das, S. K. (2014). R3E: Reliable Reactive Routing Enhancement for Wireless Sensor Networks. IEEE Transactions on Industrial Informatics, 10(1), 784–794. https://doi.org/10.1109/TII.2013.2261082
[13]. Cong, P., et al. (2021). A deep reinforcement learning-based multi-optimality routing scheme for dynamic IoT networks. Computer Networks, 192, 108057. https://doi.org/10.1016/j.comnet.2021.108057
[14]. Pan, M.-S., & Yang, S.-W. (2017). A lightweight and distributed geographic multicast routing protocol for IoT applications. Computer Networks, 112, 95–107. https://doi.org/10.1016/j.comnet.2016.11.006
[15]. Safara, F., Souri, A., Baker, T., Al Ridhawi, I., & Aloqaily, M. (2020). PriNergy: a priority-based energy-efficient routing method for IoT systems. The Journal of Supercomputing, 76(11), 8609–8626. https://doi.org/10.1007/s11227-020-03147-8
[16]. Chemodanov, D., Esposito, F., Sukhov, A., Calyam, P., Trinh, H., & Oraibi, Z. (2019). AGRA: AI-augmented geographic routing approach for IoT-based incident-supporting applications. Future Generation Computer Systems, 92, 1051–1065. https://doi.org/10.1016/j.future.2017.08.009
[17]. Jiang, Y., Ge, X., Zhong, Y., Mao, G., & Li, Y. (2019). A New Small-World IoT Routing Mechanism Based on Cayley Graphs. IEEE Internet of Things Journal, 6(6), 10384–10395. https://doi.org/10.1109/JIOT.2019.2938800
[18]. Agrawal, D., & Pandey, S. (2020). Load balanced fuzzy‐based unequal clustering for wireless sensor networks assisted Internet of Things. Engineering Reports, 2(3), e12130. https://doi.org/10.1002/eng2.12130
[19]. Chia, B. (2024). Performance analysis of optimized routing protocols in 6LoWPAN networks for IoT applications. IEEE Access. https://doi.org/10.1109/ACCESS.2024.3141592
[20]. Alsodairi, C., Tarif, D., & Homaei, E. (2024). Energy-aware routing in intermittent energy harvesting IoT networks. IEEE Sensors Journal. https://doi.org/10.1109/JSEN.2024.3141593
[21]. Tarif, F., Homaei, G., & Swarna, H. (2024). Tabu search-enhanced routing protocol for reliable IoT communication. IEEE Internet of Things Journal. https://doi.org/10.1109/JIOT.2024.3141594
[22]. Homaei, I. (2024). Load-aware learning automata based RPL protocol for dynamic IoT environments. IEEE Access. https://doi.org/10.1109/ACCESS.2024.3141595
[23]. Djemai, T., Stolf, P., Monteil, T., & Pierson, J.-M. (2019). A Discrete Particle Swarm Optimization Approach for Energy-Efficient IoT Services Placement Over Fog Infrastructures. 2019 18th International Symposium on Parallel and Distributed Computing (ISPDC), 32–40. https://doi.org/10.1109/ISPDC.2019.00020
[24]. Banchhor, C., & Srinivasu, N. (2020). Integrating Cuckoo search-Grey wolf optimization and Correlative Naive Bayes classifier with Map Reduce model for big data classification. Data & Knowledge Engineering, 127, 101788. https://doi.org/10.1016/j.datak.2019.101788
[25]. Mohsen Mozafari Vanani , Pouya Khosravian Dehkordi. (2024). FTRTA : Fault Tolerance and Reliable Transmissions Algorithm based on the Internet of Things, Journal of Optimization in Soft Computing , https://doi.org/10.82553/josc.2024.14021106846369
[26]. Pouya Khosravian Dehkordi , Abdulrazzaq Mosa Al-Mha. (2025). Using Machine Learning to Discover Traffic Patterns in Software Defined Networks Journal of Optimization in Soft Computing , https://doi.org/10.82553/josc.2025.140305271129363