Aug 2026· International Conference on Circuit, Power and Computing Technologies· pp. 1210-1215· 0 citations· 14 references
Abstract
Mobile Ad Hoc Networks (MANETs) are characterized by dynamic topology, limited node energy, and frequent link failures, which collectively pose significant challenges to reliable and energy-stable routing. Existing routing protocols and bio-inspired optimization techniques often rely on static parameter tuning, suffer from premature convergence, and lack adaptive mechanisms to preserve route diversity under high mobility conditions. These limitations lead to increased energy consumption, frequent route breakages, and degraded network lifetime. To address these issues, this paper proposes an Immune-Regulated Swarm Intelligence (IRIS)-based routing framework designed to achieve energy-stable and resilient data transmission in MANETs. The proposed approach integrates swarm-based multi-path exploration with fuzzy logic-based route fitness evaluation and an artificial immune regulation mechanism that dynamically suppresses weak routes while reinforcing high-affinity paths. Immune memory is further employed to prevent repeated selection of unstable routes, enabling rapid recovery from link failures. The performance of the proposed routing protocol is evaluated using extensive simulations conducted in the NS-3 environment under varying node mobility and traffic conditions. The proposed framework supports Sustainable Development Goals SDG 7 (Affordable and Clean Energy) and SDG 9 (Industry, Innovation and Infrastructure) by promoting energy-efficient and resilient wireless communication systems. Experimental results demonstrate that the proposed method achieves improvements of up to 12-18% in packet delivery ratio, 15-22% reduction in energy consumption, and significantly lower end-to-end delay compared to conventional AODV, PSO-based, and ACO-based routing protocols.
This study proposes a Fault-Tolerant Backup Cluster Head with Ant Colony Optimization (FT-BKCH-ACO) approach, offering a scalable and energy-efficient solution for large-scale sensor networks.
Manisha Chandrakar, A. Hasan· International Journal of Wir...· 0 citations
This paper proposes ASGRR (Adaptive Swarm-Guided Graph Policy Routing), a novel hybrid routing framework that integrates Message Passing Neural Networks, Policy Gradient Reinforcement Learning (PGRL), and the Artificial Bee Colony algorithm in a self-adaptive hybrid form.
Mehdi Hosseinzadeh, Parisa Khoshvaght, Amir Masoud Rahmani et al.· Cluster Computing· 0 citations
ThGCDTR-RP is proposed, an energy-efficient clustering and routing protocol that integrates Grey Wolf Optimizer, Cheetah Optimizer, and Differential Evolution for cluster-head (CH) selection that consistently outperforms LEACH, LPSO, LGWO, WOA-P, and LACO.
Xuan Yang, Jiaqi Yan, Desheng Wang et al.· Journal of King Saud Univers...· 0 citations
An in-depth review of energy-efficient routing protocols that have been developed for FANETs and highlights the main research challenges, such as high mobility, dynamic topology, routing overhead, scalability, and security, and discusses future research directions to design more intelligent and energy-aware routing protocols.
Ragvinder Kaur, Amit Sharma· Journal of Intelligent Decis...· 0 citations
A novel cluster-based routing protocol that integrates a Fungal Growth Optimizer for adaptive cluster head (CH) selection and a Graph Neural Network for inter-cluster routing, which demonstrates FGOGNN’s potential for deployment in real-time WSN applications, where energy efficiency and dynamic adaptability are paramount.
Huang-shui Hu, Shuo Liu, Qier Kang et al.· Symmetry· 1 citation
WSNs continue to struggle with the issue of energy usage, network lifetime, and dependable data transfer, especially in heterogeneous networks where nodes have diverse computational and energy resources. The classical clustering algorithms are usually characterized by uneven distribution of cluster-heads, inefficient routing paths, and poor responsiveness to changes in network dynamics. This article presents a new framework, OEEMLCR (Optimized Energy-Efficient Machine Learning-Based Clustering and Routing), which is a combination of Particle Swarm Optimization-based K-means clustering and Coati Optimization Algorithm-based routing and Q-learning adaptation. The suggested methodology fills in crucial gaps through the use of a dual-objective fitness function that both maximizes the compactness of space and the homogeneity of energy when forming clusters and introduces the learning technique of reinforcement to allow adaptive routing behavior under the influence of experience gained in the network. Experimental validation of heterogeneous network situations show that OEEMLCR can attain significant gains over existing protocols: network lifetime is increased 13.6% over baseline machine learning methods, cumulative packet delivery is increased 108%, energy efficiency is increased 340%. The framework operates in a stable way during 1000 rounds of the simulation and is 96.7 percent of the initial network power, far outperforming LEACH, DMHT, EDMHT, and EEMLCR in lifetime, throughput, percentage ratio of delivering packets, and the network resilience criterion.
Chinmay Charith Paladugu, R. N, Radhika G· 2026 International Conferenc...· 0 citations
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