Jul 2026· International Scientific Conference on Information, Communication and Energy Systems and Technologies· pp. 385-390· 0 citations· 15 references
Abstract
Bio-inspired routing algorithms have gained significant attention as effective approaches for solving complex optimization problems in modern communication networks. Drawing inspiration from collective behaviors in nature, these methods have been widely applied in decentralized environments such as wireless sensor and mobile ad hoc networks. However, their adoption within Software-Defined Networks (SDN) remains relatively limited. This paper provides a systematic and comparative review of bio-inspired routing algorithms with an emphasis on their applicability in SDN. A novel taxonomy is proposed to classify these algorithms according to their behavioral characteristics and their suitability for centralized control. In addition, a unified evaluation framework is introduced to enable consistent comparison among key approaches, including Ant Colony Optimization, Particle Swarm Optimization, Bee Colony Optimization, and Grey Wolf Optimization, based on performance criteria such as convergence, scalability, and quality of service. The study also includes an SDN-oriented analysis, examining the impact of centralized control on the behavior and performance of these algorithms. Finally, the paper outlines key research challenges, highlighting the importance of real-time optimization, hybrid methodologies, and integration with intelligent control mechanisms.
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
The adoption of a new communication paradigm is getting attention in the research world, where Flying Ad Hoc Networks (FANETs) have been deemed a viable approach for supporting coordinated operations of multiple Unmanned Aerial Vehicles (UAVs) in situations characterized by dynamic environments and the absence of infrastructure. Taking into consideration these drawbacks, in this paper, a novel and up-to-date AI-Based Mobility and Topology Management Framework for Flying Ad Hoc Networks via Hybrid Bio-Inspired Optimization is proposed. The proposed systems combine a Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO) inspired model, introducing a novel hybrid model, with Artificial Intelligence techniques to provide a dynamic framework for optimizing UAV mobility patterns, topology formation, and communication paths within the proposed framework. Predictive mobility analysis using AI to make networks more adaptable and minimize topology changes. In addition, the hybrid optimization method will optimize the routing efficiency, reduce the communication overhead, and increase the packet delivery efficiency between nodes in the highly dynamic FANET environment. Results of experimental analysis prove that the proposed scheme has a better PDR of 96.4%, lower EED or end-to-end delay of 31%, and better topology stability that performs better than the traditional mobility management approaches with respect to reducing energy consumption.
Anshu Vashisth, Gagandeep Kaur, Ruhi Saxena et al.· 2026 7th International Confe...· 0 citations
Wireless sensor networks (WSNs), fundamental building block of IoT, are subject to several constraints because of the finite non-rechargeable energy resources available in the nodes. The selection of Cluster Head (CH) plays a critical role in determining the energy balance in a network. Conventional methods such as the LEACH algorithm choose CH randomly with a probability mechanism that might lead to choosing weak nodes as CHs and thereby fail prematurely. The biologically -inspired optimization methods, such as PSO and CSO, help to enhance CH selection using a global approach. However, these methods suffer from the following three major shortcomings: 1) random switching between the exploration and exploitation stages, 2) lack of intelligence during the formation of clusters, and 3) growing exponentially complex search space of CHs.This study proposes an Intelligent and Interoperable Cat Swarm Optimizer (2I-CSO), a protocol designed to address these limitations simultaneously. 2I-CSO also introduces an interoperable configuration mechanism based on LEACH’s hierarchical architecture, where the Base Station maintains a centralized energy configuration table shared with Cluster Heads and member nodes, ensuring network-wide parameter consistency and enabling the intelligent stopping mechanism. Experiments conducted on five well-known TSPLIB test cases and WSN simulations demonstrate that 2I-CSO outperforms individual metaheuristics. Simulation results on a custom web-based platform further show that 2I-CSO achieves faster convergence, lower computational cost, and competitive network lifetime compared to standard CSO and the Emperor Penguin Optimizer (EPO). To the best of our knowledge, the proposed intelligent stopping condition is the first introduced for bio-inspired WSN clustering protocols.
Oumaima Hassan, M. Riffi· International Journal of Adv...· 0 citations
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.
S. John, J. Thangaraj, D. Santhakumar· International Conference on...· 0 citations
Wireless Sensor Networks (WSNs) are widely used in environmental monitoring, smart agriculture, and Internet of Things applications, but their performance is constrained by limited battery capacity, uneven energy consumption, and inefficient routing. To address these issues, this paper proposes THGCDTR-RP, an energy-efficient clustering and routing protocol that integrates Grey Wolf Optimizer, Cheetah Optimizer, and Differential Evolution for cluster-head (CH) selection. The proposed CH selection strategy jointly considers residual energy, node centrality, intra-cluster compactness, and cluster-size balance, while an energy-aware minimum spanning tree mechanism constructs multi-hop routing paths among CHs and the base station (BS). Extensive MATLAB-based simulations under different network sizes, node densities, and BS locations show that THGCDTR-RP consistently outperforms LEACH, LPSO, LGWO, WOA-P, and LACO. For example, in the 50×50\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$50 \times 50$$\end{document} network size, THGCDTR-RP increases the number of packets received at the BS by 144.4%, 83.3%, 89.7%, 77.4%, and 93.1% compared with LEACH, LPSO, LACO, LGWO, and WOA-P, respectively. It also improves the first-node-death round by 271.5%, 71.9%, 78.2%, 65.2%, and 103.0%, and extends the all-node-death round by 17.78%, 44.46%, 46.63%, 35.22%, and 51.17% over the same baselines, respectively.
Xuan Yang, Jiaqi Yan, Desheng Wang et al.· Journal of King Saud Univers...· 0 citations
Simulation results indicate that HOA-MEPFL-CLCT-RP outperforms existing models in terms of Packet Delivery Ratio (PDR), energy efficiency, End-to-End Delay (E2D), and routing overhead.
Shaleena H, Sumangala K· International journal of com...· 0 citations
