SDN-based hierarchically enhanced regularized routing optimization algorithm in space–ground–sea integrated network

Link-Aware Routing in Multi-Tier LEO Mega-Constellations

In multi-tier low-Earth orbit (LEO) mega-constellations, the mobility of satellites across different orbital altitudes leads to dynamic changes in network topology and inter-satellite link (ISL) states, including ISL duration and capacity. These changes often result in unstable connectivity and disrupted end-to-end data transmission. To address this issue, we formulate a routing optimization problem to determine the optimal ISL path between two end users by maximizing the average ISL utility, considering both ISL duration and capacity. To solve this problem, we propose a routing method that first prunes unstable ISLs using a graph neural network (GNN) and then selects optimal end-to-end paths on the pruned graph using a heuristic routing algorithm. Simulation results demonstrate that the proposed algorithm achieves higher throughput and a lower packet loss rate compared to benchmark methods.

GT-PAR: Graph Transformer-Aided Traffic Prediction and Adaptive Routing for Dynamic LEO Satellite Networks

Low Earth Orbit (LEO) satellites are essential for 6G non-terrestrial networks due to their global coverage and low-latency communication. However, the highly dynamic topology and uneven traffic distribution cause routing inefficiencies. This letter proposes a Graph Transformer–aided Traffic Prediction and Adaptive Routing (GT-PAR) scheme to capture topology-dependent spatial coupling and long-range link-utilization dynamics. The ground segment periodically broadcasts lightweight link-utilization predictions, and the satellites select the next routing hops using the congestion-aware cost analyzed in Lemmas 1 and 2. The simulation results show that GT-PAR can significantly reduce the packet loss and end-to-end delay when compared with representative routing schemes.

Event Based Routing Scheme for FANET

Simulation results demonstrate that the proposed protocol significantly minimizes traffic loss and guarantees a stable topology update time independent of timeout configurations, effectively maximizing network efficiency in multi-hop drone swarm operations.

An Optimization of Multiple Route Selection in Flying Ad Hoc Network

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.

DELAY REDUCTION IN SD-WAN BY USING BUCKET SORT ALGORITHM

An enhanced seagull optimization algorithm for multi-dimensional internet of things routing under heterogeneity and mobility constraints

The rapid growth of the Internet of Things (IoT) has created several complications for routing and parameter optimization, especially in heterogeneous networks characterized by mobility and large scale. Poor routing algorithms will result in energy waste, congestion, high latency, and unreliable packet delivery. As such, this limits the efficacy and viability of IoT for deploying applications such as smart cities and industrial automation. To address these challenges, this paper proposes an Enhanced Seagull Optimization Algorithm (ESOA) for multi-objective IoT routing optimization. Inspired by the migration and attack behaviors of seagulls, ESOA integrates collaborative subgrouping, simulated generation, and random rearrangement mechanisms to achieve an effective balance between exploration and exploitation. The proposed algorithm simultaneously optimizes critical network performance metrics, including energy consumption, traffic congestion, transmission delay, and packet loss. Extensive simulations in heterogeneous and mobility-aware scenarios demonstrate that ESOA significantly improves network lifetime, reduces routing cost, minimizes congestion and end-to-end delay, enhances packet delivery performance, and preserves higher residual energy than several state-of-the-art bio-inspired and metaheuristic optimization algorithms.