## 2 Related Work
Specifically, Importance-TOPSIS adaptation to dynamic variations in criteria remains limited in a highly variable environment likesatellite networks, where factors such as upload speed, download speed, and latency fluctuate rapidly. A multi-attribute decision handover scheme is proposed in [14] for LEO mobile satellite networks, aiming to reduce handover frequency and enhance data stream stability. The scheme considers three factors: receiving signal strength, remaining service time, and satellite idle channels, using a multi-attribute decision algorithm to make handover decisions. Simulations on the Iridium satellite network show that the scheme reduces handover frequency, lowers channel utilization variance, and improves signal strength compared to other schemes. However, the study does not address the rapid fluctuations in key performance criteria, such as signal strength and satellite availability, which are characteristic of LEO satellite networks. The approach would benefit from a sensitivity analysis to account for these dynamic variations. Additionally, integrating a decision-making method like TOPSIS could provide a more adaptable solution, better handling the dynamic nature of LEO satellite networks and ensuring more robust performance in varying conditions. Authors in [4] address network congestion in LEO satellite networks by proposing a Parameter-Adaptive Signal-to-Interference-plus-Noise Ratio (SINR)-based Multi-Attribute Decision (PASMAD) algorithm for access and handover in GEO/LEO heterogeneous satellite networks. The algorithm considers SINR, required bandwidth, traffic cost, and satellite load to ensure QoS for multimedia services. It adapts parameters based on traffic distribution predictions to balance network loads, outperforming traditional Received Signal Strength (RSS)-based algorithms in terms of QoS and data rates, as well as improving system performance compared to fixed-parameter algorithms. However, the paper does not account for the dynamic variations in performance criteria, such as SINR or bandwidth, that are inherent in rapidly moving LEO satellites. It also lacks a sensitivity analysis of how fluctuations in these factors might impact the handover process. The work in [5] proposes a user-centric handover scheme for ultra-dense LEO satellite networks that buffers downlink data across multiple satellites, ensuring seamless handover and improved communication quality. While the approach outperforms traditional schemes in throughput, delay, and latency, it does not address the dynamic fluctuations in key performance metrics like bandwidth and latency due to satellite movement. Additionally, there is no sensitivity analysis of how these variations affect handover performance. Integrating a method like TOPSIS could improve decision-making by accounting for the dynamic nature of LEO satellite networks. Table 1 summarizes the related literature, the methodology applied in these works, the main advantages and limitations of the technique used, and the domain of application. In this paper, we investigate the use of TOPSIS-based handover algorithms in LEO satellite networks, an environment where decision making criteria fluctuate rapidly with the movement of satellites. We analyze their robustness to dynamic variations in performance criteria by conducting a sensitivity analysis on the following criteria: upload speed, download speed, ping, and packet loss. To the best of our knowledge, at the time of writing, the use of TOPSIS-based handover algorithms in the context of sensitivity analysis in LEO satellite networks remains underexplored in the current literature.