A Novel Load Balancing Scheme for Satellite IoT Networks Based on Spatial–Temporal Distribution of Users and Advanced Genetic Algorithms

Abstract

Satellite IoT networks (S-IoT-N), which have been a hot issue regarding the next generation of communication, are quite important for the coming era of digital twins and the metaverse because of their performance in sensing and monitoring anywhere, anytime, and anyway, in more dimensions. However, this will cause communication links to face greater traffic loads. Satellite internet networks (SIN) are considered the most possible evolution road, possessing characteristics of many satellites, such as low earth orbit (LEO), the Ku/Ka frequency, and a high data rate. Existing research on load balancing schemes for satellite networks cannot solve the problems of low efficiency under conditions of extremely non-uniform distribution of users (DoU) and dynamic density variances. Therefore, this paper proposes a novel load balancing scheme of adjacent beams for S-IoT-N based on the modeling of spatial–temporal DoU and advanced GA. In our scheme, the PDF of the DoU in the direction of movement of the SSP’s trajectory was modeled first, which provided a multi-directional constraint for the non-uniform distribution of users in S-IoT-N. Fully considering the prior periodicity of satellite movement and the similarity of DoU in different areas, we proposed an adaptive inheritance iteration to optimize the crossover factor and mutation factor for GA for the first time. Based on the proposed improved GA, we obtained the optimal scheme of load balancing under the conditions of the adaptation from the local balancing scheme to global balancing, and a selection of Ser-Beams to access. Finally, the simulations show that the proposed method can improve the average throughput by 3% under specific conditions and improve processing efficiency by 30% on average.

Multiple Channel LoRa-to-LEO Scheduling for Direct-to-Satellite IoT

“…In addition, the authors concluded that trajectory-based policies can duplicate the amount of served IoT nodes. Departing from LoRa-based networks, a resource allocation scheme based on genetic algorithms for load balancing was provided in [23]. Therein, problems such as low efficiency in extremely non-uniform user distribution conditions and density variations were detected.…”

“…Furthermore, unlike the policies proposed in [16], our method generates a collision-free channel access strategy that can be implemented with low computational complexity, so it can even be run on-board the satellite if necessary. Finally, concerning [23], [24], our proposed approach is tailored for LoRa-based DtS-IoT networks, with low computational complexity and high effectiveness despite the dynamics of these scenarios.…”