Extensive evaluation of R-SCRAW: a recursive spectral clustering algorithm for intelligent grouping in dense wireless networks
Year: 2026
Type: article
Abstract: Dense wireless network (DWN) scenarios are more likely to suffer from the problem of hidden terminals and consequently an increase in collisions, as they support a large number of stations in a limited geographic area. While the restricted access window (RAW) mechanism introduced by IEEE 802.11ah reduces the levels of contention by separating nodes into groups with exclusive transmission slots, it does not take hidden terminals into account and, therefore, is still susceptible to collisions caused by them. Recently, a spectral clustering for RAW (SCRAW) algorithm showed potential in finding more efficient RAW groups to reduce hidden terminals, increasing DWN performance. In runtime performance experiments, we observed that SCRAW exhibits a long execution time, which may make its use impractical. This paper is an extension of a previous work that proposed a more efficient SCRAW algorithm, called Recursive SCRAW (R-SCRAW). Our proposal uses a recursive divide and conquer strategy, replacing the full spectral clustering with several executions of a simpler binary split. Simulation results show that the R-SCRAW still significantly outperforms the standard IEEE 802.11ah grouping in terms of collision rate, average packet delay, aggregate network throughput, and number of hidden terminals, while performing just as well as SCRAW but with much lower execution time. Simulations were also performed to compare the behavior of R-SCRAW in scenarios with and without mobility. In scenarios with mobility, the occurrence of hidden terminals increases over time with the distance between stations in each group, decreasing network performance in terms of collision rate and delay compared to simulations without considering mobility. However, even with the performance degradation caused by mobility, R-SCRAW still manages to outperform the standard IEEE 802.11ah grouping under non-mobility conditions. The performance of R-SCRAW was also compared with other grouping algorithms, all under mobility conditions. R-SCRAW presented similar results to K-Means in terms of collision rate, delay, and throughput metrics, and slightly better than the HMR algorithm in terms of throughput, since the initial grouping in HMR is arbitrarily formed.
Source: Annals of Telecommunications
Language: English
Subfield: Computer Networks and Communications
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $2,890
OpenAlex ID: W7225758695