Status: Alfa -- Version: 0.1
GeTS introduces a new evolutionary model for solving the SINR scheduling problem. A population of individuals (each representing a candidate schedule to solve the problem) evolves over generations. To do so, we designed crossover and mutation mechanisms that allow the efficient exploration and exploitation of the search space. The objective is to find the schedules of minimum size, i.e., with the minimum possible number time of slots.
SINR GeTS was developed using Python 3 programming language, including the non-native library NumPy.
- CLI interface
- Results report generator
- GUI interface
Contact us towards git issues requests or by the e-mail vinicius@inf.ufsm.br.
Vinicius Fulber-Garcia (UFPR - Brazil)
Fábio Engel (UTFPR - Brazil)
Elias Procópio Duarte Junior (UFPR - Brazil)
F. Engel, V. Fulber-Garcia and E. P. Duarte Jr., "Uma Estratégia Bioinspirada para Escalonamento em Redes Sem Fio sob o Modelo SINR", in XXVII Workshop de Gerência e Operação de Redes e Serviços (WGRS), Fortaleza, Brazil, 2022.
Fulber-Garcia, Vinicius, Fábio Engel, and Elias P. Duarte Jr. "A bioinspired scheduling strategy for dense wireless networks under the sinr model." International Conference on Intelligent Systems Design and Applications (ISDA). Springer Nature Switzerland, 2022.
Fulber-Garcia, Vinicius, Fábio Engel, and Elias P. Duarte. "A genetic scheduling strategy with spatial reuse for dense wireless networks." International Journal of Hybrid Intelligent Systems (IJHIS).