Abstract-Selecting routes of higher throughput in wireless mesh networks plays an important hole and has been considered in several works. Previous studies establish routing metrics that do not consider the possibility of using new technologies such as Software Defined Radio (SDR) that allows transmission channels of different widths. In wireless mesh networks equipped with routers that apply this technology, it is possible to improve the network capacity by transmitting multiple parallel flows in narrower channels. Another improvement related to the use of such approach is the reduction in the number of hops of an endto-end communication. In this work, we employ these possibilities to develop a simulation model for wireless mesh networks that are capable to perform channel width adaptation. Additionally, we extend the traditional Medium Time Metric (MTM), propose the B-MTM (Burst per MTM) metric and an algorithm that jointly select routes with higher throughput in wireless mesh networks.
Increasing the capacity of wireless mesh networks has motivated numerous studies. In this context, the cross-layer optimization techniques involving joint use of routing and link scheduling are able to provide better capacity improvements. Most works in the literature propose linear programming models to combine both mechanisms. However, this approach has high computational complexity and cannot be extended to large-scale networks. Alternatively, algorithmic solutions are less complex and can obtain capacity values close to the optimal. Thus, we propose the REUSE algorithm, which combines routing and link scheduling and aims to increase throughput capacity in wireless mesh networks. Through simulations, the performance of the proposal is compared to a developed linear programming model, which provides optimal results, and to other proposed mechanisms in the literature that also deal with the problem algorithmically. We observed higher values of capacity in favor of our proposal when compared to the benchmark algorithms.
Considering the variation of the received signal strength indicator (RSSI) in wireless networks, the objective of this study is to investigate and propose a method of indoor localization in order to improve the accuracy of localization that is compromised by RSSI variation. For this, quartile analysis is used for data pre-processing and the k-nearest neighbors (kNN) classifier is used for localization. In addition to the tests in a real environment, simulations were performed, varying many parameters related to the proposed method and the environment. In the real environment with reference points of 1.284 density per unit area (RPs/m2), the method presents zero-mean error in the localization in test points (TPs) coinciding with the RPs. In the simulated environment with a density of 0.327 RPs/m2, a mean error of 0.490 m for the localization of random TPs was achieved. These results are important contributions and allow us to conclude that the method is promising for locating objects in indoor environments.
Resumo-Este artigo apresenta uma métrica de roteamento para redes de satélites de órbita baixa (Low Earth Orbit-LEO), de forma que permita aumentar a demanda de tráfego atendida, observando o consumo energético das baterias dos satélites que estão na área de eclipse. Nossa proposta aproveita o movimento dos satélites em suas órbitas para favorecer o tráfego de dados entre satélites que estão expostos à luz solar, em oposição aos satélites que estão na área de eclipse, a fim de reduzir a profundidade de descarga das baterias. Além disso, também são observadas as capacidades dos enlaces entre os satélites adjacentes, com o objetivo de evitar congestionamentos na rede, aumentando o escoamento de tráfego de dados. Os resultados das simulações demonstraram aumento de demanda em 14%, na situação com 100 fontes com taxa de 1,5Mbps, quando comparado a métrica LASER [1], sem aumentar o consumo energético das baterias dos satélites em área de eclipse.
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