Topology preservation and control approach for interference aware non-overlapping channel assignment in wireless mesh networks

The Wireless Mesh Networks (WMN) has attracted significant interests due to their fast and inexpensive deployment and the ability to provide flexible and ubiquitous internet access. A key challenge to deploy the WMN is the interference problem between the links. The interference results in three problems of limited throughput, capacity and fairness of the WMN. The topology preservation strategy is used in this research to improve the throughput and address the problems of link failure and partitioning of the WMN. However, the existing channel assignment algorithms, based on the topology preservation strategy, result in high interference. Thus, there is a need to improve the network throughput by using the topology preservation strategy while the network connectivity is maintained. The problems of fairness and network capacity in the dense networks are due to limited available resources in WMN. Hence, efficient exploitation of the available resources increases the concurrent transmission between the links and improves the network performance. Firstly, the thesis proposes a Topology Preservation for Low Interference Channel Assignment (TLCA) algorithm to mitigate the impact of interference based on the topology preservation strategy. Secondly, it proposes the Max-flow based on Topology Control Channel Assignment (MTCA) algorithm to improve the network capacity by removing useless links from the original topology. Thirdly, the proposed Fairness Distribution of the Non-Overlapping Channels (FNOC) algorithm improves the fairness of the WMN through an equitable distribution of the non-overlapping channels between the wireless links. The F-NOC is based on the Differential Evolution optimization algorithm. The numerical and simulation results indicate that the proposed algorithms perform better compared to Connected Low Interference Channel Assignment algorithm (CLICA) in terms of network capacity (19%), fractional network interference (80%) and network throughput (28.6%). In conclusion, the proposed algorithms achieved higher throughput, better network capacity and lower interference compared to previous algorithms

Dynamical systems : mathematical and numerical approaches

Proceedings of the 13th Conference „Dynamical Systems - Theory and Applications" summarize 164 and the Springer Proceedings summarize 60 best papers of university teachers and students, researchers and engineers from whole the world. The papers were chosen by the International Scientific Committee from 315 papers submitted to the conference. The reader thus obtains an overview of the recent developments of dynamical systems and can study the most progressive tendencies in this field of science

Vibration Sensor-Based Bearing Fault Diagnosis Using Ellipsoid-ARTMAP and Differential Evolution Algorithms

Effective fault classification of rolling element bearings provides an important basis for ensuring safe operation of rotating machinery. In this paper, a novel vibration sensor-based fault diagnosis method using an Ellipsoid-ARTMAP network (EAM) and a differential evolution (DE) algorithm is proposed. The original features are firstly extracted from vibration signals based on wavelet packet decomposition. Then, a minimum-redundancy maximum-relevancy algorithm is introduced to select the most prominent features so as to decrease feature dimensions. Finally, a DE-based EAM (DE-EAM) classifier is constructed to realize the fault diagnosis. The major characteristic of EAM is that the sample distribution of each category is realized by using a hyper-ellipsoid node and smoothing operation algorithm. Therefore, it can depict the decision boundary of disperse samples accurately and effectively avoid over-fitting phenomena. To optimize EAM network parameters, the DE algorithm is presented and two objectives, including both classification accuracy and nodes number, are simultaneously introduced as the fitness functions. Meanwhile, an exponential criterion is proposed to realize final selection of the optimal parameters. To prove the effectiveness of the proposed method, the vibration signals of four types of rolling element bearings under different loads were collected. Moreover, to improve the robustness of the classifier evaluation, a two-fold cross validation scheme is adopted and the order of feature samples is randomly arranged ten times within each fold. The results show that DE-EAM classifier can recognize the fault categories of the rolling element bearings reliably and accurately

EUROSENSORS XVII : book of abstracts

Fundação Calouste Gulbenkien (FCG).Fundação para a Ciência e a Tecnologia (FCT)