Semantic reasoning in cognitive networks for heterogeneous wireless mesh systems

The next generation of wireless networks is expected to provide not only higher bandwidths anywhere and at any time but also ubiquitous communication using different network types. However, several important issues including routing, self-configuration, device management, and context awareness have to be considered before this vision becomes reality. This paper proposes a novel cognitive network framework for heterogeneous wireless mesh systems to abstract the network control system from the infrastructure by introducing a layer that separates the management of different radio access networks from the data transmission. This approach simplifies the process of managing and optimizing the networks by using extendable smart middleware that automatically manages, configures, and optimizes the network performance. The proposed cognitive network framework, called FuzzOnto, is based on a novel approach that employs ontologies and fuzzy reasoning to facilitate the dynamic addition of new network types to the heterogeneous network. The novelty is in using semantic reasoning with cross-layer parameters from heterogeneous network architectures to manage and optimize the performance of the networks. The concept is demonstrated through the use of three network architectures: 1) wireless mesh network; 2) long-term evolution (LTE) cellular network; and 3) vehicular ad hoc network (VANET). These networks utilize nonoverlapped frequency bands and can operate simultaneously with no interference. The proposed heterogeneous network was evaluated using ns-3 network simulation software. The simulation results were compared with those produced by other networks that utilize multiple transmission devices. The results showed that the heterogeneous network outperformed the benchmark networks in both urban and VANET scenarios by up to 70% of the network throughput, even when the LTE network utilized a high bandwidth

GERENCIAMENTO DE SERVIÇOS E GOVERNANÇA DE TI MODELAGEM DE UM PROCESSO DE GERENCIAMENTO DE CONFIGURAÇÃO UMA ABORDAGEM USANDO ONTOLOGIAS DE FUNDAMENTAÇÃO

A tecnologia da informação (TI) vem se tornando essencial para as organizações. Nesse contexto, o gerenciamento de TI, vem evoluindo para incluir gerenciamento de serviços e governança de TI, apontando para paradigmas de gerenciamento de TI orientado ao negócio. Dessa forma, o alinhamento entre a TI e o negócio tem sido considerado como um dos fatores preponderantes para a efetividade de tais paradigmas. Em adicional, são mencionadas as contribuições provenientes da automação das atividades de gerenciamento.Acompanhando essa evolução, o gerenciamento de configuração desempenha um papel fundamental, fornecendo informações precisas da TI a todos os envolvidos no gerenciamento. Todavia, em função desse estreito relacionamento com todos os agentes envolvidos no gerenciamento, a interoperabilidade entre esses agentes tem sido caracterizada como um dos principais desafios de pesquisa em gerenciamento de redes e serviços.Nesse sentido, o uso de ontologias, em especial, ontologias fundacionais, tem sido indicado como uma maneira promissora de se obter interoperabilidade semântica no domínio de gerenciamento de configuração, uma vez que elas expressam o significado dos conceitos do domínio, bem como os relacionamentos existentes entre eles, de forma clara e explícita. Além disso, ontologias permitem que esse significado seja definido em um formato legível por máquinas, tornando o conhecimento compartilhado por humanos e também por sistemas computacionais, permitindo a automação de processos.Assim, este trabalho apresenta uma proposta de modelagem conceitual do domínio de gerenciamento de configuração, no contexto do gerenciamento de serviços e governança de TI, baseada em ontologias fundacionais. O propósito dessa ontologia é prover um modelo conceitual desse domínio, comprometido em maximizar a expressividade, a clareza e a veracidade dos conceitos pertencentes a ele. Ademais, este trabalho apresenta uma proposta de modelo de implementação, derivado do modelo conceitual desenvolvido. O objetivo é realizar uma prova de conceito da ontologia e também demonstrar como essa ontologia pode apoiar as atividades de gerenciamento de maneira automatizada

Cognitive network framework for heterogeneous wireless mesh systems

Heterogeneous wireless mesh networks (WMN) provide an opportunity to secure higher network capacity, wider coverage and higher quality of service (QoS). However, heterogeneous systems are complex to configure because of the high diversity of associated devices and resources. This thesis introduces a novel cognitive network framework that allows the integration of WMNs with long-term evolution (LTE) networks so that none of the overlapped frequency bands are used. The framework consists of three novel systems: the QoS metrics management system, the heterogeneous network management system and the routing decision-making system. The novelty of the QoS metrics management system is that it introduces a new routing metric for multi-hop wireless networks by developing a new rate adaptation algorithm. This system directly addresses the interference between neighbouring nodes, which has not been addressed in previous research on rate adaptation for WMN. The results indicated that there was a significant improvement in the system throughput by as much as to 90%. The routing decision-making system introduces two novel methods to select the transmission technology in heterogeneous nodes: the cognitive heterogeneous routing (CHR) system and the semantic reasoning system. The CHR method is used to develop a novel reinforcement learning algorithm to optimise the selection of transmission technology on wireless heterogeneous nodes by learning from previous actions. The semantic reasoning method uses ontologies and fuzzy-based semantic reasoning to facilitate the dynamic addition of new network types to the heterogeneous network. The simulation results showed that the heterogeneous network outperformed the benchmark networks by up to 200% of the network throughput