MHM : a novel collaborative spectrum sensing method based on markov-chains and harmonic mean for 5G networks

Ferreira, Gabriel de Carvalho; Barreto, Priscila América Solís Mendez; Rocha, Geraldo P.; Caetano, Marcos Fagundes; Karvonen, Heikki; Vartiainen, Johanna

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Campo DC	Valor	Idioma
dc.contributor.author	Ferreira, Gabriel de Carvalho	-
dc.contributor.author	Barreto, Priscila América Solís Mendez	-
dc.contributor.author	Rocha, Geraldo P.	-
dc.contributor.author	Caetano, Marcos Fagundes	-
dc.contributor.author	Karvonen, Heikki	-
dc.contributor.author	Vartiainen, Johanna	-
dc.date.accessioned	2021-12-06T17:54:08Z	-
dc.date.available	2021-12-06T17:54:08Z	-
dc.date.issued	2020-06	-
dc.identifier.citation	FERREIRA, Gabriel et al. MHM: a novel collaborative spectrum sensing method based on markov-chains and harmonic mean for 5G networks. In: 2020 IFIP NETWORKING CONFERENCE (NETWORKING), 2020, Paris. Proceedings [...]. Paris: IEEE, 2020. Disponível em: https://ieeexplore.ieee.org/document/9142763.	pt_BR
dc.identifier.uri	https://repositorio.unb.br/handle/10482/42526	-
dc.language.iso	Inglês	pt_BR
dc.publisher	IEEE	pt_BR
dc.rights	Acesso Restrito	pt_BR
dc.title	MHM : a novel collaborative spectrum sensing method based on markov-chains and harmonic mean for 5G networks	pt_BR
dc.type	Trabalhos apresentados em evento	pt_BR
dc.subject.keyword	Sensores	pt_BR
dc.subject.keyword	Processos de Markov	pt_BR
dc.subject.keyword	5G	pt_BR
dc.subject.keyword	Rádio	pt_BR
dc.subject.keyword	Acesso dinâmico ao espectro	pt_BR
dc.relation.publisherversion	https://ieeexplore.ieee.org/document/9142763	pt_BR
dc.description.abstract1	Cognitive radios and spectrum sensing are considered fundamental for spectrum optimization in 5G networks. Collaborative spectrum sensing improves detection by collecting data from different nodes and increasing the amount of information available for accurate channel state detection. However, malicious nodes can report wrong information, disturbing the collaborative sensing results and network operation. This paper presents two techniques: (1) a Markov chain-based technique that improves spectrum sensing accuracy while reducing the reporting control traffic; (2) a harmonic mean-based technique that discards less relevant sensing reports, mitigating Byzantine attacks. The two techniques were evaluated in a simulation scenarios based on rural areas. The results show that the proposed techniques increase the accuracy of a classic hard-combining fusion technique, reducing false positives and reporting overhead while improving network resilience to malicious nodes.	pt_BR
dc.description.unidade	Instituto de Ciências Exatas (IE)	-
dc.description.unidade	Departamento de Ciência da Computação (IE CIC)	-
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