A framework for cognitive radio wireless sensor networks

Correia, Luiz H. A.; Oliveira, Erasmo E.; Macedo, Daniel F.; Moura, Pedro M.; Loureiro, Antônio A. F.; Silva, Jorge Sá

doi:10.1109/iscc.2012.6249364

Cited by 12 publications

(7 citation statements)

References 15 publications

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“…Em RSSF, [16] propõem dois métodos de decisão de espectro para seleção de canal. O primeiro deles toma como base medições de RSSI (Received Signal Strength Indicator) e resultados apresentados pelo algoritmo CCA (Clear Channel Assessment) em cada canal analisado.…”

Section: Conceitos E Trabalhos Relacionadosunclassified

Spectrum Decision in Wireless Sensor Networks Employing Machine Learning

Silva

Macedo

Leoni

2014

2014 Brazilian Symposium on Computer Networks and Distributed Systems

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Wireless Sensor Networks (WSNs) employ ISM spectrum bands for communication, which are overloaded due to various technologies such as WLANs and other WSNs. Therefore, such networks must employ intelligent methods such as Cognitive Radio to coexist with other networks. This study evaluates the use of Supervised Machine Learning (ML) for channel selection in WSNs, considering the channel quality and communication metrics. The methods were evaluated experimentally and compared with energy-based methods. The results show that ML-based methods increase the communication performance by reducing the number of transmission attempts and therefore also reducing the delivery delay. I. INTRODUÇÃOUm estudo da FCC (Federal Communications Commision) [1] mostrou que as regras atuais adotadas para alocação de canal causam perdas consideráveis na eficiência e aproveitamento do espectro, pois em inúmeros intervalos de tempo os usuários licenciados ao uso desses canais não estão em operação [2], [3]. Além disso, o acúmulo de usuários não licenciados em frequências livres gera interferências, causando perdas de informação e um aumento no consumo de energia. Redes de Sensores Sem Fio (RSSFs) são suscetíveis a esse problema, o que levà a redução do seu tempo de vida.Por isso, as RSSFs deveriam adotar técnicas que explorem o espectro com mais eficiência, de forma a coexistir com usuários de diferentes redes sem causar conflitos entre elas. Os Rádios Cognitivos (RCs) permitem uma melhor exploração de faixas de frequência pouco utilizadas [3]. Neste trabalho seguimos a definição de [4], onde o RC mensura o ambiente eletromagnético no qual ele opera, para ajustar seus parâmetros de forma autônoma e dinâmica, de forma a aumentar o fluxo de dados, reduzir a interferência e facilitar a interoperabilidade.No caso das RSSFs, que empregam a faixa de frequência não licenciada ISM (Industrial, Scientific and Medical), as funções de RC são utilizadas para aumentar a coexistência de múltiplas redes, ao mesmo tempo que garantem uma comunicação mais eficiente e confiável [5]. Enquanto nos RCs "tradicionais" procura-se selecionar faixas de frequência livres de usuários licenciados (também chamados de Usuários Primários), nas RSSFs os RCs selecionam canais com melhores condições para a comunicação, evitando a interferência tanto de outras RSSFs instaladas na região quanto de outros tipos de redes operando na faixa ISM (Wi-Fi, Bluetooth, etc). Entretanto, o principal desafio na implementação de RCs em RSSFś e a limitação de recursos de processamento, memória e energia destas redes. Assim, as soluções de RC existentes devem ser adequadasàs restrições das RSSFs.Neste trabalho propomos o emprego de Aprendizado de Máquina (AM) para a seleção de canais em RSSFs. As soluções propostas são avaliadas experimentalmente, comparando o seu desempenho com soluções baseadas somente em energia, as quais são populares em ambientes reais. Foram coletados dados de desempenho acerca de uma sequência de transmissões e recepções realizadas, como forma de quantificar o nível...

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Section: Conceitos E Trabalhos Relacionadosunclassified

Spectrum Decision in Wireless Sensor Networks Employing Machine Learning

Silva

Macedo

Leoni

2014

2014 Brazilian Symposium on Computer Networks and Distributed Systems

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“…That is, one aim of CR is to maximize the spectrum utilization for scare radio resources [ 8 , 9 , 10 , 11 , 12 , 13 ]. Until now, many researches have been done in order to cope with the coexistence of multiple wireless technologies in unlicensed spectrum bands in the WSNs [ 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 ].…”

Section: Introductionmentioning

confidence: 99%

Dynamic Spectrum Access for Internet of Things Service in Cognitive Radio-Enabled LPWANs

Moon

2017

Sensors

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In this paper, we focus on a dynamic spectrum access strategy for Internet of Things (IoT) applications in two types of radio systems: cellular networks and cognitive radio-enabled low power wide area networks (CR-LPWANs). The spectrum channel contention between the licensed cellular networks and the unlicensed CR-LPWANs, which work with them, only takes place within the cellular radio spectrum range. Our aim is to maximize the spectrum capacity for the unlicensed users while ensuring that it never interferes with the licensed network. Therefore, in this paper we propose a dynamic spectrum access strategy for CR-LPWANs operating in both licensed and unlicensed bands. The simulation and the numerical analysis by using a matrix geometric approach for the strategy are presented. Finally, we obtain the blocking probability of the licensed users, the mean dwell time of the unlicensed user, and the total carried traffic and combined service quality for the licensed and unlicensed users. The results show that the proposed strategy can maximize the spectrum capacity for the unlicensed users using IoT applications as well as keep the service quality of the licensed users independent of them.

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“…Wireless sensor networks (WSNs) have been adopted to enable the large number of connected devices in the IoT and 5G in many industries. Currently, WSN utilizes ISM (Industrial, Scientific and Medical) frequency bands, which are also used by other wireless standards such as Wi-Fi, Bluetooth and IEEE 802.15.4 [2]. The bandwidth of ISM or unlicensed spectrum will not be able to meet the demand of the large number of sensor nodes that are expected to be deployed.…”

Section: Introductionmentioning

confidence: 99%

“…Thus, integration between WSN and CRN as cognitive radio wireless sensor network (CR-WSN) has become fundamental for IoT applications to solve the scarcity of spectrum and large number of sensor nodes. CR-WSNs have been investigated by employing dynamic sensing of the spectrum found in CRNs [2]. It is expected to comprise wireless sensor nodes in large numbers however, conventional sensor nodes may suffer from power constrain and delay sensitivity.…”

Section: Introductionmentioning

confidence: 99%

Optimal Operational Parameters for 5G Energy Harvesting Cognitive Wireless Sensor Networks

Mahmoud

ElAttar

Saafan

et al. 2017

IETE Technical Review

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Dense communication networks have been investigated recently to enable communications of low power wireless devices such as Internet-of-things (IoT) applications in the fifth cellular generation (5G). The wireless sensor network (WSN) is one of the principal technologies of IoT as it plays a critical role in numerous industries such as agriculture, healthcare, and environmental applications. Despite the advantages of the WSN, it is yet difficult to be deployed due to the scarcity of the radio spectrum with the increasing popularity of wireless applications. Therefore, merging of two technologies WSN and cognitive radio network (CRN), as cognitive radio wireless sensor network (CR-WSN), became essential for IoT applications. Another major challenge in such systems is the power constrain and delay sensitivity in such numerous wireless devices. Radio-frequency energy harvesting (EH) capability is supposed to merge with such systems in order to efficiently power and enhance the overall system energy. Thus, this paper discusses a CR-WSN model based on EH in a non-ordinary M/M/1 Markovian battery model with the proposal of a frame structure of the wireless node's charging and sensing time. The contribution of the power obtained from harvesting is derived with the proposal of a realistic power required/harvested model in RF EH CRN. Moreover, the power efficiency of the CR-WSN model is calculated and the derivation of the transmission delay is introduced in the same model. Furthermore, combined optimization of the required power, probability of packet loss and the transmission delay is proposed to validate the overall system performance with the recommended system operational parameters. KEYWORDS Cognitive radio network (CRN); Cognitive radio sensor network (CRWSN); Internet of things (IoT); Markovian model; Power efficiency (PE); Radio frequency energy harvesting (RF EH); Wireless sensor network (WSN)

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A framework for cognitive radio wireless sensor networks

Cited by 12 publications

References 15 publications

Spectrum Decision in Wireless Sensor Networks Employing Machine Learning

Spectrum Decision in Wireless Sensor Networks Employing Machine Learning

Dynamic Spectrum Access for Internet of Things Service in Cognitive Radio-Enabled LPWANs

Optimal Operational Parameters for 5G Energy Harvesting Cognitive Wireless Sensor Networks

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