Um Mecanismo de Controle de Congestionamento Disparado por Eventos Através de Filtragem Local de Dados para RSSFs Cluster-Tree
Resumo
A Internet das Coisas (IoT - Internet of Things) é uma inovação tecnológica que tem se tornado bastante atrativa no âmbito comercial, industrial e residencial. Por conta disso, houve uma expansão no desenvolvimento de mecanismos de comunicação eficientes e desenvolvimento de aplicações em Redes de Sensores Sem Fio (RSSF). O conjunto de normas IEEE 802.15.4 e ZigBee forma uma pilha adequada de protocolos para implantação de RSSFs. Esse conjunto de normas especifica uma topologia especial peer-to-peer chamada cluster-tree, apontada como uma das topologias mais apropriadas para implantação de RSSFs de larga escala. Entretanto, a ocorrência de congestionamentos evidencia-se como um dos maiores desafios de redes cluster-tree, tornando necessário o desenvolvimento de mecanismos eficientes para mitigar este problema, sem comprometer a integridade dos dados. Este artigo propõe a utilização de um mecanismo local de filtragem de dados eficiente como forma de evitar ou postergar congestionamentos para RSSFs cluster-tree, evitando reconfigurações de rede e melhorando métricas importantes, tais como atrasos de comunicação fim-a-fim e perda de pacotes. Os resultados mostram que a implementação de mecanismos de filtragem local nos nós sensores pode fornecer qualidade de serviço para o fluxo de monitoramento, evitando a execução de mecanismos de controle e reconfiguração de redes.
Palavras-chave:
Rede de Sensores sem Fio, IEEE 802.15.4, ZigBee, Cluster-tree, Filtragem de dados, Controle de congestionamento, Internet das Coisas
Referências
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S. R. Barath, B. Usha, G. Arpitha, V. Kumar, and S. Yaseen, “Strategies for congestion control in wireless sensor networks: A survey,” in 2019 International Conference on Smart Systems and Inventive Technology (ICSSIT). IEEE, 2019, pp. 629–633
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A. D. Rathnayaka and V. M. Potdar, “Wireless sensor network transport protocol: A critical review,” Journal of Network and Computer Applications, vol. 36, no. 1, pp. 134–146, 2013.
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A. Aziz, K. Singh, W. Osamy, and A. M. Khedr, “Effective algorithm for optimizing compressive sensing in iot and periodic monitoring applications,” Journal of Network and Computer Applications, vol. 126, pp. 12–28, 2019.
T. L. Le and M.-H. Vo, “Lossless data compression algorithm to save energy in wireless sensor network,” in 2018 4th International Conference on Green Technology and Sustainable Development (GTSD). IEEE, 2018, pp. 597–600.
B. R. Stojkoska and Z. Nikolovski, “Data compression for energy efficient iot solutions,” in 2017 25th telecommunication forum (TELFOR). IEEE, 2017, pp. 1–4.
A. Chatterjee, R. J. Shah, and K. S. Hasan, “Efficient data compression for iot devices using huffman coding based techniques,” in 2018 IEEE International Conference on Big Data (Big Data). IEEE, 2018, pp. 5137–5141.
A. Hanumanthaiah, A. Gopinath, C. Arun, B. Hariharan, and R. Murugan, “Comparison of lossless data compression techniques in low-cost low-power (lclp) iot systems,” in 2019 9th International Symposium on Embedded Computing and System Design (ISED). IEEE, 2019, pp. 1–5.
E. J. Neto, L. Augusto, D. C. Souza, and L. A. Guedes, “Adaptive swinging door trending: um algoritmo adaptativo para compressao de dados em tempo real,” in Proceedings of the Anais do XX Congresso Brasileiro de Automatica, Belo Horizonte, Brazil , 2014, pp. 20–24.
E. Leao, R. Moraes, C. Montez, P. Portugal, and F. Vasques, “CT-SIM: A simulation model for wide-scale cluster-tree networks based on the IEEE 802.15.4 and zigbee standards,” International Journal of Distributed Sensor Networks, vol. 13, no. 3, pp. 1–17, 2017.
Y. Tselishchev, A. Boulis, and L. Libman, “Experiences and lessons from implementing a wireless sensor network mac protocol in the castalia simulator,” in 2010 IEEE Wireless Communication and Networking Conference. IEEE, 2010, pp. 1–6.
T. C. Rodrigues, P. V. Dantas, F. C. Delicato, P. F. Pires, C. Miceli, and L. Pirmez, “Using mda for building wireless sensor network applications,” in 2010 Fourth Brazilian Symposium on Software Components, Architectures and Reuse. IEEE, 2010, pp. 110–119.
J. A. Stankovic, A. D. Wood, and T. He, “Realistic applications for wireless sensor networks,” in Theoretical aspects of distributed computing in sensor networks. Springer, 2011, pp. 835–863.
M. Mohammadi, A. Al-Fuqaha, S. Sorour, and M. Guizani, “Deep learning for iot big data and streaming analytics: A survey,” IEEE Communications Surveys & Tutorials, vol. 20, no. 4, pp. 2923–2960, 2018.
A. O. Adebayo, M. S. Chaubey, and L. P. Numbu, “Industry 4.0: The fourth industrial revolution and how it relates to the application of internet of things (IoT),” Journal of Multidisciplinary Engineering Science Studies (JMESS), vol. 5, no. 2, 2019.
R. A. Khalil, N. Saeed, M. Masood, Y. M. Fard, M.-S. Alouini, and T. Y. Al-Naffouri, “Deep learning in the industrial internet of things: Potentials, challenges, and emerging applications,” IEEE Internet of Things Journal, 2021.
I. F. Akyildiz, W. Su, Y. Sankarasubramaniam, and E. Cayirci, “Wireless sensor networks: a survey,” Computer networks, vol. 38, no. 4, pp. 393–422, 2002.
IEEE, “Ieee 802.15. 4-2015-IEEE standard for low-rate wireless networks,” IEEE standards association, 2015.
ZigBee, “Zigbee specification overview,” Tech. Rep. 10, 2012.
D. V. d. Queiroz et al., “Simulação realista de redes de sensores sem fio industriais,” Master’s thesis, Universidade Federal da Paraíba, 2016.
E. L. F. dos Santos, “A IEEE 802.15.4 como plataforma de comunicação de dados,” Revista Ilha Digital, vol. 4, pp. 97–105, 2013.
E. Leao, C. Montez, R. Moraes, P. Portugal, and F. Vasques, “Superframe duration allocation schemes to improve the throughput of cluster-tree wireless sensor networks,” Sensors, vol. 17, no. 2, p. 249, 2017.
M. Lino, V. Vasconcelos, E. Leao, A. Soares, C. Montez, R. Moraes, and F. Vasques, “Mecanismo eficiente para disseminação do tráfego de controle em redes de sensores sem fio cluster-tree baseadas no IEEE 802.15. 4,” in Anais Estendidos do IX Simpósio Brasileiro de Engenharia de Sistemas Computacionais. SBC, 2019, pp. 1–8.
S. R. Barath, B. Usha, G. Arpitha, V. Kumar, and S. Yaseen, “Strategies for congestion control in wireless sensor networks: A survey,” in 2019 International Conference on Smart Systems and Inventive Technology (ICSSIT). IEEE, 2019, pp. 629–633
A. Cunha, A. Koubaa, R. Severino, and M. Alves, “Open-zb: an open-source implementation of the IEEE 802.15. 4/zigbee protocol stack on tinyos,” in 2007 IEEE International Conference on Mobile Adhoc and Sensor Systems. IEEE, 2007, pp. 1–12.
A. Kiraza and M. C¸ akıroglub, “A survey of congestion control protocols providing energy conservation in wireless sensor networks,” Turkish Journal of Engineering, vol. 1, pp. 12–22, 2014.
A. D. Rathnayaka and V. M. Potdar, “Wireless sensor network transport protocol: A critical review,” Journal of Network and Computer Applications, vol. 36, no. 1, pp. 134–146, 2013.
X. Yin, X. Zhou, R. Huang, Y. Fang, and S. Li, “A fairness-aware congestion control scheme in wireless sensor networks,” IEEE transactions on vehicular technology, vol. 58, no. 9, pp. 5225–5234, 2009.
A. Ghaffari, “Congestion control mechanisms in wireless sensor networks: A survey,” Journal of network and computer applications, vol. 52, pp. 101–115, 2015.
M. A. Jan, S. R. U. Jan, M. Alam, A. Akhunzada, and I. U. Rahman, “A comprehensive analysis of congestion control protocols in wireless sensor networks,” Mobile networks and applications, vol. 23, no. 3, pp. 456–468, 2018.
K. P. Shravana and D. S. V. Veena, “Review on lossless data compression using x-matchpro algorithm,” in 2017 2nd IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology (RTEICT). IEEE, 2017, pp. 1095–1100.
S. Hamdan, A. Awaian, and S. Almajali, “Compression techniques used in iot: A comparitive study,” in 2019 2nd International Conference on new Trends in Computing Sciences (ICTCS). IEEE, 2019, pp. 1–5.
E. Bristol, “Swinging door trending: Adaptive trend recording?” in ISA National Conf. Proc., 1990, 1990, pp. 749–754.
J. D. A. Correa, A. S. R. Pinto, C. Montez, and E. Leao, “Swinging door trending compression algorithm for iot environments,” in Anais Estendidos do IX Simpósio Brasileiro de Engenharia de Sistemas Computacionais. SBC, 2019, pp. 143–148.
A. Aziz, K. Singh, W. Osamy, and A. M. Khedr, “Effective algorithm for optimizing compressive sensing in iot and periodic monitoring applications,” Journal of Network and Computer Applications, vol. 126, pp. 12–28, 2019.
T. L. Le and M.-H. Vo, “Lossless data compression algorithm to save energy in wireless sensor network,” in 2018 4th International Conference on Green Technology and Sustainable Development (GTSD). IEEE, 2018, pp. 597–600.
B. R. Stojkoska and Z. Nikolovski, “Data compression for energy efficient iot solutions,” in 2017 25th telecommunication forum (TELFOR). IEEE, 2017, pp. 1–4.
A. Chatterjee, R. J. Shah, and K. S. Hasan, “Efficient data compression for iot devices using huffman coding based techniques,” in 2018 IEEE International Conference on Big Data (Big Data). IEEE, 2018, pp. 5137–5141.
A. Hanumanthaiah, A. Gopinath, C. Arun, B. Hariharan, and R. Murugan, “Comparison of lossless data compression techniques in low-cost low-power (lclp) iot systems,” in 2019 9th International Symposium on Embedded Computing and System Design (ISED). IEEE, 2019, pp. 1–5.
E. J. Neto, L. Augusto, D. C. Souza, and L. A. Guedes, “Adaptive swinging door trending: um algoritmo adaptativo para compressao de dados em tempo real,” in Proceedings of the Anais do XX Congresso Brasileiro de Automatica, Belo Horizonte, Brazil , 2014, pp. 20–24.
E. Leao, R. Moraes, C. Montez, P. Portugal, and F. Vasques, “CT-SIM: A simulation model for wide-scale cluster-tree networks based on the IEEE 802.15.4 and zigbee standards,” International Journal of Distributed Sensor Networks, vol. 13, no. 3, pp. 1–17, 2017.
Y. Tselishchev, A. Boulis, and L. Libman, “Experiences and lessons from implementing a wireless sensor network mac protocol in the castalia simulator,” in 2010 IEEE Wireless Communication and Networking Conference. IEEE, 2010, pp. 1–6.
Publicado
21/11/2022
Como Citar
RIBEIRO, Natanael; LEÃO, Erico; RABÊLO, Ricardo; VASCONCELOS, Vitor; LINO, Miguel.
Um Mecanismo de Controle de Congestionamento Disparado por Eventos Através de Filtragem Local de Dados para RSSFs Cluster-Tree. In: ARTIGOS COMPLETOS - SIMPÓSIO BRASILEIRO DE ENGENHARIA DE SISTEMAS COMPUTACIONAIS (SBESC), 12. , 2022, Fortaleza/CE.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2022
.
p. 9-16.
ISSN 2763-9002.
DOI: https://doi.org/10.5753/sbesc_estendido.2022.227189.
