MobETX: uma métrica de roteamento ciente da mobilidade para Internet das Coisas
Resumo
O processo de roteamento na Internet of Things (IoT) é um aspecto chave quando dispositivos podem mover. O roteamento habilita a comunicação entre os dispositivos, potencializando a funcionalidade dos objetos do dia-adia a eles anexados. Contudo, a mobilidade causa frequentes mudanças na topologia tornando o roteamento um desafio. A maioria dos protocolos de roteamento da IoT usam um estimador de enlace (Link Quality Estimator (LQE)) no processo de escolha de rotas, sendo o Expected Transmission Count (ETX) um dos mais utilizados para ponderar enlaces sem fio entre dispositivos estáticos. Contudo, o ETX não funciona bem quando os dispositivos se movem. Neste artigo, apresentamos o Mobile Expected Transmission Count (MobETX) como estimador para enlaces sem fio entre dispositivos móveis. MobETX mantém as características do ETX, mas usa métricas de mobilidade dos dispositivos para dar assistência no processo de escolha de rotas. Em nossos experimentos, o protocolo de roteamento usando MobETX apresentou melhora de ao menos 5% na taxa de entrega de pacotes quando comparado com ETX em cenários de alta mobilidade e > 98% de entrega de pacotes em cenários estáticos.
Referências
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Sobral, J. V., Rodrigues, J. J., Rabêlo, R. A., Al-Muhtadi, J., and Korotaev, V. (2019). Routing Protocols for Low Power and Lossy Networks in Internet of Things Applications. Sensors, 19(9):2144.
Souza, F. R., Domingues, A. C., de Melo, P. O. V., and Loureiro, A. A. (2018). MOCHA: Um framework para caracterização e comparação de traces de mobilidade. In Anais do XXXVI SBRC. SBC.
Sun, W., Lu, W., Li, Q., Chen, L., Mu, D., and Yuan, X. (2017). Wnn-lqe: Wavelet-neuralIEEE Access, 5:12788– network-based link quality estimation for smart grid wsns. 12797.
Tan, W. L., Hu, P., and Portmann, M. (2012). SNR-based link quality estimation. In 2012 IEEE 75th VTC, pages 1–5.
Thubert, P. et al. (2012). Objective function zero for the routing protocol for low-power and lossy networks (rpl).
University of Osnabrück (2016). A Mobility Scenario Generation and Analysis Tool. University of Osnabrück. http://sys.cs.uos.de/bonnmotion/doc/README.pdf.
Xue, X., Sun, W., Wang, J., Li, Q., Luo, G., and Yu, K. (2020). Rv-lqp: Rv-based link quality prediction of wireless sensor networks in smart grid. IEEE Access, 8:7829– 7841.
Atzori, L., Iera, A., and Morabito, G. (2010). The internet of things: A survey. Computer networks, 54(15):2787–2805.
Baccour, N., Koubâa, A., Mottola, L., Zú˜niga, M. A., Youssef, H., Boano, C. A., and Alves, M. (2012). Radio link quality estimation in wireless sensor networks: A survey. ACM TOSN, 8(4):34.
Bahr, M., Wang, J., and Jia, X. (2006). Routing in Wireless Mesh Networks. Proceedings of the Annual International Conference on MOBICOM.
Boano, C. A., Zúniga, M. A., Voigt, T., Willig, A., and Römer, K. (2010). The triangle metric: Fast link quality estimation for mobile wireless sensor networks. In ICCCN. IEEE.
Charles, A. J. and Palanisamy, K. (2020). Neo-hybrid Composite Routing Metric for RPL. Procedia Computer Science, 171:1819–1828.
De Couto, D. S., Aguayo, D., Bicket, J., and Morris, R. (2005). A high-throughput path metric for multi-hop wireless routing. Wireless networks.
Ding, W., Tang, L., and Ji, S. (2016). Optimizing routing based on congestion control for wireless sensor networks. Wireless Networks, 22(3):915–925.
Draves, R., Padhye, J., and Zill, B. (2004). Comparison of routing metrics for static multi-hop wireless networks. ACM SIGCOMM CCR.
Dunkels, A., Gronvall, B., and Voigt, T. (2004). Contiki-a lightweight and exible operating system for tiny networked sensors. In 29th annual IEEE LCN.
Fotouhi, H., Moreira, D., and Alves, M. (2015). mRPL: Boosting mobility in the Internet of Things. Ad Hoc Networks, 26:17–35.
Gnawali, O. and Levis, P. (2012). The minimum rank with hysteresis objective function. RFC 6719.
Guo, Z.-Q., Wang, Q., Li, M.-H., and He, J. (2013). Fuzzy logic based multidimensional link quality estimation for multi-hop wireless sensor networks. IEEE Sensors Journal, 13(10):3605–3615.
Instruments, T. (2007). CC2420 datasheet. Reference SWRS041B.
Jayasri, T. and Hemalatha, M. (2017). Link quality estimation for adaptive data streaming in wsn. Wireless Personal Communications.
Kassab, W. and Darabkh, K. A. (2020). A–Z survey of Internet of Things: Architectures, protocols, applications, recent advances, future directions and recommendations. Journal of Network and Computer Applications, 163:102663.
Lamaazi, H. and Benamar, N. (2017). RPL enhancement using a new objective function based on combined metrics. In 2017 13th IEEE IWCMC.
Liu, D., Cao, Z., Hou, M., and Zhang, Y. (2016). Frame counter: Achieving accurate In 2016 15th and real-time link estimation in low power wireless sensor networks. ACM/IEEE IPSN, pages 1–12. IEEE.
Liu, T. and Cerpa, A. E. (2014a). Data-driven link quality prediction using link features. ACM TOSN, 10(2):1–35.
Liu, T. and Cerpa, A. E. (2014b). Temporal adaptive link quality prediction with online learning. ACM TOSN, 10(3):1–41.
Lowrance, C. J. and Lauf, A. P. (2017). Link quality estimation in ad hoc and mesh networks: a survey and future directions. Wireless Personal Communications.
Oliveira, A. and Vazao, T. (2016). Low-power and lossy networks under mobility: A survey. Computer networks, 107:339–352.
Pereira, H., Moritz, G. L., and Fonseca, M. (2019). Uma métrica de roteamento com In Anais do XXXVII SBRC, eficiência energética para o rpl da internet das coisas. pages 224–237. SBC.
Ren, F., He, T., Das, S. K., and Lin, C. (2011). Traffic-aware dynamic routing to alleviate congestion in wireless sensor networks. IEEE TPDS.
Santos, B. P., Goussevskaia, O., Vieira, L. F., Vieira, M. A., and Loureiro, A. A. (2018). Mobile matrix: routing under mobility in IoT, IoMT, and social IoT. Ad Hoc Networks, 78:84–98.
Santos, B. P., Silva, L. A., Celes, C., Borges, J. B., Neto, B. S. P., Vieira, M. A. M., Vieira, L. F. M., Goussevskaia, O. N., and Loureiro, A. (2016). Internet das coisas: da teoria à prática. Minicursos SBRC.
Santos, B. P., Vieira, L. F., and Vieira, M. A. (2017). CGR: Centrality-based green routing for Low-power and Lossy Networks. Computer Networks.
Santos, B. P., Vieira, L. F. M., and Loureiro, A. A. F. (2020). Routing and Mobility Management in the Internet of Things. In Anais Estendidos do XXXVIII SBRC, pages 161–168. SBC.
Sobral, J. V., Rodrigues, J. J., Rabêlo, R. A., Al-Muhtadi, J., and Korotaev, V. (2019). Routing Protocols for Low Power and Lossy Networks in Internet of Things Applications. Sensors, 19(9):2144.
Souza, F. R., Domingues, A. C., de Melo, P. O. V., and Loureiro, A. A. (2018). MOCHA: Um framework para caracterização e comparação de traces de mobilidade. In Anais do XXXVI SBRC. SBC.
Sun, W., Lu, W., Li, Q., Chen, L., Mu, D., and Yuan, X. (2017). Wnn-lqe: Wavelet-neuralIEEE Access, 5:12788– network-based link quality estimation for smart grid wsns. 12797.
Tan, W. L., Hu, P., and Portmann, M. (2012). SNR-based link quality estimation. In 2012 IEEE 75th VTC, pages 1–5.
Thubert, P. et al. (2012). Objective function zero for the routing protocol for low-power and lossy networks (rpl).
University of Osnabrück (2016). A Mobility Scenario Generation and Analysis Tool. University of Osnabrück. http://sys.cs.uos.de/bonnmotion/doc/README.pdf.
Xue, X., Sun, W., Wang, J., Li, Q., Luo, G., and Yu, K. (2020). Rv-lqp: Rv-based link quality prediction of wireless sensor networks in smart grid. IEEE Access, 8:7829– 7841.
Publicado
16/08/2021
Como Citar
REIS, Gabriel Augusto R. dos; SANTOS, Bruno P.; VIEIRA, Luiz F. M..
MobETX: uma métrica de roteamento ciente da mobilidade para Internet das Coisas. In: SIMPÓSIO BRASILEIRO DE REDES DE COMPUTADORES E SISTEMAS DISTRIBUÍDOS (SBRC), 39. , 2021, Uberlândia.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2021
.
p. 364-377.
ISSN 2177-9384.
DOI: https://doi.org/10.5753/sbrc.2021.16733.
