Compressão de Dados Local em Tempo Real Energeticamente Eficiente para Redes WBAN
Resumo
A evolução das tecnologias sem fio tem permitido a criação de diversos tipos de redes para suportar os serviços do dia-a-dia das pessoas. Entre as redes voltadas ao monitoramento dos cuidados com a saúde, destacam-se as redes corporais sem fio (WBANs), que possibilitam um monitoramento contínuo e em tempo real dos dados fisiológicos. Contudo, este monitoramento acarreta uma excessiva transmissão de dados e eleva o consumo de energia dos dispositivos. Embora algumas abordagens reduzam o consumo de energia, poucas desconsideram a transmissão de informações redundantes. Este trabalho apresenta o mecanismo GROWN para gerenciar a redundância da informação através de compressão de dados fisiológicos em dispositivos vestíveis, reduzindo a transmissão de dados e o consumo de energia do dispositivo em WBANs de tempo real. O GROWN combina métodos de compressão de dados local com e sem perdas a partir de limiares definidos. Avaliação por experimentações do GROWN demonstrou uma diminuição do consumo de energia dos dispositivos em até 55,73%, o aumento do tempo de vida da rede e latência máxima de 55ms, comprovando sua eficiência e aplicação em WBANs de tempo real.
Palavras-chave:
compressão de dados, tempo real, eficiência energética, WBAN
Referências
Antonopoulos, C. P. and Voros, N. S. (2016). Resource efficient data compression algorithms for demanding, wsn based biomedical applications. Journal of biomedical informatics, 59:1-14.
Azar, J., Darazi, R., Habib, C., Makhoul, A., and Demerjian, J. (20184). Using dwt lifting schemefor lossless data compression in wireless body sensor networks. In 2018 14th International Wi-reless Communications & Mobile Computing Conference (IWCMC), pages 1465-1470. IEEE.
Azar, J., Makhoul, A., Darazi, R., Demerjian, J., and Couturier, R. (2018b). On the performance of resource-aware compression techniques for vital signs data in wireless body sensor networks. In Middle East and North Africa Communications (MENACOMM), pages 1-6. IEEE.
Bontorin, M., Nogueira, M., and Santos, A. (2015). A bio-inspired technique for energy-aware fpgas on body area networks. IEEE Latin America Transactions, 13(12):3707-3713.
Deepu, C. J., Heng, C.-H., and Lian, Y. (2017). A hybrid data compression scheme for power reduction in wireless sensors for iot. IEEE transactions on biomedical circuits and systems,11(2):245-254.
Ding, W., Jing, X., Yan, Z., and Yang, L. T. (2019). A survey on data fusion in internet of things: Towards secure and privacy-preserving fusion. Information Fusion, 51:129-144.
Gatouillat, A., Massot, B., Badr, Y., Sejdié, E., and Gehin, C. (2018). Building iot-enabled wearable medical devices: an application to a wearable, multiparametric, cardiorespiratory sensor.
Giorgi, G. (2017). A combined approach for real-time data compression in wireless body sensor networks. IEEE Sensors Journal, 17(18):6129-6135.
Instruments, T. (2020). INA219 Zero-Drift, Bidirectional Current/Power Monitor With I2C Interface. In http://www.ti.com/lit/ds/symlink/ina219.pdf. Acessado em 20/03/2020.
Javaid, N., Ahmad, A., Taugir, A., Imran, M., Guizani, M., Khan, Z. A., and Qasim, U. (2016) .Modeling induction and routing to monitor hospitalized patients in multi-hop mobility-awarebody area sensor networks. EURASIP Journal on Wireless Communications and Networking,2016(1):147.
Khan, R. A. and Pathan, A.-S. K. (2018). The state-of-the-art wireless body area sensor networks: A survey. International Journal of Distributed Sensor Networks, 14(4):1550147718768994.
Liao, Y., Leeson, M., Cai, Q., Ai, Q., and Liu, Q. (2018). Mutual-information-based incremental relaying communications for wireless biomedical implant systems. Sensors, 18(2):515.
Marcelloni, F. and Vecchio, M. (2008). A simple algorithm for data compression in wireless networks. JEEE communications letters, 12(6).
Marcelloni, F. and Vecchio, M. (2010). Enabling energy-efficient and lossy-aware data compres-sion in wireless sensor networks by multi-objective evolutionary optimization. InformationSciences, 180(10):1924-1941.
Movassaghi, S., Abolhasan, M., Lipman, J., Smith, D., and Jamalipour, A. (2014). Wireless bodyarea networks: A survey. IEEE Communications Surveys & Tutorials, 16(3):1658-1686.
Parks, J. E. (2007). Ohms Law II Resistors in Series and Parallel. Department of Physics andAnatomy, University of Tennessee.
Patel, D., Bhogan, V., and Janson, A. (2013). Simulation and comparison of various lossless data compression techniques based on compression ratio and processing delay. International Journal of Computer Applications, 81(14).
Qu, Y., Zheng, G., Ma, H., Wang, X., Ji, B., and Wu, H. (2019). A survey of routing protocols inwban for healthcare applications. Sensors, 19(7):1638.
Schoellhammer, T., Greenstein, B., Osterweil, E., Wimbrow, M., and Estrin, D. (2004). Lightweight temporal compression of microclimate datasets [wireless sensor networks]. 29th Annual IEEE International Conference on Local Computer Networks, pages 516-524.
Sweldens, W. (1998). The lifting scheme: A construction of second generation wavelets. SIAM journal on mathematical analysis, 29(2):511-546.
Teuhola, J. (1978). A compression method for clustered bit-vectors. Information processing letters, 7(6):308-311.
Tsai, T.-H. and Kuo, W.-T. (2018). An Efficient ECG Lossless Compression System for Embedded Platforms With Telemedicine Applications. IEEE Access, 6:42207-42215.
Uthayakumar, J., Vengattaraman, T., and Dhavachelvan, P. (2018). A survey on data compression techniques: From the perspective of data quality, coding schemes, data type and applications. Journal of King Saud University-Computer and Information Sciences.
Vergutz, A., da Silva, R., Vieira, A. B., Nogueira, M., et al. (2017). Um Sistema de Identificação Antecipada e Transmissão Prioritária de Alertas Médicos sobre WBAN e WLAN. In Anais do XXXV Simpósio Brasileiro de Redes de Computadores e Sistemas Distribuídos. SBC.
Azar, J., Darazi, R., Habib, C., Makhoul, A., and Demerjian, J. (20184). Using dwt lifting schemefor lossless data compression in wireless body sensor networks. In 2018 14th International Wi-reless Communications & Mobile Computing Conference (IWCMC), pages 1465-1470. IEEE.
Azar, J., Makhoul, A., Darazi, R., Demerjian, J., and Couturier, R. (2018b). On the performance of resource-aware compression techniques for vital signs data in wireless body sensor networks. In Middle East and North Africa Communications (MENACOMM), pages 1-6. IEEE.
Bontorin, M., Nogueira, M., and Santos, A. (2015). A bio-inspired technique for energy-aware fpgas on body area networks. IEEE Latin America Transactions, 13(12):3707-3713.
Deepu, C. J., Heng, C.-H., and Lian, Y. (2017). A hybrid data compression scheme for power reduction in wireless sensors for iot. IEEE transactions on biomedical circuits and systems,11(2):245-254.
Ding, W., Jing, X., Yan, Z., and Yang, L. T. (2019). A survey on data fusion in internet of things: Towards secure and privacy-preserving fusion. Information Fusion, 51:129-144.
Gatouillat, A., Massot, B., Badr, Y., Sejdié, E., and Gehin, C. (2018). Building iot-enabled wearable medical devices: an application to a wearable, multiparametric, cardiorespiratory sensor.
Giorgi, G. (2017). A combined approach for real-time data compression in wireless body sensor networks. IEEE Sensors Journal, 17(18):6129-6135.
Instruments, T. (2020). INA219 Zero-Drift, Bidirectional Current/Power Monitor With I2C Interface. In http://www.ti.com/lit/ds/symlink/ina219.pdf. Acessado em 20/03/2020.
Javaid, N., Ahmad, A., Taugir, A., Imran, M., Guizani, M., Khan, Z. A., and Qasim, U. (2016) .Modeling induction and routing to monitor hospitalized patients in multi-hop mobility-awarebody area sensor networks. EURASIP Journal on Wireless Communications and Networking,2016(1):147.
Khan, R. A. and Pathan, A.-S. K. (2018). The state-of-the-art wireless body area sensor networks: A survey. International Journal of Distributed Sensor Networks, 14(4):1550147718768994.
Liao, Y., Leeson, M., Cai, Q., Ai, Q., and Liu, Q. (2018). Mutual-information-based incremental relaying communications for wireless biomedical implant systems. Sensors, 18(2):515.
Marcelloni, F. and Vecchio, M. (2008). A simple algorithm for data compression in wireless networks. JEEE communications letters, 12(6).
Marcelloni, F. and Vecchio, M. (2010). Enabling energy-efficient and lossy-aware data compres-sion in wireless sensor networks by multi-objective evolutionary optimization. InformationSciences, 180(10):1924-1941.
Movassaghi, S., Abolhasan, M., Lipman, J., Smith, D., and Jamalipour, A. (2014). Wireless bodyarea networks: A survey. IEEE Communications Surveys & Tutorials, 16(3):1658-1686.
Parks, J. E. (2007). Ohms Law II Resistors in Series and Parallel. Department of Physics andAnatomy, University of Tennessee.
Patel, D., Bhogan, V., and Janson, A. (2013). Simulation and comparison of various lossless data compression techniques based on compression ratio and processing delay. International Journal of Computer Applications, 81(14).
Qu, Y., Zheng, G., Ma, H., Wang, X., Ji, B., and Wu, H. (2019). A survey of routing protocols inwban for healthcare applications. Sensors, 19(7):1638.
Schoellhammer, T., Greenstein, B., Osterweil, E., Wimbrow, M., and Estrin, D. (2004). Lightweight temporal compression of microclimate datasets [wireless sensor networks]. 29th Annual IEEE International Conference on Local Computer Networks, pages 516-524.
Sweldens, W. (1998). The lifting scheme: A construction of second generation wavelets. SIAM journal on mathematical analysis, 29(2):511-546.
Teuhola, J. (1978). A compression method for clustered bit-vectors. Information processing letters, 7(6):308-311.
Tsai, T.-H. and Kuo, W.-T. (2018). An Efficient ECG Lossless Compression System for Embedded Platforms With Telemedicine Applications. IEEE Access, 6:42207-42215.
Uthayakumar, J., Vengattaraman, T., and Dhavachelvan, P. (2018). A survey on data compression techniques: From the perspective of data quality, coding schemes, data type and applications. Journal of King Saud University-Computer and Information Sciences.
Vergutz, A., da Silva, R., Vieira, A. B., Nogueira, M., et al. (2017). Um Sistema de Identificação Antecipada e Transmissão Prioritária de Alertas Médicos sobre WBAN e WLAN. In Anais do XXXV Simpósio Brasileiro de Redes de Computadores e Sistemas Distribuídos. SBC.
Publicado
07/12/2020
Como Citar
PASSOS, Cainã; PEDROSO, Carlos; BATISTA, Agnaldo; NOGUEIRA, Michele; SANTOS, Aldri.
Compressão de Dados Local em Tempo Real Energeticamente Eficiente para Redes WBAN. In: WORKSHOP DE GERÊNCIA E OPERAÇÃO DE REDES E SERVIÇOS (WGRS), 25. , 2020, Rio de Janeiro.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2020
.
p. 85-98.
ISSN 2595-2722.
DOI: https://doi.org/10.5753/wgrs.2020.12453.
