Sistemas Ubíquos Eficientes em Consumo de Energia por Meio da Redução de Dados
Resumo
A adaptabilidade de sistemas ubíquos é fortemente relacionada a capacidade de monitorar continuamente o ambiente, o que requer soluções energeticamente econˆomicas. Neste cenário, faz-se necessária a adoção de mecanismos para aumentar o tempo de vida da camada de sensoriamento e, consequentemente, prover alta disponibilidade aos serviços sensíveis ao contexto. Uma forma de resolver o problema é adotar mecanismos de redução de dados, mas isso pode gerar ruídos (erros) que prejudiquem a acurácia da aplicação. Assim, este artigo propõe um mecanismo de redução de dados adaptativo ao erro. Esta solução é baseada em predição, a qual é capaz de modelar as coletas de dados em parâmetros de função linear, que são usados para recuperar o sinal no destinatário (sorvedouro). Nos resultados dos experimentos, nosso mecanismo, conseguiu reduzir cerca de 89,58% a 97,22% dos pacotes enviados na rede e consequentemente, a quantidade de energia consumida pelos dispositivos.
Referências
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Bringel Filho, J. and Agoulmine, N. (2011). A quality-aware approach for resolving context conflicts in context-aware systems. 9th IEEEIFIP International Conference on Embedded and Ubiquitous Computing.
Bringel Filho, J., Miron, A., Satoh, I., Gensel, J., and Martin, H. (2010). Modeling and measuring quality of context information in pervasive environments. 24th IEEE International Conference on Advanced Information Networking and Applications, 24:690–697.
Caceres, R. and Friday, A. (2012). Ubicomp systems at 20: Progress, opportunities, and challenges. IEEE Pervasive Computing, 11:14–21.
Carvalho, C., Cronemberger, I., Silva, W., Silva, T., Leal, L., Lemos, M., and Bringel Filho, J. (2012). Smart grid communication data reduction to enhanced bandwidth usage. In International Workshop on ADVANCEs in ICT Infrastructures and Services, ADVANCE2012.
Carvalho, C., Gomes, D., De Souza, J., and Agoulmine, N. (2011a). Multiple linear regression to improve prediction accuracy in wsn data reduction. In Network Operations and Management Symposium (LANOMS), 2011 7th Latin American, pages 1–8.
Carvalho, C., Gomes, D. G., Agoulmine, N., and de Souza, J. N. (2011b). Improving prediction accuracy for wsn data reduction by applying multivariate spatio-temporal correlation. Sensors, 11(11):10010–10037.
Carvalho, C. d., Santos, W., Nunes, L., Souza, B., Carvalho-Zilse, G., and ALVES, R. (2011c). Offspring analysis in a polygyne colony of melipona scutellaris (hymenoptera: Apidae) by means of morphometric analyses. In Sociobiology, volume 57, pages 347–354.
Carvalho, C. d., Souza, B., Dias, C., Alves, R., Melo, A., Soares, A., and Carvalho-Zilse, G. (2011d). Five egg-laying queens in a single colony of brazilian stingless bees (melipona scutellaris latreille). In Acta Amazonica, volume 41, pages 123–126.
Chong, L., Kui,W., and Jian, P. (2007). An energy-efficient data collection framework for wireless sensor networks by exploiting spatiotemporal correlation. Parallel and Distributed Systems, IEEE Transactions on, 18(7):1010–1023.
Debono, C. and Borg, N. (2008). The implementation of an adaptive data reduction technique for wireless sensor networks. Signal Processing and Information Technology, ISSPIT 2008, IEEE International Symposium on, pages 402–406.
Dietrich, I. and Dressler, F. (2009). On the lifetime of wireless sensor networks. ACM Trans. Sen. Netw., 5(1):5:1–5:39.
Hodges, S., Villar, N., Scott, J., and Schmidt, A. (2012). A new era for ubicomp development. IEEE Pervasive Computing, 11:5–9.
Hongbo, J., Shudong, J., and Chonggang, W. (2011). Prediction or not: An energy-efficient framework for clustering-based data collection in wireless sensor networks. Parallel and Distributed Systems, IEEE Transactions on, 22(6):1064–1071.
Li, J., Deshpande, A., and Khuller, S. (2010). On computing compression trees for data collection in wireless sensor networks. Proceedings of the 29th conference on Information communications, INFOCOM’10, pages 2115–2123.
Meitalovs, J., Histjaves, A., and Stalidzans, E. (2009). Automatic microclimate controlled beehive observation system. In 8th International Scientific Conference, Engineering for Rural Development, Latvia University of Agriculture, pages 265–271.
Santini, S. and Romer, K. (2006). An adaptive strategy for quality-based data reduction in wireless sensor networks. Proc. INSS.
Sathe, S., Papaioannou, T., Jeung, H., and Aberer, K. (2013). A survey of model-based sensor data acquisition and management. Managing and Mining Sensor Data, Springer US, pages 9–50.
Wei, C. and Wassell, I. (2011). Energy efficient signal acquisition via compressive sensing in wireless sensor networks. Wireless and Pervasive Computing (ISWPC), 6th International Symposium on, pages 1–6.
Zacepins, A. (2012). Application of bee hive temperature measurements for recognition of bee colony state. In International Conference on Applied Information and Communication Technologies (AICT2012), pages 465–468, Jelgava, Latvia.
Bakhtiar, Q., Makki, K., and Pissinou, N. (2012). Data reduction in low powered wireless sensor networks. Wireless Sensor Networks - Technology and Applications, Chapter 8, pages 171–186. Baldauf, M., Dustdar, S., and Rosenberg, F. (2007). A survey on context-aware systems. Int. J. Ad Hoc Ubiquitous Comput., 2:263–277.
Bringel Filho, J. and Agoulmine, N. (2011). A quality-aware approach for resolving context conflicts in context-aware systems. 9th IEEEIFIP International Conference on Embedded and Ubiquitous Computing.
Bringel Filho, J., Miron, A., Satoh, I., Gensel, J., and Martin, H. (2010). Modeling and measuring quality of context information in pervasive environments. 24th IEEE International Conference on Advanced Information Networking and Applications, 24:690–697.
Caceres, R. and Friday, A. (2012). Ubicomp systems at 20: Progress, opportunities, and challenges. IEEE Pervasive Computing, 11:14–21.
Carvalho, C., Cronemberger, I., Silva, W., Silva, T., Leal, L., Lemos, M., and Bringel Filho, J. (2012). Smart grid communication data reduction to enhanced bandwidth usage. In International Workshop on ADVANCEs in ICT Infrastructures and Services, ADVANCE2012.
Carvalho, C., Gomes, D., De Souza, J., and Agoulmine, N. (2011a). Multiple linear regression to improve prediction accuracy in wsn data reduction. In Network Operations and Management Symposium (LANOMS), 2011 7th Latin American, pages 1–8.
Carvalho, C., Gomes, D. G., Agoulmine, N., and de Souza, J. N. (2011b). Improving prediction accuracy for wsn data reduction by applying multivariate spatio-temporal correlation. Sensors, 11(11):10010–10037.
Carvalho, C. d., Santos, W., Nunes, L., Souza, B., Carvalho-Zilse, G., and ALVES, R. (2011c). Offspring analysis in a polygyne colony of melipona scutellaris (hymenoptera: Apidae) by means of morphometric analyses. In Sociobiology, volume 57, pages 347–354.
Carvalho, C. d., Souza, B., Dias, C., Alves, R., Melo, A., Soares, A., and Carvalho-Zilse, G. (2011d). Five egg-laying queens in a single colony of brazilian stingless bees (melipona scutellaris latreille). In Acta Amazonica, volume 41, pages 123–126.
Chong, L., Kui,W., and Jian, P. (2007). An energy-efficient data collection framework for wireless sensor networks by exploiting spatiotemporal correlation. Parallel and Distributed Systems, IEEE Transactions on, 18(7):1010–1023.
Debono, C. and Borg, N. (2008). The implementation of an adaptive data reduction technique for wireless sensor networks. Signal Processing and Information Technology, ISSPIT 2008, IEEE International Symposium on, pages 402–406.
Dietrich, I. and Dressler, F. (2009). On the lifetime of wireless sensor networks. ACM Trans. Sen. Netw., 5(1):5:1–5:39.
Hodges, S., Villar, N., Scott, J., and Schmidt, A. (2012). A new era for ubicomp development. IEEE Pervasive Computing, 11:5–9.
Hongbo, J., Shudong, J., and Chonggang, W. (2011). Prediction or not: An energy-efficient framework for clustering-based data collection in wireless sensor networks. Parallel and Distributed Systems, IEEE Transactions on, 22(6):1064–1071.
Li, J., Deshpande, A., and Khuller, S. (2010). On computing compression trees for data collection in wireless sensor networks. Proceedings of the 29th conference on Information communications, INFOCOM’10, pages 2115–2123.
Meitalovs, J., Histjaves, A., and Stalidzans, E. (2009). Automatic microclimate controlled beehive observation system. In 8th International Scientific Conference, Engineering for Rural Development, Latvia University of Agriculture, pages 265–271.
Santini, S. and Romer, K. (2006). An adaptive strategy for quality-based data reduction in wireless sensor networks. Proc. INSS.
Sathe, S., Papaioannou, T., Jeung, H., and Aberer, K. (2013). A survey of model-based sensor data acquisition and management. Managing and Mining Sensor Data, Springer US, pages 9–50.
Wei, C. and Wassell, I. (2011). Energy efficient signal acquisition via compressive sensing in wireless sensor networks. Wireless and Pervasive Computing (ISWPC), 6th International Symposium on, pages 1–6.
Zacepins, A. (2012). Application of bee hive temperature measurements for recognition of bee colony state. In International Conference on Applied Information and Communication Technologies (AICT2012), pages 465–468, Jelgava, Latvia.
Publicado
28/07/2014
Como Citar
DA SILVA, Thiago; LEAL, Liliam; LEMOS, Markus; DE CARVALHO, Carlos; BRINGEL FILHO, José; H. FILHO, Raimir.
Sistemas Ubíquos Eficientes em Consumo de Energia por Meio da Redução de Dados. In: SIMPÓSIO BRASILEIRO DE COMPUTAÇÃO UBÍQUA E PERVASIVA (SBCUP), 6. , 2014, Brasília.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2014
.
p. 160-169.
ISSN 2595-6183.
