A Wavelet-Based Data Reduction Approach for Sensor-Rich Ubiquitous Systems
Resumo
Sensor-rich ubiquitous systems require continuous monitoring platforms for instantaneously providing context-aware services. These sensor-based monitoring platforms generate a large amount of discrete and waveform data, resulting in a high energy expenditure while transmitting data. In this scenario, data reduction mechanisms can be applied for saving transmission energy of sensor devices by reducing discrete and waveform data, maximizing the availability and reliability of sensor-rich ubiquitous systems. Adaptive Simple Linear Regression (ASLR) is well suited for reducing discrete data, however, it is required a more efficient data reduction approach for reducing waveform data, such as ECG signal. This paper proposes and evaluates a wavelet-based data reduction approach for reducing waveform data, which was integrated with our context management framework.
Referências
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[2] A. Zacepins, Application of Bee Hive Temperature Measurements for Recognition of Bee Colony State, International Conference on Applied Information and Communication Technologies (AICT2012), pp.465-468, Jelgava, Latvia, 2012.
[3] A. Zacepins, J. Meitalovs, V. Komasilovs and E. Stalidzans, Temperature sensor network for prediction of possible start of brood rearing by indoor wintered honey bees, Carpathian Control Conference (ICCC), 2011 12th International, pp.465-468, 2011.
[4] D. Conan, R. Rouvoy, L. Seinturier, Scalable processing of context information with COSMOS. DAIS’07: Proceedings of the 7th IFIP WG 6.1 international conference on Distributed applications and interoperable systems, Springer-Verlag, pp.210-224, 2007.
[5] A.K. Dey, G.D. Abowd, The Context Toolkit: Aiding the Development of Context-Aware Applications. Workshop on Software Engineering forWearable and Pervasive Computing, ACM Press, pp.434-441, 1999.
[6] A.K. Dey, Understanding and Using Context Personal Ubiquitous Computing, Springer-Verlag, v.5, pp.4-7, 2001.
[7] M. Baldauf, S. Dustdar, F. Rosenberg,A survey on context-aware systems, Int. J. Ad Hoc Ubiquitous Comput., Inderscience Publishers, v.2, pp.263-277, 2007.
[8] T. Gu, H. Pung, D. Zhang, X. Wang,A Middleware for Building Context-Aware Mobile Services, IEEE Vehicular Technology Conference (VTC), 2004.
[9] A. Manzoor, H.L. Truong, S. Dustdar, Quality Aware Context Information Aggregation System for Pervasive Environments, 2009 International Conference on Advanced Information Networking and Applications Workshops, IEEE Computer Society, pp.266-271, 2009.
[10] S. Sathe, T.G. Papaioannou, H. Jeung and K. Aberer, A Survey of Model-based Sensor Data Acquisition and Management, Managing and Mining Sensor Data, Springer US, pp.9-50, ISBN 978-1-4614-6308-5, 2013.
[11] Q.A. Bakhtiar, K. Makki and N. Pissinou, Data Reduction in Low Powered Wireless Sensor Networks, Wireless Sensor Networks - Technology and Applications, Chapter 8, pp. 171-186, ISBN 978-953-51-0676-0, Published: July 18, 2012.
[12] J. Smalls; W. Yue; L. Xi; C. Zehuang; K.W. Tang, Health monitoring systems for massive emergency situations, Systems, Applications and Technology Conference, LISAT ’09, IEEE Long Island, pp.1-11, 2009.
[13] A. Alahmadi; B. Soh, A smart approach towards a mobile e-health monitoring system architecture, International Conference on Research and Innovation in Information Systems (ICRIIS), pp.1-5, 2011.
[14] X. Haitao; T. Li; H. Ogai; Z. Xiaohong; T. Otawa; S. Umeda; T. Tsuji, The health monitoring system based on distributed data aggregation for WSN used in bridge diagnosis, SICE Annual Conference 2010, Proceedings of, 2010, pp.2134-2138.
[15] J. Bringel Filho, N. AgoulmineEvaluation of Quality of Context Information in U-Health Smart Homes In: IGI Global. (Org.). Telemedicine and E-Health Services, Policies and Applications: Advancements and Developments. Hershey PA: IGI Global, 2012.
[16] J. Bringel Filho, A.D. Miron, I. Satoh, J. Gensel,H. MartinModeling and Measuring Quality of Context Information in Pervasive Environments. In: 24th IEEE International Conference on Advanced Information Networking and Applications, Perth, Australia. AINA 2010. Los Alamitos, CA: ACM, 2010. v. 24. p. 690-697, 2010.
[17] J. Bringel Filho, N. AgoulmineA Quality-Aware Approach for Resolving Context Conflicts in Context-Aware Systems In: 9th IEEE/IFIP International Conference on Embedded and Ubiquitous Computing, Melbourne. EUC, 2011.
[18] C.G.N Carvalho; D.G. Gomes; N. Agoulmine; J.N. Souza, Improving Prediction Accuracy for WSN Data Reduction by Applying Multivariate Spatio-Temporal Correlation, Sensors, Vol.11, No11, pp.10010-10037, 2011.
[19] G. Anastasi; M. Conti; M.D. Francesco; A. Passarella, Energy conservation in wireless sensor networks: A survey, Ad Hoc Networks, Vol.7, No3, pp.537-568, 2009.
[20] J. Yick; B. Mukherjee; D. Ghosal, Wireless sensor network survey, Computer Networks, Vol.52, pp.2292-2330, 2008.
[21] P. Chulsung, P.H. Chou, B. Ying, R. Matthews, A. Hibbs, An ultra-wearable, wireless, low power ECG monitoring system. Biomedical Circuits and Systems Conference, 2006. BioCAS. IEEE, vol., no., pp.241,244, 2006.
[22] J. Li, A. Deshpande and S. Khuller, On computing compression trees for data collection in wireless sensor networks, Proceedings of the 29th conference on Information communications, INFOCOM’10, ISBN 978-1-4244-5836-3, San Diego, California, USA, pp.2115-2123, IEEE Press, 2010.
[23] L. Chong, W. Kui and P. Jian, An Energy-Efficient Data Collection Framework for Wireless Sensor Networks by Exploiting Spatiotemporal Correlation, Parallel and Distributed Systems, IEEE Transactions on, Vol. 18, No 7, pp.1010-1023, July, 2007.
[24] J. Hongbo, J. Shudong and W. Chonggang, Prediction or Not? An Energy-Efficient Framework for Clustering-Based Data Collection in Wireless Sensor Networks, Parallel and Distributed Systems, IEEE Transactions on, Vol.22, No.6, pp. 1064-1071, 2011.
[25] M. Hazewinkel, Daubechies wavelets, Encyclopedia of Mathematics, Springer, ISBN 978-1- 55608-010-4, 2001.
[26] S. Santini and K. Romer, An Adaptive Strategy for Quality-Based Data Reduction in Wireless Sensor Networks, Proc. INSS, 2006.
[27] A. Skordylis, A. Guitton and N. Trigoni, Correlation-based data dissemination in traffic monitoring sensor networks, Proceedings of the 2006 ACM CoNEXT conference, CoNEXT ’06, ISBN 1-59593-456-1, Lisboa, Portugal, ACM, 2006.
[28] C.J. Debono and N.P. Borg, The Implementation of an Adaptive Data Reduction Technique for Wireless Sensor Networks, Signal Processing and Information Technology, ISSPIT 2008, IEEE International Symposium on, pp.402-406, 2008.
[29] C. Wei and I.J. Wassell, Energy efficient signal acquisition via compressive sensing in wireless sensor networks, Wireless and Pervasive Computing (ISWPC), 6th International Symposium on, pp.1-6, 2011.
Publicado
28/07/2014
Como Citar
VALÉRIA, Olga; RIBEIRO, Anderson; LEAL, Liliam; LEMOS, Marcus; CARVALHO, Carlos; B. FILHO, José; AGOULMINE, Nazim.
A Wavelet-Based Data Reduction Approach for Sensor-Rich Ubiquitous Systems. In: SIMPÓSIO BRASILEIRO DE COMPUTAÇÃO UBÍQUA E PERVASIVA (SBCUP), 6. , 2014, Brasília.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2014
.
p. 170-179.
ISSN 2595-6183.
