Data Aggregation, Spatio-Temporal Correlation and Energy-Aware Solutions to Perform Data Collection in Wireless Sensor Networks
Resumo
Este trabalho discute diferentes abordagens para agregação de dados e a exploração da correlação espaço-temporal dos dados em Redes de Sensores sem Fio (RSSFs). Na tese, nós investigamos e propusemos soluções que são adequadas para muitos cenários diferentes em RSSFs. Os algoritmos propostos reduzem o número de mensagens necessárias para criar uma estrutura de roteamento, maximizam o número de rotas sobrepostas, selecionam rotas com a maior taxa de agregação, e realizam a transmissão de dados agregados de forma confiável. Além disso, os nossos algoritmos têm sido extensivamente comparado com os que estão disponíveis na literatura e os resultados mostram que eles são possíveis alternativas para realizar a agregação e correlação espaçotemporal de dados durante o processo de roteamento em redes de sensores. Esta tese resultou em quatro artigos em periódicos e 18 trabalhos em conferências.Referências
Chakrabarty, K., Member, S., Iyengar, S. S., Qi, H., and Cho, E. (2002). Grid coverage for surveillance and target location in distributed sensor networks. IEEE Transactions on Computers, 51:1448–1453.
Deligiannakis, A. and Kotidis, Y. (2008). Geosensor networks. In Nittel, S., Labrinidis, A., and Stefanidis, A., editors, Book chapter: Exploiting Spatio-temporal Correlations for Data Processing in Sensor Networks, pages 45–65. Springer-Verlag.
Le, T. D., Pham, N. D., and Choo, H. (2008). Towards a distributed clustering scheme based on spatial correlation in wsns. In Wireless Communications and Mobile Computing Conference, 2008. IWCMC ’08. International, pages 529 –534.
Liu, L., Member, S., and Yu, P. S. (2007). Asap: An adaptive sampling approach to data collection in sensor networks. IEEE Transactions on Parallel and Distributed Systems, 2007:1766–1783.
Min, J.-K. and Chung, C.-W. (2010). Edges: Efficient data gathering in sensor networks using temporal and spatial correlations. J. Syst. Softw., 83:271–282.
Pham, N. D., Le, T. D., Park, K., and Choo, H. (2010). Sccs: Spatiotemporal clustering and compressing schemes for efficient data collection applications in wsns. International Journal of Communication Systems, 23:1311–1333.
Schmid, U. and Schossmaier, K. (2001). How to reconcile fault-tolerant interval intersection with the lipschitz condition. Distrib. Comput., 14:101–111.
Villas, L., Boukerche, A., Ramos Filho, H., Oliveira, H., Araujo, R., and Loureiro, A. (2012a). Drina: A lightweight and reliable routing approach for innetwork aggregation in wireless sensor networks. Computers, IEEE Transactions on, PP(99):1.
Villas, L. A., Boukerche, A., Araujo, R. B., and Loureiro, A. A. (2012b). Data Aggregation, Spatio-Temporal Correlation and Energy-Aware Solutions to perform Data Collection in Wireless Sensor Networks. PhD thesis, Federal University of Minas Gerais, [link].
Villas, L. A., Boukerche, A., de Oliveira, H. A., de Araujo, R. B., and Loureiro, A. A. (2011). A spatial correlation aware algorithm to perform efficient data collection in wireless sensor networks. Ad Hoc Networks.
Deligiannakis, A. and Kotidis, Y. (2008). Geosensor networks. In Nittel, S., Labrinidis, A., and Stefanidis, A., editors, Book chapter: Exploiting Spatio-temporal Correlations for Data Processing in Sensor Networks, pages 45–65. Springer-Verlag.
Le, T. D., Pham, N. D., and Choo, H. (2008). Towards a distributed clustering scheme based on spatial correlation in wsns. In Wireless Communications and Mobile Computing Conference, 2008. IWCMC ’08. International, pages 529 –534.
Liu, L., Member, S., and Yu, P. S. (2007). Asap: An adaptive sampling approach to data collection in sensor networks. IEEE Transactions on Parallel and Distributed Systems, 2007:1766–1783.
Min, J.-K. and Chung, C.-W. (2010). Edges: Efficient data gathering in sensor networks using temporal and spatial correlations. J. Syst. Softw., 83:271–282.
Pham, N. D., Le, T. D., Park, K., and Choo, H. (2010). Sccs: Spatiotemporal clustering and compressing schemes for efficient data collection applications in wsns. International Journal of Communication Systems, 23:1311–1333.
Schmid, U. and Schossmaier, K. (2001). How to reconcile fault-tolerant interval intersection with the lipschitz condition. Distrib. Comput., 14:101–111.
Villas, L., Boukerche, A., Ramos Filho, H., Oliveira, H., Araujo, R., and Loureiro, A. (2012a). Drina: A lightweight and reliable routing approach for innetwork aggregation in wireless sensor networks. Computers, IEEE Transactions on, PP(99):1.
Villas, L. A., Boukerche, A., Araujo, R. B., and Loureiro, A. A. (2012b). Data Aggregation, Spatio-Temporal Correlation and Energy-Aware Solutions to perform Data Collection in Wireless Sensor Networks. PhD thesis, Federal University of Minas Gerais, [link].
Villas, L. A., Boukerche, A., de Oliveira, H. A., de Araujo, R. B., and Loureiro, A. A. (2011). A spatial correlation aware algorithm to perform efficient data collection in wireless sensor networks. Ad Hoc Networks.
Publicado
23/07/2013
Como Citar
VILLAS, Leandro A.; ARAUJO, Regina B. de; LOUREIRO, Antonio A. F..
Data Aggregation, Spatio-Temporal Correlation and Energy-Aware Solutions to Perform Data Collection in Wireless Sensor Networks. In: CONCURSO DE TESES E DISSERTAÇÕES (CTD), 26. , 2013, Maceió/AL.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2013
.
p. 23-28.
ISSN 2763-8820.
