Sensoriamento Participativo de Regiões de Interesse com Descrição Adaptativa das Taxas de Amostragem

• Carlos Henrique de O. M. André UFRJ
• Dianne S. V. de Medeiros UFF
• Miguel Elias M. Campista UFRJ

DOI: https://doi.org/10.5753/sbrc.2018.2482

Abstract

Participatory Sensing (PS) is a new paradigm of collaborative networks which provides incentives for users to participate of sensing tasks on a Region of Interest (RoI). A challenge in wireless networking, however, is to balance the amount of data collected by each user so as not to impose an excessive load to the network. In this direction, this work proposes a centralized system to adapt the sample rate assigned to each participating sensor. The sample rate is computed as a function of the sample variation collected in a given RoI in the last time interval. The results obtained via simulations show a tradeoff between the sample rate and the number of participating users. The more participating users, the lower must be the individual sample rate and the lower will be the amount of data transferred. This strategy, even though requires a larger number of sensors, can increase the data delivery rate taking into account the available short contact times.

References

André, C. H. O. M., Medeiros, D. S. V., and Campista, M. E. M. (2017). A methodology to assess data consistency in vehicular networks using participatory sensing. Technical Report GTA-17-31, GTA/PEE/UFRJ.

Boulos, M. N. K., Wheeler, S., Tavares, C., and Jones, R. (2011). How smartphones are changing the face of mobile and participatory healthcare: an overview, with example from eCAALYX. Biomedical engineering online, 10(1):24:1–24:14.

Burke, J. A., Estrin, D., Hansen, M., Parker, A., Ramanathan, N., Reddy, S., and Srivastava, M. B. (2006). Participatory sensing. In World Sensor Web Workshop, ACM Sensys, pages 1–5.

Cruz, P., da Silva, F. F., Pacheco, R. G., Couto, R. S., Velloso, P. B., Campista, M. E. M., and Costa, L. H. M. (2017). SensingBus: um sistema de sensoriamento baseado em ˆOnibus urbanos. In Salão de Ferramentas do XXXV Simpósio Brasileiro de Redes de Computadores e Sistemas Distribuídos, pages 1–14.

D’Hondt, E., Stevens, M., and Jacobs, A. (2013). Participatory noise mapping works! an evaluation of participatory sensing as an alternative to standard techniques for environmental monitoring. Pervasive and Mobile Computing, 9(5):681–694.

Estrin, D., Chandy, K. M., Young, R. M., Smarr, L., Odlyzko, A., Clark, D., Reding, V., Ishida, T., Sharma, S., Cerf, V. G., et al. (2010). Participatory sensing: applications and architecture. IEEE Internet Computing, 14(1):12–42.

Jetcheva, J. G., Hu, Y.-C., PalChaudhuri, S., Saha, A. K., and Johnson, D. B. (2003). CRAWDAD dataset rice/ad hoc city (v. 2003-09-11). http://crawdad.org/ rice/ad_hoc_city/20030911/bus_mobility. traceset: bus mobility.

Liu, Y., Niu, J., Ma, J., Shu, L., Hara, T., and Wang, W. (2013). The insights of message delivery delay in vanets with a bidirectional trafc model. Journal of Network and Computer Applications, 36(5):1287–1294.

McCall, M. K. and Minang, P. A. (2005). Assessing participatory GIS for communitybased natural resource management: claiming community forests in Cameroon. Geographical Journal, 171(4):340–356.

Melo, P. C. F. et al. (2014). CSVM: Uma plataforma para crowdsensing móvel dirigida por modelos em tempo de execução. Master’s thesis, Universidade Federal de Goiás.

Miche, M. and Bohnert, T. M. (2009). The internet of vehicles or the second generation of telematic services. ERCIM News, 77:43–45.

Mohan, P., Padmanabhan, V. N., and Ramjee, R. (2008). Nericell: rich monitoring of road and trafc conditions using mobile smartphones. In ACM conference on Embedded network sensor systems, pages 323–336.

Ribeiro Neto, V., Medeiros, D. S. V., and Campista, M. E. M. (2016). Analysis of mobile user behavior in vehicular social networks. In International Conference Network of the Future (NOF 2016), pages 1–5.

Seattle.gov (2017). Seattle department of transportation. http://www.seattle.gov/transportation/sdotfaqs.htm. Acessado: 20/07/2017.

Weinschrott, H., Weisser, J., Durr, F., and Rothermel, K. (2011). Participatory sensing algorithms for mobile object discovery in urban areas. In IEEE International Conference on Pervasive Computing and Communications (PERCOM), pages 128–135.

Xiao, Y., Simoens, P., Pillai, P., Ha, K., and Satyanarayanan, M. (2013). Lowering the barriers to large-scale mobile crowdsensing. In 14th Workshop on Mobile Computing Systems and Applications, pages 9:1–9:6.

Yu, R., Zhang, Y., Wu, H., Chatzimisios, P., and Xie, S. (2013). Virtual machine live migration for pervasive services in cloud-assisted vehicular networks. In 8th International ICST Conference on Communications and Networking in China (CHINACOM), pages 540–545.

Zeng, Y. and Xiang, K. (2017). Adaptive sampling for urban air quality through participatory sensing. Sensors (Basel), 17(11):1–16.

Zhou, P., Zheng, Y., and Li, M. (2012). How long to wait?: predicting bus arrival time with mobile phone based participatory sensing. In 10th international conference on Mobile systems, applications, and services, pages 379–392.