A Bus-based Opportunistic Sensing Network

• Pedro Henrique Cruz Caminha UFRJ
• Luís Henrique Maciel Kosmalski Costa UFRJ
• Rodrigo de Souza Couto UFRJ

DOI: https://doi.org/10.5753/sbrc_estendido.2021.17154

Resumo

Smart city applications need data about the city, and this data must follow specific requirements. Two of these requirements are the maximum delivery delay and the minimum measurement frequency. Using buses to gather data and bus stops as gateways can be cost-effective, but data might not fit the application requirements. In this thesis, we present a model to minimize the delay of data delivery, a metric to estimate the coverage, and a prototype of the nodes of such a network. We use GPS data from the bus fleet of Rio de Janeiro to show that it is possible to cover a significant part of the city, fulfilling application requirements specified by the smart city literature.

Referências

Ali, J. and Dyo, V. (2017). Coverage and mobile sensor placement for vehicles on predetermined routes: a greedy heuristic approach. In ICETE 2017. SCITEPRESS.

Church, R. and Velle, C. R. (1974). The maximal covering location problem. Papers in regional science, 32(1):101–118.

Cruz, P. (2020). A bus-based opportunistic sensing network. PhD thesis, Universidade Federal do Rio de Janeiro, Rio de Janeiro.

Ekici, E., Gu, Y., and Bozdag, D. (2006). Mobility-based communication in wireless sensor networks. IEEE Communications Magazine, 44(7):56–62.

Gao, Y., Dong, W., Guo, K., Liu, X., Chen, Y., Liu, X., Bu, J., and Chen, C. (2016). Mosaic: A low-cost mobile sensing system for urban air quality monitoring. In INFOCOM’2016, pages 1–9. IEEE.

Ghafoor, S., Rehmani, M. H., Cho, S., and Park, S.-H. (2014). An efficient trajectory design for mobile sink in a wireless sensor network. Computers & Electrical Engineering, 40(7):2089–2100.

Kariv, O. and Hakimi, S. L. (1979). An algorithmic approach to network location problems. i: The p-centers. SIAM Journal on Applied Mathematics, 37(3):513–538.

Li, W., Santos, I., Delicato, F. C., Pires, P. F., Pirmez, L., Wei, W., Song, H., Zomaya, A., and Khan, S. (2016). System modelling and performance evaluation of a three-tier cloud of things. Future Generation Computer Systems.

Liu, B., Brass, P., Dousse, O., Nain, P., and Towsley, D. (2005). Mobility improves coverage of sensor networks. In MobiHoc 2005. ACM.

Marjovi, A., Arfire, A., and Martinoli, A. (2015). High resolution air pollution maps in urban environments using mobile sensor networks. In DCOSS’2015. IEEE.

Sanchez, L., Muñoz, L., Galache, J. A., Sotres, P., Santana, J. R., Gutierrez, V., Ramdhany, R., Gluhak, A., Krco, S., Theodoridis, E., et al. (2014). SmartSantander: IoT experimentation over a smart city testbed. Computer Networks, 61:217–238.

Umer, T., Amjad, M., Afzal, M. K., and Aslam, M. (2016). Hybrid rapid response routing approach for delay-sensitive data in hospital body area sensor network. In ICCCNT’2016. ACM.

Wong, J. L., Jafari, R., and Potkonjak, M. (2004). Gateway placement for latency and energy efcient data aggregation. In LCN’2004, pages 490–497. IEEE.

Internet of Zanella, A., Bui, N., Castellani, A., Vangelista, L., and Zorzi, M. (2014). things for smart cities. IEEE Internet of Things Journal, 1(1):22–32.

Zhao, D., Ma, H., Liu, L., and Zhao, J. (2013). On opportunistic coverage for urban sensing. In MASS’2013, pages 231–239. IEEE.

Zoysa, K. D., Keppitiyagama, C., Seneviratne, G. P., and Shihan, W. W. A. T. (2007). A public transport system based sensor network for road surface condition monitoring. In NSDR’2007. ACM.