Abstract
The advance of the Wireless Sensor Network (WSN) technology opens many possibilities for several kinds of applications. This kind of network, though, presents as main limitation the lack of a permanent and reliable energy supply. Keep the energy supply of a WSN for long periods may constitute a significant obstacle for its implementation. There are many strategies to prolong the energy supply of a WSN, one of them, knows as Dual Prediction Scheme (DPS) is explored in this article. This work proposes a new DPS technique, called DPCAS (Dual Prediction with Cubic Adaptive Sampling), combining adaptive sampling with prediction models based in exponential time series. Simulations were carry on with the use of this new technique and the results were promising in quality of the generated data and energy save in the sensor nodes.
This is a preview of subscription content, log in via an institution to check access.
References
Al-Hoqani, N., Yang, S.: Adaptive sampling for wireless household water consumption monitoring. Procedia Eng. 119, 1356–1365 (2015)
Anastasi, G., Conti, M., Di Francesco, M., Passarella, A.: Energy conservation in wireless sensor networks: a survey. Ad Hoc Netw. 7, 537–568 (2009)
Carrabs, F., Cerulli, R., D’Ambrosio, C., Raiconi, A.: An exact algorithm to extend lifetime through roles allocation in sensor networks with connectivity constraints. Optim. Lett. (2016)
Carrabs, F., Cerulli, R., D’Ambrosio, C., Raiconi, A.: Extending Lifetime Through Partial Coverage And Roles Allocation in Connectivity-Constrained Sensor Networks. IFAC-PapersOnLine 49, 973–978 (2016)
Delicato, F.C., Pires, P.F.: Energy awareness and efficiency in wireless sensor networks: from physical devices to the communication link. In: Energy-Efficient Distributed Computing Systems, pp 673–707 (2012)
Gupta, M., Shum, L.V., Bodanese, E., Hailes, S.: Design and evaluation of an adaptive sampling strategy for a wireless air pollution sensor network. In: 2011 IEEE 36th Conference on Local Computer Networks, pp 1003–1010 (2011)
Ha, S., Rhee, I.: CUBIC: a new TCP-friendly high-speed TCP variant. ACM SIGOPS Oper. Syst. Rev. - Res. Dev. Linux kernel 42, 64–74 (2008)
Hanzák, T.: Improved Holt method for irregular time series. In: WDS 2008, pp 62–67 (2008)
Hyndman, R.J., Athanasopoulos, G.: Forecasting: Principles and Practice (2013)
Jamal, H., Sultan, K.: Performance analysis of TCP congestion control algorithms. Int. J. Comput. Commun. 2, 30–38 (2008)
Kurose, J.F., Ross, K.W.: Computer Networking: A Top-Down Approach, 6th edn. (2013)
Le Borgne, Y., Santini, S., Bontempi, G.: Adaptive model selection for time series prediction in wireless sensor networks. Sig. Process. 87, 3010–3020 (2007)
Leiserson, C.C.E., Rivest, R.R.L., Stein, C., Cormen, T.H.: Introduction to Algorithms, 3rd edn. MIT Press, Cambridge (2009)
Madden, S.: Intel Lab data (2004). http://db.csail.mit.edu/labdata/labdata.html
Miret, S.: Storage Wars: Batteries vs Supercapacitors. Berkeley Energy and Resources Collaborative 10 (2013)
Shnayder, V., Hempstead, M., Chen, B-R., et al.: Simulating the power consumption of large-scale sensor network applications. In: Proceedings of the 2nd International Conference on Embedded Networked Sensor Systems - SenSys 2004, p. 188 (2004)
Wright, D.J.: Forecasting data published at irregular time intervals using an extension of Holts method. Manage. Sci. 32, 499–510 (1986)
Acknowledgments
This work was partly supported by the Brazilian funding agencies CNPq and FAPERJ. Flavia C. Delicato, Luci Pirmez and Paulo F. Pires as CNPq fellows.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Monteiro, L.C., Delicato, F.C., Pirmez, L., Pires, P.F., Miceli, C. (2017). DPCAS: Data Prediction with Cubic Adaptive Sampling for Wireless Sensor Networks. In: Au, M., Castiglione, A., Choo, KK., Palmieri, F., Li, KC. (eds) Green, Pervasive, and Cloud Computing. GPC 2017. Lecture Notes in Computer Science(), vol 10232. Springer, Cham. https://doi.org/10.1007/978-3-319-57186-7_27
Download citation
DOI: https://doi.org/10.1007/978-3-319-57186-7_27
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-57185-0
Online ISBN: 978-3-319-57186-7
eBook Packages: Computer ScienceComputer Science (R0)