Time Series Optimization for Energy Prediction in Wi-Fi Infrastructures
- 1.1k Downloads
Access points play an important role in Wi-Fi networks and can provide us with useful information about the energy consumption according to the users’ behavior. If we predict the energy consumption in a determined access point, we can make easier the maintenance plans for the network infrastructure making the most adequate decisions about the placement of new devices or reinforcement of existing ones, for example. In this work, we propose an energy prediction methodology based on system identification, where the energy measured in the access points is represented as time series. The prediction results were reasonably good for an experimental environment consisting of ten access points in an academic building, modeling the energy patterns along some weeks. Moreover, we found an optimization problem where the main parameters of the identification model can be adjusted in order to provide results more accurate. Given the computational effort required for searching in depth the optimal values, we applied a genetic algorithm, which provided better results in less time with regard to a direct search method.
KeywordsWi-Fi networks Access point Energy consumption Time series System identification Prediction Optimization Genetic algorithm
This work was partially funded by the Government of Extremadura under the project IB16002, and by the AEI (State Research Agency, Spain) and the ERDF (European Regional Development Fund, EU) under the contract TIN2016-76259-P (PROTEIN project).
- 3.Shen, Y., Jiang, C., Quek, T., Zhang, H., Ren, Y.: Pricing equilibrium for data redistribution market in wireless networks with matching methodology. In: 2015 IEEE International Conference on Communications (ICC), pp. 3051–3056 (2015)Google Scholar
- 4.Scellato, S., Musolesi, M., Mascolo, C., Latora, V., Campbell, A.: NextPlace: A Spatio-temporal Prediction Framework for Pervasive Systems. In: 2011 Proceedings of the 9th International Conference on Pervasive Computing, pp. 152–169 (2011)Google Scholar
- 5.Hamamoto, R., Takano, C., Obata, H., Ishida, K.: Improvement of throughput prediction method for access point in multi-rate WLANs considering media access control and frame collision. In: 2015 Third International Symposium on Computing and Networking (CANDAR), pp. 227–233 (2015)Google Scholar
- 7.Gmach, D., Rolia, J., Cherkasova, L., Kemper, A.: Workload analysis and demand prediction of enterprise data center applications. In: Proceedings of the 2007 IEEE 10th International Symposium on Workload Characterization, pp. 171–180 (2007)Google Scholar
- 10.Gendreau, M., Potvin, J. (eds.): Handbook of Metaheuristics. Springer (2010)Google Scholar
- 11.Fogel, G., Corne, D.: Evolutionary computation in bioinformatics. Morgan Kaufmann Publishers (2003)Google Scholar
- 12.Reeves, C., Rowe, J.: Genetic Algorithms. Principles and perspectives. A guide to GA Theory. Kluwer Academic Publisher, EE.UU (2003)Google Scholar