Abstract
In a sensor cloud system, the sensors collect data and send it to the cloud system so that the end-users can access the data. When the frequency of data transmission is high, more power is consumed which reduces the finite battery lifetime of sensors. To reduce the data transmission between sensor and cloud without compromising the frequency of availability of data to end-users, an efficient hybrid forecasting method is developed to forecast the sensor data within the cloud and the end-users get the forecasted data at the same frequency with minimal error. In the proposed hybrid method, the sensor data is first modeled using the support vector machine (SVM) with Gaussian kernel function (SVMG) and SVM with linear kernel function (SVML). The forecasted values of SVMG and SVML are modeled using the artificial neural network to get the true forecasts. To assess the effectiveness of the proposed method, hourly temperature sensor data of Delhi is considered, and the obtained results are compared with the state-of-the-art deep learning, machine learning, ensemble, naïve and hybrid models. In the proposed approach, the sensor communicates with the cloud either every two hours (in case of one-step-ahead forecasting) or six hours (in case of five-step-ahead forecasting), but the user gets the forecasted hourly data from the cloud. Using the proposed approach, the energy consumption is reduced by 50% and 83.56% in comparison to the traditional approach in 1-step-ahead and 5-step-ahead forecasting without compromising much in accuracy.
Similar content being viewed by others
Data Availability
The datasets analyzed during the current study are available in the open-source Kaggle repository.
References
Yuriyama, M., & Kushida, T. (2010). Sensor-cloud infrastructure-physical sensor management with virtualized sensors on cloud computing. In 2010 13th International Conference on Network-Based Information Systems (pp. 1–8). IEEE.
Ahmed, K., & Gregory, M. (2011). Integrating wireless sensor networks with cloud computing. In 2011 Seventh International Conference on Mobile Ad-hoc and Sensor Networks (pp. 364–366). IEEE.
You, P., Li, H., Peng, Y., & Li, Z. (2013). An integration framework of cloud computing with wireless sensor networks. Ubiquitous Information Technologies and Applications (pp. 381–387). Dordrecht: Springer.
Alamri, A., Ansari, W. S., Hassan, M. M., Hossain, M. S., Alelaiwi, A., & Hossain, M. A. (2013). A survey on sensor-cloud: Architecture, applications, and approaches. International Journal of Distributed Sensor Networks, 9(2), 917923.
Mekala, M. S., & Viswanathan, P. (2020). A survey: Energy-efficient sensor and VM selection approaches in green computing for X-IoT applications. International Journal of Computers and Applications, 42(3), 290–305.
Panigrahi, S., & Behera, H. S. (2017). A hybrid ETS–ANN model for time series forecasting. Engineering applications of artificial intelligence, 66, 49–59.
Panigrahi, S., & Behera, H. S. (2019). Nonlinear time series forecasting using a novel self-adaptive TLBO-MFLANN model. International Journal of Computational Intelligence Studies, 8(1–2), 4–26.
Panigrahi, S., & Behera, H. S. (2020). Time Series forecasting using Differential Evolution-Based ANN Modelling Scheme. Arabian Journal for Science and Engineering, 45(12), 11129–11146.
Pattanayak, R. M., Panigrahi, S., & Behera, H. S. (2020). High-order fuzzy time series forecasting by using membership values along with data and support Vector Machine. Arabian Journal for Science and Engineering, 45(12), 10311–10325.
Büyükşahin, Ü. Ç., & Ertekin, Ş. (2019). Improving forecasting accuracy of time series data using a new ARIMA-ANN hybrid method and empirical mode decomposition. Neurocomputing, 361, 151–163.
Qin, M., Li, Z., & Du, Z. (2017). Red tide time series forecasting by combining ARIMA and deep belief network. Knowledge-Based Systems, 125, 39–52.
Singh, S. N., & Mohapatra, A. (2019). Repeated wavelet transform based ARIMA model for very short-term wind speed forecasting. Renewable energy, 136, 758–768.
Vaishali, A., Ramakrishnan, R., & Subasini, A. (2019). Weather Prediction Model using Savitzky-Golay and Kalman Filters. Procedia Computer Science, 165, 449–455.
Chattopadhyay, S., & Chattopadhyay, G. (2010). Univariate modelling of summer-monsoon rainfall time series: Comparison between ARIMA and ARNN. Comptes Rendus Geoscience, 342(2), 100–107.
Li, X., Han, Z., Zhao, T., Zhang, J., & Xue, D. (2021). Modeling for indoor temperature prediction based on time-delay and Elman neural network in air conditioning system. Journal of Building Engineering, 33, 101854.
Bao, X., Jiang, D., Yang, X., & Wang, H. (2021). An improved deep belief network for traffic prediction considering weather factors. Alexandria Engineering Journal, 60(1), 413–420.
Sabzehgar, R., Amirhosseini, D. Z., & Rasouli, M. (2020). Solar power forecast for a residential smart microgrid based on numerical weather predictions using artificial intelligence methods. Journal of Building Engineering, 32, 101629.
Nourani, V., Sayyah-Fard, M., Alami, M. T., & Sharghi, E. (2020). Data pre-processing effect on ANN-based prediction intervals construction of the evaporation process at different climate regions in Iran. Journal of Hydrology, 588, 125078.
Qing, X., & Niu, Y. (2018). Hourly day-ahead solar irradiance prediction using weather forecasts by LSTM. Energy, 148, 461–468.
Xiang, Y., Gou, L., He, L., Xia, S., & Wang, W. (2018). A SVR–ANN combined model based on ensemble EMD for rainfall prediction. Applied Soft Computing, 73, 874–883.
Panigrahi, S., Pattanayak, R. M., Sethy, P. K., & Behera, S. K. (2021). Forecasting of Sunspot Time Series using a hybridization of ARIMA, ETS and SVM methods. Solar Physics, 296(1), 1–19.
Wang, T., Li, Y., Wang, G., Cao, J., Bhuiyan, M. Z. A., & Jia, W. (2017). Sustainable and efficient data collection from WSNs to cloud. IEEE Transactions on Sustainable Computing, 4(2), 252–262.
Chatterjee, S., Sarkar, S., & Misra, S. (2015). Energy-efficient data transmission in sensor-cloud. In 2015 applications and innovations in Mobile Computing (AIMoC) (pp. 68–73). IEEE.
Abed, S., Al-Shayeji, M., & Ebrahim, F. (2019). A secure and energy-efficient platform for the integration of Wireless Sensor Networks and Mobile Cloud Computing. Computer Networks, 165, 106956.
Liu, S., Huang, G., Gui, J., Wang, T., & Li, X. (2020). Energy-aware MAC protocol for data differentiated services in sensor-cloud computing. Journal of Cloud Computing, 9(1), 1–33.
Lemos, M., Rabelo, R., Mendes, D., Carvalho, C., & Holanda, R. (2019). An approach for provisioning virtual sensors in sensor clouds. International Journal of Network Management, 29(2), 206.
Ullah, Z. (2020). A survey on hybrid, energy efficient and distributed (HEED) based energy efficient clustering protocols for wireless sensor networks. Wireless personal communications, 112(4), 2685–2713.
Das, K., & Das, S. (2022). Energy-efficient cloud integrated sensor based on clustering and multihop transmission. Science, Engineering and Health Studies, 16:22040001–22040001.
Sachan, S., Sharma, R., & Sehgal, A. (2021). Energy efficient scheme for better connectivity in sustainable mobile wireless sensor networks. Sustainable Computing: Informatics and Systems, 30, 100504.
Loganathan, S., & Arumugam, J. (2021). Energy efficient clustering algorithm based on particle swarm optimization technique for wireless sensor networks. Wireless Personal Communications, 119(1), 815–843.
Wen, J., Yang, J., Wang, T., Li, Y., & Lv, Z. (2022). Energy-efficient task allocation for reliable parallel computation of cluster-based wireless sensor network in edge computing. Digital Communications and Networks. 10.1016/j.dcan.2022.06.014
Subramanian, M., Narayanan, M., Bhasker, B., Gnanavel, S., Rahman, M. H., & Reddy, C. P. (2022). Hybrid Electro Search with Ant Colony Optimization Algorithm for Task Scheduling in a Sensor Cloud Environment for Agriculture Irrigation Control System. Complexity, Doi: 10.1155/2022/4525220
Guha, R. K., Gunter, C. A., & Sarkar, S. (2006). Fair coalitions for power-aware routing in wireless networks. IEEE transactions on mobile computing, 6(2), 206–220.
Vapnik, V. (1999). The nature of statistical learning theory. Springer science & business media, Berlin
Chen, X., Yang, J., Liang, J., & Ye, Q. (2012). Smooth twin support vector regression. Neural Computing and Applications, 21(3), 505–513.
Kukreja, M. (2017). Delhi weather data. Retrieved from https://www.kaggle.com/mahirkukreja/delhi-weather-data.
Gorriz, J. M., Puntonet, C. G., & Lang, E. (2004). Hybrid ICA-ANN model applied to volatile time series forecasting. In Proc. Int. Conf. on Artificial Intelligence and Applications (AIA) (Vol. 411, p. 815).
Wu, J., & Wei, J. (2007). Combining ICA with SVR for prediction of finance time Series. In 2007 IEEE International Conference on Automation and Logistics (pp. 95–100). IEEE.
Funding
No funding is received for this study.
Author information
Authors and Affiliations
Contributions
All authors contributed to the design and study’s conception. Data collection, Material preparation, and analysis were performed by Kalyan Das, Satyabrata Das, and Sibarama Panigrahi. The first draft of the manuscript was written by Kalyan Das and all authors commented on previous versions of the manuscript. The final manuscript was read and approved by all the authors.
Corresponding author
Ethics declarations
Conflict of interest
The authors don’t have any relevant financial or non-financial interests to disclose.
Additional information
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Das, K., Das, S. & Panigrahi, S. Energy-Efficient Forecasting of Temperature Data in Sensor Cloud System Using a Hybrid SVM-ANN Method. Wireless Pers Commun 129, 2929–2944 (2023). https://doi.org/10.1007/s11277-023-10265-y
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11277-023-10265-y