{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,20]],"date-time":"2026-01-20T02:17:44Z","timestamp":1768875464581,"version":"3.49.0"},"reference-count":51,"publisher":"MDPI AG","issue":"21","license":[{"start":{"date-parts":[[2021,11,6]],"date-time":"2021-11-06T00:00:00Z","timestamp":1636156800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>One of the most important challenges in Wireless Sensor Networks (WSN) is the extension of the sensors lifetime, which are battery-powered devices, through a reduction in energy consumption. Using data prediction to decrease the amount of transmitted data is one of the approaches to solve this problem. This paper provides a comparison of deep learning methods in a dual prediction scheme to reduce transmission. The structures of the models are presented along with their parameters. A comparison of the models is provided using different performance metrics, together with the percent of points transmitted per threshold, and the errors between the final data received by Base Station (BS) and the measured values. The results show that the model with better performance in the dataset was the model with Attention, saving a considerable amount of data in transmission and still maintaining a good representation of the measured data.<\/jats:p>","DOI":"10.3390\/s21217375","type":"journal-article","created":{"date-parts":[[2021,11,7]],"date-time":"2021-11-07T20:42:54Z","timestamp":1636317774000},"page":"7375","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["Evaluation of Deep Learning Methods in a Dual Prediction Scheme to Reduce Transmission Data in a WSN"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0699-5160","authenticated-orcid":false,"given":"Carlos R.","family":"Morales","sequence":"first","affiliation":[{"name":"Department of Electrical and Electronic Engineering, Universidade Federal de Santa Catarina, Florian\u00f3polis 88040-900, Brazil"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1647-0421","authenticated-orcid":false,"given":"Fernando","family":"Rangel de Sousa","sequence":"additional","affiliation":[{"name":"Department of Electrical and Electronic Engineering, Universidade Federal de Santa Catarina, Florian\u00f3polis 88040-900, Brazil"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6615-6134","authenticated-orcid":false,"given":"Valner","family":"Brusamarello","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, Universidade Federal do Rio Grande do Sul, Porto Alegre 91501-970, Brazil"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4163-6422","authenticated-orcid":false,"given":"Nestor C.","family":"Fernandes","sequence":"additional","affiliation":[{"name":"Traceback Technologies, Rua Ant\u00f4nia dos Santos Silveira, Florian\u00f3polis 88090-145, Brazil"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,11,6]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1130","DOI":"10.1109\/JPROC.2005.849717","article-title":"Wireless Technology in Industrial Networks","volume":"93","author":"Willig","year":"2005","journal-title":"Proc. IEEE"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Senouci, M.R., and Mellouk, A. (2016). Wolisz. Deploying Wireless Sensor Networks: Theory and Practice, ISTE Press Ltd.","DOI":"10.1016\/B978-1-78548-099-7.50001-5"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"2826","DOI":"10.1016\/j.comcom.2007.05.024","article-title":"A survey on clustering algorithms for wireless sensor networks","volume":"30","author":"Abbasi","year":"2007","journal-title":"Comput. Commun."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"537","DOI":"10.1016\/j.adhoc.2008.06.003","article-title":"Energy conservation in wireless sensor networks: A survey","volume":"7","author":"Anastasi","year":"2009","journal-title":"Ad. Hoc. Netw."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Sharma, S., Bansal, R.K., and Bansal, S. (2013, January 21\u201323). Issues and Challenges in Wireless Sensor Networks. Proceedings of the 2013 International Conference on Machine Intelligence and Research Advancement, Katra, India.","DOI":"10.1109\/ICMIRA.2013.18"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Krishna, G., Singh, S.K., Singh, J.P., and Kumar, P. (2018, January 26\u201327). Energy conservation through data prediction in wireless sensor networks. Proceedings of the 3rd International Conference on Internet of Things and Connected Technologies (ICIoTCT), Jaipur, India.","DOI":"10.2139\/ssrn.3172770"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Song, Y., Luo, J., Liu, C., and He, W. (2015, January 20\u201322). Periodicity-and-Linear-Based Data Suppression Mechanism for WSN. Proceedings of the 2015 IEEE Trustcom\/BigDataSE\/ISPA, Helsinki, Finland.","DOI":"10.1109\/Trustcom.2015.516"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/2996356","article-title":"A survey about prediction-based data reduction in wireless sensor networks","volume":"49","author":"Dias","year":"2016","journal-title":"ACM Comput. Surv. (CSUR)"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Morales, C.R., de Sousa, F.R., Brusamarello, V., and Fernandes, N.C. (2021, January 17\u201320). Multivariate Data Prediction in a Wireless Sensor Network based on Sequence to Sequence Models. Proceedings of the 2021 IEEE International Instrumentation and Measurement Technology Conference (I2MTC), Glasgow, UK.","DOI":"10.1109\/I2MTC50364.2021.9459957"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1068","DOI":"10.1109\/JPROC.2020.2983857","article-title":"Time Series Forecasting for Self-Aware Systems","volume":"108","author":"Bauer","year":"2020","journal-title":"Proc. IEEE"},{"key":"ref_11","unstructured":"Adhikari, R., and Agrawal, R.K. (2013). An introductory study on time series modeling and forecasting. arXiv."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Samal, K.K., Babu, K.S., Das, S.K., and Acharaya, A. (2019, January 16\u201318). Time series based air pollution forecasting using SARIMA and prophet model. Proceedings of the 2019 International Conference on Information Technology and Computer Communications, Singapore.","DOI":"10.1145\/3355402.3355417"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Wang, L., Wang, X., Chen, A., Jin, X., and Che, H. (2020). Prediction of Type 2 Diabetes Risk and Its Effect Evaluation Based on the XGBoost Model. Healthcare, 8.","DOI":"10.3390\/healthcare8030247"},{"key":"ref_14","unstructured":"Niu, Y. (November, January 30). Walmart Sales Forecasting using XGBoost algorithm and Feature engineering. Proceedings of the International Conference on Big Data & Artificial Intelligence & Software Engineering (ICBASE), Bangkok, Thailand."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Aliyu, F., Umar, S., and Al-Duwaish, H. (2019, January 15\u201317). A survey of applications of artificial neural networks in wireless sensor networks. Proceedings of the 8th International Conference on Modeling Simulation and Applied Optimization (ICMSAO), Manama, Bahrain.","DOI":"10.1109\/ICMSAO.2019.8880364"},{"key":"ref_16","unstructured":"Gamboa, J.C. (2017). Deep learning for time-series analysis. arXiv."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1038\/s41591-018-0268-3","article-title":"Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network","volume":"25","author":"Hannun","year":"2019","journal-title":"Nat. Med."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"917","DOI":"10.1007\/s10618-019-00619-1","article-title":"Deep learning for time series classification: A review","volume":"33","author":"Fawaz","year":"2019","journal-title":"Data Min. Knowl. Discov."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1421","DOI":"10.1007\/s10994-019-05815-0","article-title":"Temporal pattern attention for multivariate time series forecasting","volume":"108","author":"Shih","year":"2019","journal-title":"Mach. Learn."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","article-title":"Long Short-Term Memory","volume":"9","author":"Hochreiter","year":"1997","journal-title":"Neural Comput."},{"key":"ref_21","unstructured":"Wu, N., Green, B., Ben, X., and O\u2019Banion, S. (2020). Deep transformer models for time series forecasting: The influenza prevalence case. arXiv."},{"key":"ref_22","unstructured":"Goodfellow, I., Bengio, Y., and Courville, A. (2016). Deep Learning, MIT Press."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"107398","DOI":"10.1016\/j.ymssp.2020.107398","article-title":"1D convolutional neural networks and applications: A survey","volume":"151","author":"Kiranyaz","year":"2021","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_24","unstructured":"Sutskever, I., Vinyals, O., and Le, Q.V. (2014, January 8\u201313). Sequence to sequence learning with neural networks. Proceedings of the 27th International Conference on Neural Information Processing Systems, Montreal, QC, Canada."},{"key":"ref_25","unstructured":"Bahdanau, D., Cho, K.H., and Bengio, Y. (2015, January 7\u20139). Neural machine translation by jointly learning to align and translate. Proceedings of the 3rd International Conference on Learning Representations, ICLR 2015\u2014Conference Track Proceedings, San Diego, CA, USA."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Luong, M.T., Pham, H., and Manning, C.D. (2015, January 17\u201321). Effective approaches to attention-based neural machine translation. Proceedings of the Conference Proceedings EMNLP 2015: Conference on Empirical Methods in Natural Language Processing, Lisbon, Portugal.","DOI":"10.18653\/v1\/D15-1166"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Lai, G., Chang, W.C., Yang, Y., and Liu, H. (2018, January 8\u201312). Modeling long- and short-term temporal patterns with deep neural networks. Proceedings of the 41st International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR, Ann Arbor, MI, USA.","DOI":"10.1145\/3209978.3210006"},{"key":"ref_28","first-page":"5243","article-title":"Enhancing the locality and breaking the memory bottleneck of transformer on time series forecasting","volume":"32","author":"Li","year":"2019","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_29","unstructured":"Li, Y., Sun, R., and Horne, R. (October, January 30). Deep learning for well data history analysis. Proceedings of the SPE Annual Technical Conference and Exhibition. OnePetro, Calgary, AB, Canada."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"3545","DOI":"10.1007\/s11276-019-01950-7","article-title":"An improved adaptive dual prediction scheme for reducing data transmission in wireless sensor networks","volume":"25","author":"Liazid","year":"2019","journal-title":"Wirel. Netw."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"L\u00f3pez-Ardao, J.C., Rodr\u00edguez-Rubio, R.F., Su\u00e1rez-Gonz\u00e1lez, A., Rodr\u00edguez-P\u00e9rez, M., and Sousa-Vieira, M.E. (2021). Current Trends on Green Wireless Sensor Networks. Sensors, 21.","DOI":"10.3390\/s21134281"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"6736","DOI":"10.1109\/JIOT.2019.2911295","article-title":"An Energy-Efficient Dual Prediction Scheme Using LMS Filter and LSTM in Wireless Sensor Networks for Environment Monitoring","volume":"6","author":"Shu","year":"2019","journal-title":"IEEE Internet Things J."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"58","DOI":"10.1016\/j.comcom.2017.08.002","article-title":"The impact of dual prediction schemes on the reduction of the number of transmissions in sensor networks","volume":"112","author":"Dias","year":"2017","journal-title":"Comput. Commun."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Shen, Y., and Li, X. (2008, January 29\u201331). Wavelet Neural Network Approach for Dynamic Power Management in Wireless Sensor Networks. Proceedings of the International Conference on Embedded Software and Systems, Chengdu, China.","DOI":"10.1109\/ICESS.2008.36"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"1311","DOI":"10.1007\/s11277-019-06614-5","article-title":"Clustering and compressive data gathering in wireless sensor network","volume":"109","author":"Pacharaney","year":"2019","journal-title":"Wirel. Pers. Commun."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"50669","DOI":"10.1109\/ACCESS.2019.2910886","article-title":"A spatial-temporal correlation approach for data reduction in cluster-based sensor networks","volume":"7","author":"Tayeh","year":"2019","journal-title":"IEEE Access"},{"key":"ref_37","unstructured":"Abboud, A., Yazbek, A.-K., Cances, J.-P., and Meghdadi, V. (2016). Forecasting and skipping to Reduce Transmission Energy in WSN. arXiv."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"335","DOI":"10.1109\/TIM.2009.2023818","article-title":"An Adaptive Sampling Algorithm for Effective Energy Management in Wireless Sensor Networks With Energy-Hungry Sensors","volume":"59","author":"Alippi","year":"2018","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"656","DOI":"10.1109\/TVT.2010.2102375","article-title":"A Predictive Energy-Efficient Technique to Support Object-Tracking Sensor Networks","volume":"60","author":"Samarah","year":"2011","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Fathy, Y., Barnaghi, P., and Tafazolli, R. (2018, January 5\u20138). An adaptive method for data reduction in the Internet of Things. Proceedings of the IEEE World Forum on Internet of Things, WF-IoT 2018\u2014Proceedings, Singapore.","DOI":"10.1109\/WF-IoT.2018.8355187"},{"key":"ref_41","unstructured":"Arbi, I.B., Derbel, F., and Strakosch, F. (2017, January 22\u201325). Forecasting methods to reduce energy consumption in WSN. Proceedings of the 2017 IEEE International Instrumentation and Measurement Technology Conference (I2MTC), Turin, Italy."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"1708","DOI":"10.1109\/JSEN.2015.2504106","article-title":"Data reduction in wireless sensor networks: A hierarchical LMS prediction approach","volume":"16","author":"Tan","year":"2015","journal-title":"IEEE Sens. J."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Deng, H., Guo, Z., Lin, R., and Zou, H. (2019, January 23\u201327). Fog computing architecture-based data reduction scheme for WSN. Proceedings of the IEEE 2019 1st International Conference on Industrial Artificial Intelligence (IAI), Shenyang, China.","DOI":"10.1109\/ICIAI.2019.8850817"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s13638-019-1511-4","article-title":"Data prediction model in wireless sensor networks based on bidirectional LSTM","volume":"2019","author":"Cheng","year":"2019","journal-title":"Eurasip J. Wirel. Commun. Netw."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"117883","DOI":"10.1109\/ACCESS.2019.2937098","article-title":"Multi-Step Data Prediction in Wireless Sensor Networks Based on One-Dimensional CNN and Bidirectional LSTM","volume":"7","author":"Cheng","year":"2019","journal-title":"IEEE Access"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"1325","DOI":"10.1007\/s11277-015-2690-x","article-title":"Prediction Models for Energy Efficient Data Aggregation in Wireless Sensor Network","volume":"84","author":"Sinha","year":"2015","journal-title":"Wirel. Pers. Commun."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Das, R., Ghosh, S., and Mukherjee, D. (2018, January 22\u201325). Bayesian Estimator Based Weather Forecasting using WSN. Proceedings of the 2018 3rd International Conference and Workshops on Recent Advances and Innovations in Engineering (ICRAIE), Jaipur, India.","DOI":"10.1109\/ICRAIE.2018.8710410"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Chreim, B., Nassar, J., and Habib, C. (2021, January 9\u201312). Regression-based Data Reduction Algorithm for Smart Grids. Proceedings of the 2021 IEEE 18th Annual Consumer Communications & Networking Conference (CCNC), Las Vegas, NV, USA.","DOI":"10.1109\/CCNC49032.2021.9369555"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1016\/j.jnca.2016.12.027","article-title":"Damage prediction for wind turbines using wireless sensor and actuator networks","volume":"80","author":"Alves","year":"2017","journal-title":"J. Netw. Comput. Appl."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"3076","DOI":"10.3390\/s20113076","article-title":"Exploiting the RSSI Long-Term Data of a WSN for the RF Channel Modeling in EPS Environments","volume":"20","author":"Antayhua","year":"2020","journal-title":"Sensors"},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Pereira, M.D., Romero, R.A., Fernandes, N., and de Sousa, F.R. (2018, January 14\u201317). Path-loss and shadowing measurements at 2.4 GHz in a power plant using a mesh network. In Proceedings of the IEEE International Instrumentation and Measurement Technology Conference. Houston, TX, USA.","DOI":"10.1109\/I2MTC.2018.8409563"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/21\/7375\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T07:26:43Z","timestamp":1760167603000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/21\/7375"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,11,6]]},"references-count":51,"journal-issue":{"issue":"21","published-online":{"date-parts":[[2021,11]]}},"alternative-id":["s21217375"],"URL":"https:\/\/doi.org\/10.3390\/s21217375","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,11,6]]}}}