{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,31]],"date-time":"2026-01-31T22:28:35Z","timestamp":1769898515284,"version":"3.49.0"},"reference-count":33,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2019,1,14]],"date-time":"2019-01-14T00:00:00Z","timestamp":1547424000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100002643","name":"Kwangwoon University","doi-asserted-by":"publisher","award":["Not Applicable"],"award-info":[{"award-number":["Not Applicable"]}],"id":[{"id":"10.13039\/501100002643","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Small-to-medium scale smart buildings are an important part of the Internet of Things (IoT). Wireless Sensor Networks (WSNs) are the major enabler for smart control in such environments. Reliability is among the key performance requirements for many loss-sensitive IoT and WSN applications, while Energy Consumption (EC) remains a primary concern in WSN design. Error-prone links, traffic intense applications, and limited physical resources make it challenging to meet these service goals\u2014not only that these performance metrics often conflict with one another, but also require solving optimization problems, which are intrinsically NP-hard. Correctly forecasting Packet Delivery Ratio (PDR) and EC can play a significant role in different loss-sensitive application environments. With the ever-increasing availability of performance data, data-driven techniques are becoming popular in such settings. It is observed that a number of communication parameters like transmission power, packet size, etc., influence metrics like PDR and EC in diverse ways. In this work, different regression models including linear, gradient boosting, random forest, and deep learning are used for the purpose of predicting both PDR and EC based on such communication parameters. To evaluate the performance, a public dataset of the IEEE 802.15.4 network, containing measurements against more than 48,000 combinations of parameter configurations, is used. Results are evaluated using root mean square error and it turns out that deep learning achieves up to 98% accuracy for both PDR and EC predictions. These prediction results can help configure communication parameters taking into account the performance goals.<\/jats:p>","DOI":"10.3390\/s19020309","type":"journal-article","created":{"date-parts":[[2019,1,14]],"date-time":"2019-01-14T12:20:07Z","timestamp":1547468407000},"page":"309","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":32,"title":["Multi-Parametric Analysis of Reliability and Energy Consumption in IoT: A Deep Learning Approach"],"prefix":"10.3390","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7033-8937","authenticated-orcid":false,"given":"Muhammad","family":"Ateeq","sequence":"first","affiliation":[{"name":"Department of Computer Science, COMSATS University Islamabad, Wah Campus, Wah Cantt 47040, Pakistan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7378-8009","authenticated-orcid":false,"given":"Farruh","family":"Ishmanov","sequence":"additional","affiliation":[{"name":"Department of Electronics &amp; Communication Engineering, Kwangwoon University, Seoul 01897, Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6161-1310","authenticated-orcid":false,"given":"Muhammad Khalil","family":"Afzal","sequence":"additional","affiliation":[{"name":"Department of Computer Science, COMSATS University Islamabad, Wah Campus, Wah Cantt 47040, Pakistan"}]},{"given":"Muhammad","family":"Naeem","sequence":"additional","affiliation":[{"name":"Department of Electrical &amp; Computer Engineering, COMSATS University Islamabad, Wah Campus, Wah Cantt 47040, Pakistan"}]}],"member":"1968","published-online":{"date-parts":[[2019,1,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"393","DOI":"10.1016\/S1389-1286(01)00302-4","article-title":"Wireless sensor networks: A survey","volume":"38","author":"Akyildiz","year":"2000","journal-title":"Comput. Netw."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"10350","DOI":"10.3390\/s150510350","article-title":"WSN-and IOT-based smart homes and their extension to smart buildings","volume":"15","author":"Ghayvat","year":"2015","journal-title":"Sensors"},{"key":"ref_3","unstructured":"Mainetti, L., Patrono, L., and Vilei, A. (2011, January 15\u201317). Evolution of wireless sensor networks towards the internet of things: A survey. Proceedings of the 19th International Conference on Software, Telecommunications and Computer Networks (SoftCOM), Split, Croatia."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"2347","DOI":"10.1109\/COMST.2015.2444095","article-title":"Internet of things: A survey on enabling technologies, protocols, and applications","volume":"17","author":"Guizani","year":"2015","journal-title":"IEEE Commun. Surv. Tutor."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"509","DOI":"10.3390\/jsan2030509","article-title":"Wireless sensor network operating system design rules based on real-world deployment survey","volume":"2","author":"Strazdins","year":"2013","journal-title":"J. Sens. Actuator Netw."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"525","DOI":"10.1109\/COMST.2014.2363950","article-title":"A survey on cross-layer quality-of-service approaches in WSNs for delay and reliability-aware applications","volume":"18","author":"Mouftah","year":"2016","journal-title":"IEEE Commun. Surv. Tutor."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1185","DOI":"10.1109\/JIOT.2017.2701408","article-title":"QoS-aware deployment of IoT applications through the fog","volume":"4","author":"Brogi","year":"2017","journal-title":"IEEE Internet Things J."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"White, G., Palade, A., Cabrera, C., and Clarke, S. (2018, January 19\u201323). IoTPredict: Collaborative QoS prediction in IoT. Proceedings of the 2018 IEEE International Conference on Pervasive Computing and Communications (PerCom 2018), Athens, Greece.","DOI":"10.1109\/PERCOM.2018.8444598"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"550","DOI":"10.1109\/COMST.2016.2610578","article-title":"A Survey of Multi-Objective Optimization in Wireless Sensor Networks: Metrics, Algorithms, and Open Problems","volume":"19","author":"Fei","year":"2017","journal-title":"IEEE Commun. Surv. Tutor."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1996","DOI":"10.1109\/COMST.2014.2320099","article-title":"Machine learning in wireless sensor networks: Algorithms, strategies, and applications","volume":"16","author":"Alsheikh","year":"2014","journal-title":"IEEE Commun. Surv. Tutor."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"2923","DOI":"10.1109\/COMST.2018.2844341","article-title":"Deep Learning for IoT Big Data and Streaming Analytics: A Survey","volume":"20","author":"Mohammadi","year":"2018","journal-title":"IEEE Commun. Surv. Tutor."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1399","DOI":"10.1007\/s13042-018-0834-5","article-title":"A survey on application of machine learning for Internet of Things","volume":"9","author":"Cui","year":"2018","journal-title":"Int. J. Mach. Learn. Cybern."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Kulin, M., Fortuna, C., De Poorter, E., Deschrijver, D., and Moerman, I. (2016). Data-driven design of intelligent wireless networks: An overview and tutorial. Sensors, 16.","DOI":"10.3390\/s16060790"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"166","DOI":"10.1016\/j.comnet.2014.12.016","article-title":"Reliability in wireless sensor networks: A survey and challenges ahead","volume":"79","author":"Mahmood","year":"2015","journal-title":"Comput. Netw."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Baker, T., Lamb, D., Taleb-Bendiab, A., and Al-Jumeily, D. (2010, January 6\u20138). Facilitating Semantic Adaptation of Web Services at Runtime Using a Meta-Data Layer. Proceedings of the IEEE Developments in E-Systems Engineering (DESE), London, UK.","DOI":"10.1109\/DeSE.2010.44"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"207","DOI":"10.1016\/j.comnet.2018.08.023","article-title":"A continuous diversified vehicular cloud service availability framework for smart cities","volume":"145","author":"Aloqaily","year":"2018","journal-title":"Comput. Netw."},{"key":"ref_17","unstructured":"Aloqaily, M., Balasubramanian, V., Zaman, F., Al Ridhawi, I., and Jararweh, Y. (November, January 28). Congestion Mitigation in Densely Crowded Environments for Augmenting QoS in Vehicular Clouds. Proceedings of the 8th ACM Symposium on Design and Analysis of Intelligent Vehicular Networks and Applications, Montreal, QC, Canada."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Fahim, M., and Baker, T. (2017). Knowledge-Based Decision Support Systems for Personalized u-lifecare Big Data Services. Current Trends on Knowledge-Based Systems, Springer.","DOI":"10.1007\/978-3-319-51905-0_9"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1145\/2530535","article-title":"Data-driven link quality prediction using link features","volume":"10","author":"Liu","year":"2014","journal-title":"ACM Trans. Sens. Netw."},{"key":"ref_20","unstructured":"Werner-Allen, G., Swieskowski, P., and Welsh, M. (2005, January 24\u201327). Motelab: A wireless sensor network testbed. Proceedings of the IEEE 4th international symposium on Information processing in sensor networks, Los Angeles, CA, USA."},{"key":"ref_21","unstructured":"White, G., Palade, A., and Clarke, S. (2017, January 13\u201316). Qos prediction for reliable service composition in Iot. Proceedings of the International Conference on Service-Oriented Computing, M\u00e1laga, Spain."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1109\/TSC.2012.34","article-title":"Investigating QoS of real-world web services","volume":"7","author":"Zheng","year":"2014","journal-title":"IEEE Trans. Serv. Comput."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"White, G., Palade, A., Cabrera, C., and Clarke, S. (2017, January 26\u201329). Quantitative Evaluation of QoS Prediction in IoT. Proceedings of the 2017 47th Annual IEEE\/IFIP International Conference on Dependable Systems and Networks Workshop (DSN-W), Denver, CO, USA.","DOI":"10.1109\/DSN-W.2017.26"},{"key":"ref_24","unstructured":"Kulin, M., de Poorter, E., Kazaz, T., and Moerman, I. (2017, January 20\u201322). Poster: Towards a cognitive MAC layer: Predicting the MAC-level performance in Dynamic WSN using Machine learning. Proceedings of the 2017 International Conference on Embedded Wireless Systems and Networks, Uppsala, Sweden."},{"key":"ref_25","unstructured":"Merima, K., De Poorter, E., Kazaz, T., and Moerman, I. (2018, December 20). MAC-level performance dataset for 802.15.4 WSNs. Available online: https:\/\/zenodo.org\/record\/228613.XBr7OcQRWMo."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"669","DOI":"10.1016\/j.future.2017.06.020","article-title":"Collaborative QoS prediction with context-sensitive matrix factorization","volume":"82","author":"Wu","year":"2018","journal-title":"Future Gener. Comput. Syst."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"White, G., Palade, A., and Clarke, S. (2018, January 8\u201313). Forecasting qos attributes using lstm networks. Proceedings of the IEEE International Joint Conference on Neural Networks (IJCNN), Rio de Janeiro, Brazil.","DOI":"10.1109\/IJCNN.2018.8489052"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Cavallo, B., Di Penta, M., and Canfora, G. (2010, January 1\u20132). An empirical comparison of methods to support QoS-aware service selection. Proceedings of the ACM 2nd International Workshop on Principles of Engineering Service-Oriented Systems, Cape Town, South Africa.","DOI":"10.1145\/1808885.1808899"},{"key":"ref_29","unstructured":"White, G., Andrei, P., and Siobh\u00e1n, C. (2018, October 10). Sensor Data. Available online: https:\/\/www.scss.tcd.ie\/~whiteg5\/data\/QoS_data.zip."},{"key":"ref_30","unstructured":"Wu, H., Zhang, Z., Luo, J., Yue, K., and Hsu, C.H. (2018). Multiple Attributes QoS Prediction via Deep Neural Model with Contexts. IEEE Trans. Serv. Comput."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"5826","DOI":"10.1002\/cpe.3639","article-title":"Cloud service QoS prediction via exploiting collaborative filtering and location-based data smoothing","volume":"27","author":"Tang","year":"2015","journal-title":"Concurr. Comput. Pract. Exp."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Akbas, A., Yildiz, H.U., Ozbayoglu, A.M., and Tavli, B. (2018). Neural network based instant parameter prediction for wireless sensor network optimization models. Wirel. Netw., 1\u201314.","DOI":"10.1007\/s11276-018-1808-y"},{"key":"ref_33","unstructured":"Fu, S., and Zhang, Y. (2018, October 10). CRAWDAD Dataset Due\/Packet-Delivery (v. 2015-04-01). Available online: https:\/\/crawdad.org\/due\/packet-delivery\/20150401."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/19\/2\/309\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T12:25:48Z","timestamp":1760185548000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/19\/2\/309"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,1,14]]},"references-count":33,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2019,1]]}},"alternative-id":["s19020309"],"URL":"https:\/\/doi.org\/10.3390\/s19020309","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,1,14]]}}}