{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,29]],"date-time":"2026-05-29T11:23:09Z","timestamp":1780053789743,"version":"3.54.0"},"reference-count":35,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2022,3,9]],"date-time":"2022-03-09T00:00:00Z","timestamp":1646784000000},"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>Wireless networks and the Internet of things (IoT) have proven rapid growth in the development and management of smart environments. These technologies are applied in numerous research fields, such as security surveillance, Internet of vehicles, medical systems, etc. The sensor technologies and IoT devices are cooperative and allow the collection of unpredictable factors from the observing field. However, the constraint resources of distributed battery-powered sensors decrease the energy efficiency of the IoT network and increase the delay in receiving the network data on users\u2019 devices. It is observed that many solutions are proposed to overcome the energy deficiency in smart applications; though, due to the mobility of the nodes, lots of communication incurs frequent data discontinuity, compromising the data trust. Therefore, this work introduces a D2D multi-criteria learning algorithm for IoT networks using secured sensors, which aims to improve the data exchange without imposing additional costs and data diverting for mobile sensors. Moreover, it reduces the compromising threats in the presence of anonymous devices and increases the trustworthiness of the IoT-enabled communication system with the support of machine learning. The proposed work was tested and analyzed using broad simulation-based experiments and demonstrated the significantly improved performance of the packet delivery ratio by 17%, packet disturbances by 31%, data delay by 22%, energy consumption by 24%, and computational complexity by 37% for realistic network configurations.<\/jats:p>","DOI":"10.3390\/s22062115","type":"journal-article","created":{"date-parts":[[2022,3,10]],"date-time":"2022-03-10T02:10:35Z","timestamp":1646878235000},"page":"2115","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":21,"title":["Device-to-Device (D2D) Multi-Criteria Learning Algorithm Using Secured Sensors"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6657-9308","authenticated-orcid":false,"given":"Khalid","family":"Haseeb","sequence":"first","affiliation":[{"name":"Department of Computer Science, Islamia College Peshawar, Peshawar 25000, Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Amjad","family":"Rehman","sequence":"additional","affiliation":[{"name":"Artificial Intelligence and Data Analytics (AIDA) Lab, CCIS Prince Sultan University, Riyadh 11586, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3138-3801","authenticated-orcid":false,"given":"Tanzila","family":"Saba","sequence":"additional","affiliation":[{"name":"Artificial Intelligence and Data Analytics (AIDA) Lab, CCIS Prince Sultan University, Riyadh 11586, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Saeed Ali","family":"Bahaj","sequence":"additional","affiliation":[{"name":"MIS Department College of Business Administration, Prince Sattam Bin Abdulaziz University, Alkharj 16278, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0862-0533","authenticated-orcid":false,"given":"Jaime","family":"Lloret","sequence":"additional","affiliation":[{"name":"Instituto de Investigaci\u00f3n para la Gesti\u00f3n Integrada de Zonas Costeras, Universitat Politenica de Valencia, 46379 Gandia, Val\u00e8ncia, Spain"},{"name":"School of Computing and Digital Technologies, Staffordshire University, Stoke ST4 2DE, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,3,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Khelifi, F. (2020). Monitoring System Based in Wireless Sensor Network for Precision Agriculture, in Internet of Things (IoT), Springer.","DOI":"10.1007\/978-3-030-37468-6_24"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"e4563","DOI":"10.1002\/dac.4563","article-title":"Irrigation control system-data gathering in WSN using IOT","volume":"33","author":"Kumar","year":"2020","journal-title":"Int. J. Commun. Syst."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"157883","DOI":"10.1109\/ACCESS.2019.2950237","article-title":"Energy-efficient and coverage-guaranteed unequal-sized clustering for wireless sensor networks","volume":"7","author":"Gharaei","year":"2019","journal-title":"IEEE Access"},{"key":"ref_4","first-page":"4843","article-title":"Machine learning applications for precision agriculture: A comprehensive review","volume":"2020","author":"Sharma","year":"2020","journal-title":"IEEE Access"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"e4948","DOI":"10.1002\/dac.4948","article-title":"Efficient data uncertainty management for health industrial internet of things using machine learning","volume":"34","author":"Haseeb","year":"2021","journal-title":"Int. J. Commun. Syst."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"263","DOI":"10.1016\/j.csi.2011.03.004","article-title":"A review of wireless sensors and networks\u2019 applications in agriculture","volume":"36","author":"Abbasi","year":"2014","journal-title":"Comput. Stand. Interfaces"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"8836613","DOI":"10.1155\/2020\/8836613","article-title":"Wireless Sensor Network Applications in Healthcare and Precision Agriculture","volume":"2020","author":"Malik","year":"2020","journal-title":"J. Health Eng."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Saba, T., Haseeb, K., Din, I.U., Almogren, A., Altameem, A., and Fati, S.M. (2020). EGCIR: Energy-Aware Graph Clustering and Intelligent Routing Using Supervised System in Wireless Sensor Networks. Energies, 13.","DOI":"10.3390\/en13164072"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Rahman, G.M., and Wahid, K.A. (2020). LDAP: Lightweight Dynamic Auto-Reconfigurable Protocol in an IoT-Enabled WSN for Wide-Area Remote Monitoring. Remote Sens., 12.","DOI":"10.3390\/rs12193131"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1109\/MITP.2020.3031358","article-title":"A Machine-Learning-Based Approach for Autonomous IoT Security","volume":"23","author":"Saba","year":"2021","journal-title":"IT Prof."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Mazzia, V., Comba, L., Khaliq, A., Chiaberge, M., and Gay, P. (2020). UAV and machine learning based refinement of a satellite-driven vegetation index for precision agriculture. Sensors, 20.","DOI":"10.3390\/s20092530"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Liakos, G.K., Busato, P., Moshou, D., Pearson, S., and Bochtis, D. (2018). Machine learning in agriculture: A review. Sensors, 18.","DOI":"10.3390\/s18082674"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Haseeb, K., Din, I.U., Almogren, A., and Islam, N. (2020). An Energy Efficient and Secure IoT-Based WSN Framework: An Application to Smart Agriculture. Sensors, 20.","DOI":"10.3390\/s20072081"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"17430","DOI":"10.1109\/JSEN.2020.3017695","article-title":"FPDP: Flexible privacy-preserving data publishing scheme for smart agriculture","volume":"21","author":"Song","year":"2020","journal-title":"IEEE Sens. J."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"100048","DOI":"10.1016\/j.array.2020.100048","article-title":"Security challenges to smart agriculture: Current state, key issues, and future directions","volume":"8","author":"Albini","year":"2020","journal-title":"Array"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"200","DOI":"10.1016\/j.future.2017.06.018","article-title":"A secure user authentication and key-agreement scheme using wireless sensor networks for agriculture monitoring","volume":"84","author":"Ali","year":"2018","journal-title":"Future Gener. Comput. Syst."},{"key":"ref_17","unstructured":"Banerjee, A., Mitra, A., and Biswas, A. (2021, December 10). Wiley Online Library. Available online: https:\/\/onlinelibrary.wiley.com\/doi\/abs\/10.1002\/9781119769231.ch9."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Haseeb, K., Islam, N., Saba, T., Rehman, A., and Mehmood, Z. (2019). LSDAR: A Light-weight Structure based Data Aggregation Routing Protocol with Secure Internet of Things Integrated Next-generation Sensor Networks. Sustain. Cities Soc., 101995.","DOI":"10.1016\/j.scs.2019.101995"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"101802","DOI":"10.1016\/j.eti.2021.101802","article-title":"M-SMDM: A model of security measures using Green Internet of Things with Cloud Integrated Data Management for Smart Cities","volume":"24","author":"Rehman","year":"2021","journal-title":"Environ. Technol. Innov."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"23022","DOI":"10.1109\/ACCESS.2020.2970118","article-title":"Internet of things (IoT) for next-generation smart systems: A review of current challenges, future trends and prospects for emerging 5G-IoT scenarios","volume":"8","author":"Shafique","year":"2020","journal-title":"IEEE Access"},{"key":"ref_21","unstructured":"Garcia, M., Bri, D., Sendra, R., and Lloret, J. (2021, December 10). Available online: http:\/\/citeseerx.ist.psu.edu\/viewdoc\/summary?doi=10.1.1.681.7101."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"2337","DOI":"10.1007\/s12652-019-01359-2","article-title":"An improved energy efficient system for IoT enabled precision agriculture","volume":"11","author":"Agrawal","year":"2020","journal-title":"J. Ambient. Intell. Humaniz. Comput."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1016\/j.compag.2016.09.016","article-title":"Fuzzy based energy efficient sensor network protocol for precision agriculture","volume":"130","author":"Maurya","year":"2016","journal-title":"Comput. Electron. Agric."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"e4800","DOI":"10.1002\/dac.4800","article-title":"Precision agriculture with cluster-based optimal routing in wireless sensor network","volume":"34","author":"Agarkhed","year":"2021","journal-title":"Int. J. Commun. Syst."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"6078","DOI":"10.1007\/s11227-020-03501-w","article-title":"A cluster-tree-based energy-efficient routing protocol for wireless sensor networks with a mobile sink","volume":"77","author":"Lu","year":"2021","journal-title":"J. Supercomput."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"6242","DOI":"10.1109\/JIOT.2019.2960033","article-title":"Deep-reinforcement-learning-based QoS-aware secure routing for SDN-IoT","volume":"7","author":"Guo","year":"2019","journal-title":"IEEE Internet Things J."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"29355","DOI":"10.1109\/ACCESS.2019.2902371","article-title":"Lightweight reinforcement learning for energy efficient communications in wireless sensor networks","volume":"7","author":"Savaglio","year":"2019","journal-title":"IEEE Access"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"112079","DOI":"10.1109\/ACCESS.2019.2932086","article-title":"Energy-efficient mobile-sink sojourn location optimization scheme for consumer home networks","volume":"7","author":"Gharaei","year":"2019","journal-title":"IEEE Access"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Ullo, L.S., and Sinha, G. (2020). Advances in smart environment monitoring systems using IoT and sensors. Sensors, 20.","DOI":"10.3390\/s20113113"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Rehman, A., Haseeb, K., Fati, S.M., Lloret, J., and Pe\u00f1alver, L. (2021). Reliable Bidirectional Data Transfer Approach for the Internet of Secured Medical Things Using ZigBee Wireless Network. Appl. Sci., 11.","DOI":"10.3390\/app11219947"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1155\/2016\/3428730","article-title":"WDARS: A weighted data aggregation routing strategy with minimum link cost in event-driven WSNs","volume":"2016","author":"Mahdi","year":"2016","journal-title":"J. Sens."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Sennan, S., Balasubramaniyam, S., Luhach, A.K., Ramasubbareddy, S., Chilamkurti, N., and Nam, Y. (2019). Energy and delay aware data aggregation in routing protocol for Internet of Things. Sensors, 19.","DOI":"10.3390\/s19245486"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"237","DOI":"10.1613\/jair.301","article-title":"Reinforcement learning: A survey","volume":"4","author":"Kaelbling","year":"1996","journal-title":"J. Artif. Intell. Res."},{"key":"ref_34","first-page":"711","article-title":"An asynchronous clustering and mobile data gathering schema based on timer mechanism in wireless sensor networks","volume":"58","author":"Wang","year":"2019","journal-title":"Comput. Mater. Contin."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Wang, J., Gao, Y., Liu, W., Sangaiah, A.K., and Kim, H.-J. (2019). Energy efficient routing algorithm with mobile sink support for wireless sensor networks. Sensors, 19.","DOI":"10.3390\/s19071494"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/6\/2115\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T22:33:37Z","timestamp":1760135617000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/6\/2115"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,3,9]]},"references-count":35,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2022,3]]}},"alternative-id":["s22062115"],"URL":"https:\/\/doi.org\/10.3390\/s22062115","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,3,9]]}}}