Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Article Faculty Publications -Articles Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Nisha, U. Nilabar; Manikandan, A.; Venkataramanan, C.; Ashok, J. Title A name given to the resource A novel mobile sink placement in wireless sensor network using deep maxout network based energy prediction with adjacent cell score Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived Wireless Networks;Volume;31;Issue;8;pp.4823-4838 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/s11276-025-04020-3" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/s11276-025-04020-3</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105018017339?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105018017339?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Nisha U.N., Department of CSE, Mahendra Institute of Technology, Mallasamudram, India; Manikandan A., Department of ECE, SRM Institute of Science and Technology, Kattankulathur, 603203, India; Venkataramanan C., Department of ECE, Sri Eshwar College of Engineering, Tamil Nadu, Coimbatore, 641202, India; Ashok J., School of Business and Management, CHRIST (Deemed to be University), Karnataka, Bengaluru, India Description An account of the resource The majority of Wireless Sensor Networks (WSNs) are made up of energy- and cost-efficient detecting nodes. Traditional wireless sensor networks encounter serious problems, including latency, network failure, and congestion, since they rely on individual base stations (BSs) to gather data from the whole network. Sensor nodes adjacent to the base station will use more energy because of excessive energy consumption and energy-hole constraints, affecting the network's life. Understanding the best place for mobile sink nodes can help alleviate this issue by lowering energy usage and extending the network's lifespan. In this paper, utilizing a deep learning-based energy prediction and neighbour cell score model, we build and construct an efficient method to locate mobile receivers using distance, expected energy, and fairness variables. Furthermore, a Deep Maximum Output Network (DMN) calculates the desired power. However, the minimum length, maximum residual energy, complete normalized right, maximum network lifespan, and maximum normalized throughput for our suggested neighbor-based cell scoring with Deep Maxout Network are 137.364, 30.903, 64.426, and 60.613, respectively. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025. Subject The topic of the resource Cluster head (CH); Deep maxout networks; Mobile aggregation nodes; Voronoi partition; Wireless sensor networks (WSN) Publisher An entity responsible for making the resource available Springer Relation A related resource ISSN: 10220038; CODEN: WINEF Language A language of the resource English Type The nature or genre of the resource Article Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online