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An efficient localization-based secure resource allocation using e-fso with ss-ddnn-based cm-lsgeo techniques
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An efficient localization-based secure resource allocation using e-fso with ss-ddnn-based cm-lsgeo techniques
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Journal Article
An efficient localization-based secure resource allocation using e-fso with ss-ddnn-based cm-lsgeo techniques
2024
Overview
Complex applications, as well as multimedia services, are increasing swiftly with the substantial growth of Mobile Users (MUs) and IoT devices; thus, demanding extra computations along with higher data communication. Nevertheless, with limited computation power along with energy, these devices are still resource-constrained. Numerous methodologies have been developed for efficient Resource Allocation (RA) in the cloud; however, owing to the loss of network resources, energy consumption, along lower battery lifetime, the prevailing methodologies are deemed to be ineffective. So, by utilizing an Entropy-centric Fish Swarm Optimization Algorithm (E-FSO) with Soft Swish Deep Dense Neural Network (SS-DDNN)-guided Crossover Mutation – Linear Scaling-centered Golden Eagle Optimization (CM-LSGEO) technique, a practical localization-centric safe RA has been suggested to help with the previously mentioned issues. Initially, the mobile’s optimal location is detected by utilizing the E-FSO algorithm. Next, for the secure transmission of packets, the failure node is detected effectively by employing the SS-DDNN algorithm. By utilizing the CM-LSGEO algorithm, a model has been constructed to perform efficient RA. The performance of the suggested model is compared to that of the current models in the experimental assessment, which takes into account some metrics. The outcomes displayed that in comparison with the prevailing methodologies, the proposed framework is more efficient.
Publisher
Springer US,Springer Nature B.V
