<abstract> <p>In the livestock industry, wireless sensor networks (WSNs) play a significant role in monitoring many fauna health statuses and behaviors. Energy preservation in WSNs is considered one of the critical, complicated tasks since the sensors are coupled to constrained resources. Therefore, the clustering approach has proved its efficacy in preserving energy in WSNs. In recent studies, various clustering approaches have been introduced that use optimization techniques to improve the network lifespan by decreasing energy depletion. Yet, they take longer to converge and choose the optimal cluster heads in the network. In addition, the energy is exhausted quickly in the network. This paper introduces a novel optimization technique, i.e., an artificial rabbits optimization algorithm-based energy efficient cluster formation (EECHS-ARO) approach in a WSN, to extend the network lifetime by minimizing the energy consumption rate. The EECHS-ARO technique balances the search process in terms of enriched exploration and exploitation while selecting the optimal cluster heads. The experimentation was carried out on a MATLAB 2021a platform with varying sensor nodes. The obtained results of EECHS-ARO are contrasted with other existing approaches via teaching–learning based optimization algorithm (TLBO), ant lion optimizer (ALO) and quasi oppositional butterfly optimization algorithm (QOBOA). The proposed EECHS-ARO enriches the network lifespan by ~15% and improves the packet delivery ratio by ~5%.</p> </abstract>

<abstract> <p>The central remote servers are essential for storing and processing data for cloud computing evaluation. However, traditional systems need to improve their ability to provide technical data security solutions. Many data security challenges and complexities await technical solutions in today's fast-growing technology. These complexities will not be resolved by combining all secure encryption techniques. Quantum computing efficiently evolves composite algorithms, allowing for natural advances in cyber security, forensics, artificial intelligence, and machine learning-based complex systems. It also demonstrates solutions to many challenging problems in cloud computing security. This study proposes a user-storage-transit-server authentication process model based on secure keys data distribution and mathematical post-quantum cryptography methodology. The post-quantum cryptography mathematical algorithm is used in this study to involve the quantum computing-based distribution of security keys. It provides security scenarios and technical options for securing data in transit, storage, user, and server modes. Post-quantum cryptography has defined and included the mathematical algorithm in generating the distributed security key and the data in transit, on-storage, and on-editing. It has involved reversible computations on many different numbers by super positioning the qubits to provide quantum services and other product-based cloud-online access used to process the end-user's artificial intelligence-based hardware service components. This study will help researchers and industry experts prepare specific scenarios for synchronizing data with medicine, finance, engineering, and banking cloud servers. The proposed methodology is implemented with single-tenant, multi-tenant, and cloud-tenant-level servers and a database server. This model is designed for four enterprises with 245 users, and it employs integration parity rules that are implemented using salting techniques. The experimental scenario considers the plain text size ranging from 24 to 8248 for analyzing secure key data distribution, key generation, encryption, and decryption time variations. The key generation and encryption time variations are 2.3233 ms to 8.7277 ms at quantum-level 1 and 0.0355 ms to 1.8491 ms at quantum-level 2. The key generation and decryption time variations are 2.1533 ms to 19.4799 ms at quantum-level 1 and 0.0525 ms to 3.3513 ms at quantum-level 2.</p> </abstract>