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Summary: Communicating large amounts of data requires an encryption solution that ensures fast and secure data transfer. Unfortunately, it faced privacy and security issues, resulting in higher power consumption, data scarcity, energy, diversity, data reliability, low security, and packet loss in the communication process. The goal of this research is to ensure data transfer while conserving energy and protecting data transmission in WSNs. Therefore, this paper proposes a multipath routing protocol using the HGSPSO algorithm, which is a combination of hybrid gravitational search algorithm (HGSA) and particle swarm optimization (PSO), to securely transmit data from one node to another node under public and private key cryptography. The PSO approach is utilized to resolve the energy hole problem by selecting the cluster head in the sink coverage area. The RSA can be employed for data encryption, data decryption, key generation, and sharing. The metrics like acceleration constants and inertial weights are used for the HGSPSO algorithm. The result of this implementation demonstrated that the scheme had a low energy consumption of 36%, a maximum packet delivery ratio (PDR) of 96%, a higher throughput rate of 88%, and a low packet loss of 39%. [ABSTRACT FROM AUTHOR]

Industry 4.0 is a term used to refer to the fourth industrial revolution, characterized by the introduction of new technologies, such as the Internet of Things, Big Data, and artificial intelligence (AI). As the number of connected devices in industrial settings grows, energy optimization of such sensors becomes increasingly essential. This paper proposes an energy optimization framework for sensor nodes in Industry 4.0. The framework is based on energy efficiency, energy conservation, and energy harvesting principles. It is designed to optimize the energy consumption of sensor nodes while maintaining their performance. The framework includes dynamic power management, scheduling, and harvesting techniques to reduce energy consumption while maintaining performance. In addition, the framework provides a comprehensive approach to energy optimization, including advanced analytics and AI to predict energy consumption and optimize energy use. The proposed model reached 96.93% sensitivity, 91.36% false discovery rate, 11.28% false omission rate, 90.12% prevalence threshold, and 91.24% threat score. The proposed framework is expected to improve the performance of sensor nodes in Industry 4.0, enabling increased efficiency and cost savings. [ABSTRACT FROM AUTHOR]

Summary: Wireless sensor networks are an indispensable part of the present industrial and environmental scenario, with the everlasting challenges to increase network lifetime and reduce energy consumption. This paper presents an energy‐efficient adaptive clustering (EEAC) model, which is built upon the existing Low Energy Adaptive Clustering Hierarchy (LEACH) protocol with mobile sink and rendezvous nodes. The nodes are energy aware and participate in cluster head selection only if their current energy is higher than the average energy for the round. The idle nodes in the rendezvous region, which do not act as rendezvous nodes undergo clustering within themselves for the efficient routing of data. The EEAC inspires local clustering successfully conserves the energy of continuous long‐range transmission and enhances network lifetime. The proposed scheme proves to be remarkably better than the previous schemes, especially in large network cross‐sections. It improves alive nodes' performance for around 50% more rounds than the optimizing‐LEACH and the remaining energy curves improve around 500 rounds for 250 mx250 m network. For 350 mx350 m and 450 mx450 m networks, the results improved much in EEAC. In addition, the improvement in stability period is 450%, 578%, and 198% for the network cross‐section 150 mx150 m, 200 mx200m, and 250 mx250 m, respectively. [ABSTRACT FROM AUTHOR]