Simultaneous Wireless Information and Power Transfer (SWIPT) integrated with non-orthogonal multiple access (NOMA) offers a promising solution for energy-efficient Internet of Things (IoT) applications in the context of increasingly scarce spectrum resources. This paper addresses the energy efficiency (EE) maximization problem in a downlink cooperative SWIPT-NOMA network, where user cooperation is employed to mitigate the near-far effect and enhance network performance. We formulate the EE optimization problem for a multi-user scenario by jointly optimizing the transmission time, the power allocation ratio, and the transmission power of the near user in the cooperative SWIPT-NOMA network, and we propose a cooperative SWIPT-NOMA energy efficiency allocation algorithm. Firstly, the fractional programming problem for EE maximization is transformed into a more tractable form using the Dinkelbach method. Subsequently, the resource allocation variables are iteratively updated via variable substitution, successive convex approximation, and the Lagrangian dual method until the algorithm converges. Extensive simulations are conducted to evaluate the performance of the proposed algorithm under various conditions and to compare it with existing schemes. The proposed algorithm enhances network energy efficiency while ensuring user throughput, providing a more efficient resource allocation solution for wireless communication networks.

As an important technology for eliminating redundant data, data deduplication significantly impacts today’s era of explosive data growth. In recent years, due to the rapid development of a series of related industries, such as ocean observation, ocean observation data has also shown a speedy growth trend, leading to the continuous increase in storage costs of ocean observation stations. Faced with the constant increase in data scale, our first consideration is to use data deduplication technology to reduce storage costs. While using duplicate data deletion technology to achieve our goals, we also need to pay attention to some of the actual situations of ocean observation stations. The fingerprint retrieval process in duplicate data deletion technology plays a key role in the entire process. Therefore, this paper proposes a fast retrieval strategy based on locally sensitive hashing. The fast retrieval algorithm based on locally sensitive hashing can enable us to quickly complete the retrieval process in duplicate data deletion technology and achieve the goal of saving computing resources. At the same time, we proposed a bucket optimization strategy for retrieval algorithms based on locally sensitive hashing. We utilized visual information to address the bottleneck problem in duplicate data deletion technology. At the end of the article, we conducted careful experiments to compare hash retrieval algorithms and concluded the strategy’s feasibility.