IEEE Access (Jan 2024)
Energy-Efficient Aware and Secure Model for WSN Lifetime Enhancement Using HDASCII-AE and GEK-GRU
Abstract
In the era of wireless communication, the Wireless Sensor Network (WSN) lifetime improvement has become an active research area. Basically, a set of spatially distributed sensors that can monitor and exchange data with each other is called WSN. As these sensors are connected in an open area network, secure and energy-efficient data transmission is a significant challenge. Many investigators conducted numerous studies to elevate the performance and reliability of the WSN. However, numerous prevailing works did not adequately address multiple attack scenarios and also did not predict the network’s lifespan owing to the absence of effective clustering and path selection strategies. To address this problem, a secured and energy-efficient WSN using Hexa Decimal ASCII-based Arithmetic Encoding (HDASCII-AE) and Double Right Shift 2’s Compliment (DRS2C) with a Glorot Entropy Kernal-Gated Recurrent Unit (GEK-GRU) model is proposed in this paper. Once the WSN setup is designed, the WSN sensor nodes are initialized via the Fisher Median Naive Sharding-based K-Means (FMNS-K-Means) algorithm. Thereafter, the initialized nodes are clustered based on their features. After that, grounded on the Kendall Correlation-Serval Optimization Algorithm (KC-SOA), the localization of nodes is done. For ensuring secure data transmission, the paper utilizes the HDASCII-AE-based secure routing methodology and masks sender and receiver details by employing the DRS2C approach. For efficiently transferring the sensor data, the optimal paths are chosen based on the proposed KC-SOA. Then, the sensed data is subjected to a network lifetime prediction phase, where the nodes’ lifetime is forecasted by utilizing the GEK-GRU methodology. The data transmission will become slow if the node’s lifetime is drained; therefore, the process continues to choose other significant node features. To showcase the efficacy of the research methodology, a performance assessment is carried out. Thus, the experimental findings reveal that the proposed mechanism obtains 13874Kbps throughput, 4254mJ energy consumption, 7546ms clustering time, and 0.90% SSIM. Collectively, the proposed approach proficiently increases the WSN lifetime and has minimum energy consumption and complexity.
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