Artificial Rabbits Optimized Fuzzy Elliptic Curve Signcryption for Secured Data Transmission in WSN - J, Paruvathavardhini; B, Sargunam (International Research Journal of Multidisciplinary Technovation, 2026)
Secure and energy-efficient data transmission is a significant challenge in Wireless Sensor Networks (WSN). Many energy-saving and security schemes already address this issue. However, these schemes fail to strike a balance between energy efficiency, performance, and security, making them unsuitable for WSNs with limited resources. This research proposes a new system for energy-efficient and secure data transfer in WSNs to enhance network reliability and lifetime. The scheme has two phases: forwarder node selection and secure data transfer. In the selection phase, a Gaussian Likelihood Censored Regression (GLCR) method selects the most efficient adjacent node as the forwarder node, based on factors such as energy, packet loss, and others. This continues until the sink node is reached, creating a reliable path. Once a path is set, secure data transfer occurs using a proposed Optimized Fuzzy Elliptic Curve-based Signcryption (OFECS) scheme. Here, the Artificial Rabbits Optimization Algorithm (AROA) fine-tunes Fuzzy Elliptic Curve (FEC) parameters like elliptic curve coefficients, cofactor, group order, generator point, and prime number. This process enhances cryptographic security while reducing computational overhead. The scheme is simulated using the NS2 tool. Finally, results show that GLCR-OFECS achieves a 99.3% Packet Delivery Ratio (PDR), 0.8 mJ of Energy Consumption (EC), 1.3 ms of End-to-End Delay (EED), 245 kbps of throughput, 512 seconds of network lifetime outperforming existing schemes. For 2000 data units, it achieves 98.57% data confidentiality, 97.74% data integrity, 26.1 MB space complexity, and 32 ms execution time, surpassing existing WSN schemes for data transfer.