Performance Evaluation of Network Security and Management Using Bioinspired Blockchain

Abstract

Blockchain consensus mechanisms are important to ensure the safe validation of transactions. However, the limitations of high computational complexity, energy consumption, and mining latency restrict the scalability of blockchain in large-scale IP-based and wireless network environments. Current methods mainly rely on single optimization methods without jointly optimizing miner selection and hash computation, resulting in inferior performance under dynamic network conditions. To fill this gap, this study presents a new hybrid bioinspired optimization framework for efficient blockchain mining, integrating Genetic Algorithm (GA), Firefly optimization, and Particle Swarm Optimization (PSO) into a unified architecture to take advantage of their complementary strengths. The proposed method uses both historical and real-time performance metrics to determine the best mining nodes. The Firefly algorithm is used to optimize the selection of hash ranges to reduce CPU workload. PSO is used to select high-performance neighboring nodes for collaborative mining. The model is implemented using the NS-2 simulator and tested over a network of 1000 wireless nodes under different consensus protocols. The experimental results illustrate 4.3% decrease in computational complexity, 4% decrease in energy consumption, and 5% decrease in mining delay. The model further reduces mining complexity by up to 34.2% and reduces the energy utilization from 24.5% to 16.6%, demonstrating its effectiveness for scalable and energy-efficient blockchain deployment.