An Efficient Incremental Learning Bioinspired Model for Improving QoS of Blockchain-Based Large-Scale Wireless Networks

Providing Quality of Service (QoS) under a variety of network conditions and security threats has become more difficult as the demand for large-scale wireless networks based on blockchain technology has increased. We address these issues and significantly raise the overall QoS of blockchain-based networks in this paper by presenting an effective incremental learning bioinspired model This work is necessary because large-scale wireless networks must significantly improve their energy efficiency, throughput, delay reduction, and packet delivery ratio. Existing models frequently have difficulty meeting these demands and reducing the effects of different attacks, including Finney, Distributed Denial of Service (DDoS), Man-in-the-Middle (MITM), Sybil, and Masquerading scenarios. We suggest a novel strategy that combines two optimization techniques to get around these restrictions. In order to create sidechains, we first use Grey Wolf Optimization (GWO), which improves network partitioning and scalability in blockchain-based networks. Our model efficiently distributes the computational load and boosts system performance by dynamically adjusting the sidechain formation. In order to choose the best miner nodes for data mining between network nodes, we integrate Q Learning in the second step. The Q Learning algorithm makes intelligent decisions about the best miner nodes by taking into consideration parameters like throughput, latency, and energy efficiency. This deft choice of miner nodes improves network performance and QoS overall while optimizing data mining process. Through a thorough examination of spatial and temporal parameters, consensus is attained, allowing the system to assess the accuracy and dependability of mined blocks. This guarantees the blockchain network's integrity and security. Results from experiments show how effective our suggested model is. Our model improves energy efficiency by 8.5 percent, delays are cut by 10.4 percent, throughput is increased by 9.5 percent, and the packet delivery ratio is raised by 2.5% even when there are multiple attacks. Our model improves the resilience and security of blockchain-based large-scale wireless networks by effectively reducing the effects of attacks including Finney, DDoS, MITM, Sybil, and Masquerading. Overall, the large-scale wireless networks based on blockchain are significantly advanced by our suggested model. It is appropriate for a wide range of applications in areas like the Internet of Things (IoT), smart cities, and critical infrastructure systems because it offers a comprehensive solution to improve QoS, energy efficiency, and security.