A Hybrid Deep Learning Models: Optimization for Forecasting Wireless 6G IoT Network

New specifications for 6G mobile networks will surpass the performance targets of 5G networks. As more 5G networks are deployed, their limits become obvious, encouraging the exploration of networks using 6G as the future solution. This paper covers 6G technology's core privacy and security concerns. Monitoring real-time structures need secured wireless sensor networks (WSNs). Denial of service (DoS) outbreaks increase secure risks for WSNs and potentially undermine the whole system. This study presents a new approach to control and optimize the security of 6G wireless networks based on blockchain technology by means of a machine learning method. Utilizing a blockchain users datagram transit protocol in conjunction with reinforcement projection regression, this research manages security of organized 6G wireless sensor net. The network is then optimized via artificially democratized cuckoo glowworm remora optimizations. Numerous network characteristics pertaining to accuracy, end-to-end latency, packet delivery ratio, energy efficiency, and throughput have formed the basis of the simulation findings. Reduced network traffic may be achieved by figuring out which node and route are best for data transfer. 98% of Throughput, energy effectiveness of 96%, 97% accuracy, end to end latency of 51%, and packet delivery ratio of 95% were all achieved by the suggested method.