Centralized Infrastructure-Aware Reliable Data Transaction Model in IoT-Enabled MANET and Cloud Using LDMOA and ELRNN

Mobile ad-hoc networks (MANETs) face significant challenges due to the lack of centralized infrastructure and efficient routing techniques, which hinder optimal data transfer. This paper addresses these challenges by proposing a Centralized Infrastructure-Aware, Reliable Data Transaction Model for IoT-enabled MANETs in a cloud environment. The model incorporates novel approaches to enhance network efficiency and security, including the Learning-based Multi-objective Optimization Algorithm (LDMOA) and Extended Long-Short Term Recurrent Neural Network (ELRNN). The proposed framework operates in several stages: node initialization, optimal cluster head selection using LDMOA, and clustering. Multipath generation and path detail extraction follow, after which a hash tree is constructed and stored on a fog server. The trustworthiness of each path is evaluated using the KT-Fuzzy decision-making algorithm. If the trust is high, LDMOA selects the optimal path for data transfer, which is then encrypted using the Extended Particle Collision Chaos (EPCC) algorithm. The path’s hash code is verified in the fog server, and the data undergoes intrusion detection. The data path is re-configured if an attack is detected; otherwise, secure data are forwarded to the cloud server. The intrusion detection process involves data collection, feature extraction, and classification, with ELRNN used to predict potential attacks. The proposed ELRNN model achieves an accuracy of 98.42%, significantly outperforming traditional methods such as RNN, RBM, DBN, and DNN, which have maximum accuracies of around 93.57%. This improvement highlights the effectiveness of the ELRNN in detecting intrusions in Blockchain-based IoT devices, showcasing enhanced performance in precision, recall, and overall reliability compared to existing techniques.