Secure and Energy-Aware Data Transmission for IoT-WSNs with the Help of Cluster-Based Secure Optimal Routing

In a variety of applications like military, healthcare, and industrial monitoring, Internet of Things (IoT) centered Wireless Sensor Networks (WSNs) are broadly utilized. The data is transmitted from the source Sensor Nodes (SNs) to the destination SNs. These are likely to be done in a secure way; also, in less time. In this paper, via proposing a Mutation grounded Multi-Layer Perceptron (MUMLP), energy and secure aware optimal routing are executed to provide secure data transmission along with reducing the transmission time. For Data Aggregation (DA), by proposing the Boltzmann Selection Probability-centric Gravitational Search Algorithm (BSP-GSA), the optimal Cluster Heads (CHs) are selected. These are done after node initialization in the network. After that, the non-cluster members are combined with an adjacent CH to form a cluster. The data are gathered by the CHs as of the non-cluster members; also, the gathered data, which are encrypted utilizing the Improved Elliptical Curve Cryptography (IECC) mechanism, are secured. Through the optimal route selected by a Deep Learning (DL) algorithm (MUMLP) with the consideration of a novel fitness function, the encrypted data is then dispatched to the Base Station (BS). For user access, the data in the BS is stored in a cloud server. The BlockChain (BC)-enabled authentication, which permits only the authorized user for data access, is executed to prevent unauthorized access. To evaluate the proposed approach's efficiency, simulations are performed. The outcomes display that when analogized to the prevailing mechanisms, the presented algorithm performs data transmission in a secure as well as energy-aware manner.