Trustworthy and Efficient Routing Algorithm for IoT-FinTech Applications Using Nonlinear Lévy Brownian Generalized Normal Distribution Optimization

The huge advancement in the field of communication has pushed innovation pace toward a new concept context Internet Things (IoT) named IoT for Financial Technology applications (IoT-FinTech). main intention is to leverage businesses’ income and reducing cost by facilitating benefits enabled IoT-FinTech technology. To do so, some challenging problems that mainly related routing protocols such highly dynamic, unreliable (due mobility), widely distributed network need be carefully addressed. This article, therefore, focuses on developing trustworthy efficient mechanism used data traffic over mobile networks. A nonlinear Lévy Brownian generalized normal distribution optimization (NLBGNDO) algorithm proposed solve problem finding an optimal path from source destination sensor nodes forwarding FinTech’s data. We also propose objective function maintaining trustworthiness selected relay-node candidates introducing trust-based friendship measured applied during each selection process. formulated model considering node’s residual energy, experienced response time, internode distance (to figure out density/sparsity ratio nodes). Results demonstrate our could maintain very wise decisions period comparison with other methods.