Multi‐criteria approach for handling sophisticated data transmission over gateways in blockchain and Internet of Things (IoT) federated networks

Abstract In the real‐world scenario, the Internet of Things (IoT) and blockchain federated networks implicate smart devices for gathering the data and then directing the data to the destined nodes through gateway devices. A gateway usually supports a number of wireless sensor networks (WSNs) irrespective of their underlying communication technologies. Furthermore, a gateway usually supports a number of WSNs with blockchain master nodes, which employ diverse communication technologies. As a result, gateways are meant to play a versatile role without worrying about the underlying architecture, hardware, and software details of the connecting nodes in blockchain federated networks. This paper proposes a two‐phased methodology for blockchain and IoT federated networks where efficient link selection based on a multi‐criteria‐based approach is performed. The dynamic gateway scheduling strategy is capable enough to support blockchain‐based transactions as well as communication in IoT devices. Furthermore, the proposed methodology enhances data transfer fairness for each gateway, resulting in efficient data transmission. Machine learning (ML) methods are also devised to analyze the status of the communication channels before employing the link selection mechanism. Then the selection of links is performed based on multi‐criteria statistical techniques. Finally, the scheduling is performed for selecting the appropriate gateway for channelizing the blockchain data speedily. The selection of links is made based on the dynamic status of the links. The usage of time series ML methods such as LSTM is made to predict the upcoming traffic on the links. Multiple criteria‐based statistical approaches are utilized for the selection of the link optimally. Finally, the scheduling is performed to fulfil the criteria of 6G networks, where network users are rewarded with seamless connectivity and uninterrupted services. The efficacy of the proposed two‐phased mechanism is demonstrated through simulation results. The results are demonstrated with respect to the total data conveyed, packet delivery ratio (PDR), energy consumed by the links, and throughput of the network. The PDR obtained by the proposed work for the high volume of data is 93% and for the low volume of data is 97%. The packet delay is reduced by 21% for low volume data and 19% for high volume data by the proposed methods. The data transmission is also optimized remarkably by the proposed method. The proposed fairness‐driven mechanism (FDM) is compared with the state‐of‐the‐art methods to prove the viability and robustness of the two‐phased proposed mechanism of link selection for data transmission over blockchain and IoT federated networks.