An intelligent optimized users resource searching and nearest peer identification in Mobile networks

Abstract

SummaryMobile networks have advanced in many fields to share information with others. But finding the required resources for each user to share the data is a complex task because of the moving environment. So, the present research article has introduced a novel Chimp‐based Random Forest Peer Identification Framework (CbRFPIF) in the mobile environment. Initially, the required nodes were designed dynamically in the moving background. Hence, the designed wireless moving environment has included sender, peer, and receiver nodes. Moreover, the Chimp fitness model predicted the users' requested resources in the primary stage, then the nearest peer and the user locations were predicted. Consequently, the requested resource by users was distributed to the particular closest peers, and the peer forwarded the data to the specific users. Finally, the planned model is simulated in the NS2 environment, and the communication parameters were calculated and compared with other associated models, which gained the highest throughput and search rate.