@Article{csse.2023.030786, AUTHOR = {I. Ambika, Surbhi Bhatia, Shakila Basheer, Pankaj Dadheech}, TITLE = {Optimized Resource Allocation and Queue Management for Traffic Control in MANET}, JOURNAL = {Computer Systems Science and Engineering}, VOLUME = {45}, YEAR = {2023}, NUMBER = {2}, PAGES = {1323--1342}, URL = {http://www.techscience.com/csse/v45n2/50408}, ISSN = {}, ABSTRACT = {A set of mobile devices that employs wireless transmission for communication is termed Mobile Ad hoc Networks (MANETs). Offering better communication services among the users in a centralized organization is the primary objective of the MANET. Due to the features of MANET, this can directly End-to-End Delay (EED) the Quality of Service (QoS). Hence, the implementation of resource management becomes an essential issue in MANETs. This paper focuses on the efficient Resource Allocation (RA) for many types of Traffic Flows (TF) in MANET. In Mobile Ad hoc Networks environments, the main objective of Resource Allocation (RA) is to process consistently available resources among terminals required to address the service requirements of the users. These three categories improve performance metrics by varying transmission rates and simulation time. For solving that problem, the proposed work is divided into Queue Management (QM), Admission Control (AC) and RA. For effective QM, this paper develops a QM model for elastic (EL) and inelastic (IEL) Traffic Flows. This research paper presents an AC mechanism for multiple TF for effective AC. This work presents a Resource Allocation Using Tokens (RAUT) for various priority TF for effective RA. Here, nodes have three cycles which are: Non-Critical Section (NCS), Entry Section (ES) and Critical Section (CS). When a node requires any resources, it sends Resource Request Message (RRM) to the ES. Elastic and inelastic TF priority is determined using Fuzzy Logic (FL). The token holder selects the node from the inelastic queue with high priority for allocating the resources. Using Network Simulator-2 (NS-2), simulations demonstrate that the proposed design increases Packet Delivery Ratio (PDR), decrease Packet Loss Ratio (PLR), minimise the Fairness and reduce the EED.}, DOI = {10.32604/csse.2023.030786} }