This research presents a vehicle ID-based congestion aware message (CAM) for beacon signals on the vehicle environment. At the MAC protocol of the vehicle environment, enhanced vehicle ID-based analysis model is given first. With the automobile ID embedded in their separate CAMs, the model weights the randomized back-off numbers chosen by cars engaging in the back-off procedure. This leads to identifying a car ID-based randomized back-off code, which reduces the likelihood of a collision due to the identical back-off number. A traffic density based-congestion control algorithm (TDCCA) is suggested in this research. The revised mathematical approach surpasses previous work’s overall packet latency because just one-fourth of the congestion window is employed during the experiment. The research includes a congestion management method that adjusts the rate of CAM transmitted over the host controller to improve the efficiency of the model parameters. The method considers various circumstances, from nonsaturated to substantially saturated networks (in terms of congestion probability) and sparsely dispersed and teemed networks (in the form of vehicular intensity). The technique is run across various automobile ID-based back-off values for high-standard results analysis. The simulation outcomes in terms of packet delivery ratio, energy consumption, delay, success rate, and collision ensure the effectiveness of the TDCCA method. Even at high traffic densities, the automobile ID-based CAM following information method outperforms the typical fixed CAM frequency IEEE 802.11p, according to simulation findings for all back-off figures.

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A mobile ad-hoc network (MANET) uses an omni-directional antenna, which transmits and receives power from all directions, resulting in higher noise and interference. The interference effects can be minimized with the help of adaptive directional antennas. The proposed model for implementing adaptive antenna techniques in MANET is a cross layer design. Utilizing adaptive antennas, two nodes are able to communicate when both the transmitter’s and receiver’s unidirectional radiation beams are directing toward each other’s nodes. A cross layer methodology for dynamic topology control enables the interaction between medium access control layer and the routing layer for reaching the necessary quality of service (QoS) of various data packets. After the initialization of a network, the algorithm initially develops a topology and the routing techniques use this network topology to find out the route paths for data transmission. Later, based on the network scenario for ongoing transmissions and to obtain the necessary QoS, the topology gets altered by the topology control layer in order to obtain the optimized network with better performance. Simulation results show specifically, throughput and signal-to-noise ratio were increased by 33 % and 42 %, respectively.

Machine Learning concepts have raised executions in all knowledge domains, including the Internet of Thing (IoT) and several business domains. Quality of Service (QoS) has become an important problem in IoT surrounding since there is a vast explosion of connecting sensors, information and usage. Sensor data gathering is an efficient solution to collect information from spatially disseminated IoT nodes. Reinforcement Learning Mechanism to improve the QoS (RLMQ) and use a Mobile Sink (MS) to minimize the delay in the wireless IoT s proposed in this paper. Here, we use machine learning concepts like Reinforcement Learning (RL) to improve the QoS and energy efficiency in the Wireless Sensor Network (WSN). The MS collects the data from the Cluster Head (CH), and the RL incentive values select CH. The incentives value is computed by the QoS parameters such as minimum energy utilization, minimum bandwidth utilization, minimum hop count, and minimum time delay. The MS is used to collect the data from CH, thus minimizing the network delay. The sleep and awake scheduling is used for minimizing the CH dead in the WSN. This work is simulated, and the results show that the RLMQ scheme performs better than the baseline protocol. Results prove that RLMQ increased the residual energy, throughput and minimized the network delay in the WSN.

The 5G mobile telecommunication network is becoming known as one of the finest communication networks for transmitting and controlling emergencies due to its high bandwidth and short latency. The high-quality videos taken by a drone, an incorporated Internet of Things (IoT) gadget for recording in a catastrophe situation, are very helpful in controlling the crisis. The 5G mm-Wave frequency spectrum is susceptible to intrusion and has beam realignment concerns, which can severely reduce Transmission Control Protocol (TCP) effectiveness and destroy connections. High-speed devices and disaster zones with multiple barriers make this problem significantly worse. This research offers a Deep-Learning-oriented Congestion Control Approach (DLCCA) for a catastrophic 5G mm-Wave system to solve this problem. By analyzing the node's transmitted data, DLCCA predicts when the network will be disconnected and reconnected, adjusting the TCP congestion window accordingly. To accomplish this, the proposed approach estimates the bottleneck link's queue length using the average Round Trip Time (RTT) and its data collected across the connection. Consequently, the proposed approach can use this buffer size to examine the congestion state and differentiate it from the randomized loss situation. This would stop the window length from getting smaller, increasing the amount of data transferred and speeding up the recommended method. Additionally, DLCCA frees up bottleneck bandwidth. The research provides simulated tests for TCP DLCCA compared to Newreno, Cubic, Compound, and Westwood while sustaining a two-way connection under heavy load and a wide range of randomized loss rates using the networking simulation NS-2. Experimental results show that DLCCA performs better than other TCP variants and significantly boosts throughput.