We see that many automobile accidents are becoming a significant safety problem in the current society. Both human and animal lives are damaged by vehicle collisions. Due to breaking problems and quick response times when there is an impediment, more crashes happen. Therefore, the automated braking system for cars is presented in this work. Consumers are increasingly using their cellphones and the internet to make direct purchases rather than going to conventional brick-and-mortar establishments. Thanks to the internet, conducting business has never been quicker or easier than it is right now. A transmitter and a receiver are part of an ultrasonic system that is positioned in front of the vehicle. Life’s price cannot be calculated. This device measures the distance between obstacles using an ultrasonic sensor and notifies the driver when one is present. This essay also demonstrates the automated microcontroller's operation. The car will automatically stop when the safe separation distance is achieved. Its typical response time is 0.90 seconds, and its typical error rate based on the actual distance of the barrier is 11.2 seconds.
Read MoreDoi: https://doi.org/10.54216/IJWAC.040105
Vol. Volume 4 Issue. Issue 1 PP. 50-55, (2022)
Wireless sensor networks (WSN) is widely utilized for collecting data related to physical parameters from the environment. Security remains a challenging issue in the design of WSN. Security in WSN from Denial of Service (DoS) attack is an important security risk. This study introduces an artificial flora optimization algorithm with functional link neural network (AFOA-FLNN) model for DoS attack classification in WSN. The presented AFOA-FLNN model initially undergoes data pre-processing to transform the data into meaningful way. Secondly, the FLNN model is utilized for the effective recognition and classification of intrusions in WSN. Finally, the AFOA is exploited for optimally tuning the parameters involved in the FLNN model and results in enhanced performance. In order to demonstrate the better outcomes of the AFOA-FLNN model, a wide-ranging experimentation assessment on test data and the results pointed out the improved outcomes of the AFOA-FLNN model.
Read MoreDoi: https://doi.org/10.54216/IJWAC.040101
Vol. Volume 4 Issue. Issue 1 PP. 08-18, (2022)
Industrial robots have made it possible for manufacturers to make elevated low-cost products, which are thus major elements of advanced production technologies. Welding, cleaning, assembling, dismantling, slotting for computer chips, labeling requirements, stacking pallets, quality inspection, and monitoring are just a few of the applications for robotic systems. All the features are completed with a high level of endurance, speed, and accuracy. Multiple and competing criteria must be assessed simultaneously in a comprehensive selection analysis to identify the effectiveness of robots. To provide an automated machine for such arc machining operation, simple multi-criteria decision-making (MCDM) technique based on the COPRAS method is described in this work. The COPRAS method calculates significance weights using objective preferences and ranks the options. The COPRAS technique was used to determine the ranking order. The findings revealed that MCDM techniques for robot selection are extremely useful. The study's peculiarity is that it uses COPRAS MCDM approaches to select industrial arc welding robots.
Read MoreDoi: https://doi.org/10.54216/IJWAC.040102
Vol. Volume 4 Issue. Issue 1 PP. 19-30, (2022)
In Wireless sensor network (WSN) is a different type of ad-hoc network has of battery powered minimum cost sensor node with restricted computation and communication abilities used densely from a target region. The routing in WSN roles is an essential part due to their inherent energy storing ability and is suitable to extremely scalable networks. The trust in WSN roles an essential play in creating the network and addition or deletion of sensor nodes in a network very smooth and translucent. The trust was formalized as a computational method. This study develops a novel Trust Aware Manta Ray Foraging Optimization based Secure Routing (TAMRFO-SR) protocol for WSN. The proposed TAMRFO-SR model accomplishes optimal and secure routes for WSN. In addition, the TAMRFO-SR model is mainly stimulated by the fascinating characteristics of manta rays. Besides, the TAMRFO-SR model derives a fitness function with multiple parameters for secure route selection process. Moreover, the inclusion of trust in the route selection process assists in accomplishing enhanced security. The design of TAMRFO-SR model for secure data transmission depicts the novelty of the study. The experimental validation of the TAMRFO-SR model demonstrates better performance over the other approaches.
Read MoreDoi: https://doi.org/10.54216/IJWAC.040103
Vol. Volume 4 Issue. Issue 1 PP. 31-40, (2022)
Today’s networks are designed to reliably transmit traffic such as data from point to point i.e., unicasting, or from point to multipoint i.e., broadcasting. Multimedia places further demands on the network. First of all, multimedia traffic, such as audio or video, cannot tolerate delays in delivery like those tolerable by plain data transfer applications. Multimedia requires that data packets arrive on time and in the proper order at the client-side. Real-time protocols and quality of service guarantees addresses this issue. Furthermore, multimedia requires transmitting a large amount of traffic over the network and thus uses far more of the network’s bandwidth than in the case of those basic network operations. Multicasting offers a far more efficient way of transmitting such traffic over the Internet than unicasting or broadcasting ever would. The subject of this paper addresses the issue of efficient routing of such multicast traffic.
Read MoreDoi: https://doi.org/10.54216/IJWAC.040104
Vol. Volume 4 Issue. Issue 1 PP. 41-49, (2022)
Because of the lightning-fast expansion of the Internet of Things (IoT) technologies, an enormous amount of data has been produced. This traffic can be mined for information that can be used to identify and avoid intrusions into IoT networks. Despite the significant efforts that have been put into labeling Internet of Things traffic records, the total number of labeled records is still quite low, which makes it more difficult to detect intrusions. This study introduces a semi-supervised deep learning approach for intrusion detection (S2T-Net), in which we propose a temporal transformer module to empower the model to learn valuable interactions in cellular data. An improved spatial transformer is presented to capture local representation in the cellular traffic flow. At the same time, a multilevel semi-supervised training technique is used to account for the consecutive structure of the IoT traffic information. In order to provide effective real-time threat intelligence, the suggested S2T-Net can be tightly coupled into a cellular IoT network. Last but not least, empirical assessments on two current databases (CIC-IDS2017 and CIC-IDS2018) show that S2T-Net boosts intrusion detection accuracy and resilience while retaining resource-efficient computing.
Read MoreDoi: https://doi.org/10.54216/IJWAC.040106
Vol. Volume 4 Issue. Issue 1 PP. 56-68, (2022)