Underwater wireless sensor networks (UWSNs) have gained prominence in wireless sensor technology, featuring resource-limited sensor nodes deployed in challenging underwater environments. To address challenges like power consumption, network lifetime, node deployment, topology, and propagation delays, cooperative transmission protocols like co-operative (Co-UWSN) and co-operative energy-efficient routing (CEER) have been proposed. These protocols utilize broadcast capabilities and neighbor head node (NHN) selection for cooperative routing. This research introduces NBEER, a novel neighbor-based energy-efficient routing protocol tailored for UWSNs. NBEER aims to surpass the limitations of Co-UWSN and CEER by optimizing NHNS and cooperative mechanisms to achieve load balancing and enhance network performance. Through comprehensive MATLAB simulations, we evaluated NBEER against Co-UWSN and CEER, demonstrating its superior performance across various metrics. NBEER significantly maximizes end-to-end delay, reduces energy consumption, improves packet delivery ratio, extends network lifetime, and enhances total received packets analysis compared to the existing protocols.

It is critical for intelligent vehicles to be capable of monitoring the health and well-being of the drivers they transport on a continuous basis. This is especially true in the case of autonomous vehicles. To address the issue, an automatic system is developed for driver’s real emotion recognizer (DRER) using deep learning. The emotional values of drivers in indoor vehicles are symmetrically mapped to image design in order to investigate the characteristics of abstract expressions, expression design principles, and an experimental evaluation is conducted based on existing research on the design of driver facial expressions for intelligent products. By substituting a custom-created CNN features learning block with the base 11 layers CNN model in this paper for the development of an improved faster R-CNN face detector that detects the driver’s face at a high frame per second (FPS). Transfer learning is performed in the NasNet large CNN model in order to recognize the driver’s various emotions. Additionally, a custom driver emotion recognition image dataset is being developed as part of this research task. The proposed model, which is a combination of an improved faster R-CNN and transfer learning in NasNet-Large CNN architecture for DER based on facial images, enables greater accuracy than previously possible for DER based on facial images. The proposed model outperforms some recently updated state-of-the-art techniques in terms of accuracy. The proposed model achieved the following accuracy on various benchmark datasets: JAFFE 98.48%, CK+ 99.73%, FER-2013 99.95%, AffectNet 95.28%, and 99.15% on a custom-developed dataset.

Car crashes are among the top ten leading causes of death; they could mainly be attributed to distracted drivers. An advanced driver-assistance technique (ADAT) is a procedure that can notify the driver about a dangerous scenario, reduce traffic crashes, and improve road safety. The main contribution of this work involved utilizing the driver’s attention to build an efficient ADAT. To obtain this “attention value”, the gaze tracking method is proposed. The gaze direction of the driver is critical toward understanding/discerning fatal distractions, pertaining to when it is obligatory to notify the driver about the risks on the road. A real-time gaze tracking system is proposed in this paper for the development of an ADAT that obtains and communicates the gaze information of the driver. The developed ADAT system detects various head poses of the driver and estimates eye gaze directions, which play important roles in assisting the driver and avoiding any unwanted circumstances. The first (and more significant) task in this research work involved the development of a benchmark image dataset consisting of head poses and horizontal and vertical direction gazes of the driver’s eyes. To detect the driver’s face accurately and efficiently, the You Only Look Once (YOLO-V4) face detector was used by modifying it with the Inception-v3 CNN model for robust feature learning and improved face detection. Finally, transfer learning in the InceptionResNet-v2 CNN model was performed, where the CNN was used as a classification model for head pose detection and eye gaze angle estimation; a regression layer to the InceptionResNet-v2 CNN was added instead of SoftMax and the classification output layer. The proposed model detects and estimates head pose directions and eye directions with higher accuracy. The average accuracy achieved by the head pose detection system was 91%; the model achieved a RMSE of 2.68 for vertical and 3.61 for horizontal eye gaze estimations.

In medical environments, a wireless body sensor network (WBSN) is used to operate remotely, and sensor nodes are employed. It consists of sensor nodes installed on a human body to monitor a patient’s condition, such as heartbeat, temperature, and blood sugar level, and are functionalized and controlled by remote devices. A WBSN consists of nodes that are actually sensors in nature and are operated with a short range of communication. These sensor nodes are fixed with limited computation power and the main concern is energy consumption and path loss. In this paper, we propose a new protocol named energy-efficient distance- and link-aware body area (EEDLABA) with a clustering mechanism and compare it with the current link-aware and energy-efficient body area (LAEEBA) and distance-aware relaying energy-efficient (DARE) routing protocols in a WBSN. The proposed protocol is an extended type of LAEEBA and DARE in which the positive features have been deployed. The clustering mechanism has been presented and deployed in EEDLABA for better performance. To solve these issues in LAEEBA and DARE, the EEDLABA protocol has been proposed to overcome these. Path loss and energy consumption are the major concerns in this network. For that purpose, the path loss and distance models are proposed in which the cluster head (CH) node, coordinator (C) node, and other nodes, for a total of nine nodes, are deployed on a human body. The results have been derived from MATLAB simulations in which the performance of the suggested EEDLABA has been observed in assessment with the LAEEBA and DARE. From the results, it has been concluded that the proposed protocol can perform well in the considered situations for WBSNs.

This paper proposes a Cooperative-Relay Neighboring-Based Energy-Efficient Routing (CR-NBEER) protocol with advanced relay optimization for MUSN. The utilization of the relay nodes, among all other sensor nodes, makes it possible to achieve node-to-node deployment. The proposed method focuses only on cooperation and relay optimization schemes. Both schemes have previously been implemented, and thus the proposed method represents the extended version of the Neighboring-Based Energy-Efficient Routing (NBEER) protocol. Path loss, end-to-end delay, packet delivery ratio, and energy consumption parameters were considered as part of the performance evaluation. The average performance was revealed based on simulations, where the overall average EED of Co-UWSN was measured to be 35.5 ms, CEER was measured to be 26.7 ms, NBEER was measured to be 27.6 ms, and CR-NBEER was measured to be 19.3 ms. Similarly, the overall EC of Co-UWSN was measured to be 10.759 j, CEER was measured to be 8.694 j, NBEER was measured to be 8.309 j, and CR-NBEER was measured to be 7.644 j. The overall average PDR of Co-UWSN was calculated to be 79.227%, CEER was calculated to be 66.73.464%, NBEER was calculated to be 85.82%, and CR-NBEER was calculated to be 94.831%. The overall average PL of Co-UWSN was calculated at 137.5 dB, CEER was calculated at 230 dB, NBEER was calculated at 173.8 dB, and CR-NBEER was calculated at 79.9 dB. Based on the simulations and evaluations, it was observed that the cooperation and relay optimization scheme outperformed previous schemes.