The Underwater Acoustic Sensor Networks has drawn the attention of researchers around the globe because of its diversified applications. The network is based on battery-operated acoustic sensor nodes therefore, it is of utmost significance to design energy efficient routing protocol. One of the major reasons for inefficient energy consumption is the imbalance utilization of energy in different areas of the underwater region which leads to hotspot issues. In this article, a novel localization-free Energy Efficient Clustering Routing Protocol based on Arithmetic Progression (EECRAP) is proposed. The underwater region logically segmented into asymmetric layers based on Arithmetic Progression. The proposed solution is based on the residual energy, layer division, and depth division of the node which can mitigate the hotspot issue and balance energy utilization in the network. For energy preservation and network longevity, an innovative energy-efficient routing path selection is presented in which the succeeding forwarder node is selected according to its residual energy, layer division, and the received signal strength. When compared with Low-Energy Adaptive Clustering Hierarchy (LEACH), Depth Based Routing (DBR), Energy Efficient Routing Protocol Based on Layers and unequal Clusters (EERBLC), and energy efficient clustering multi-hop routing protocol (EECMR), the simulated results illustrate improvements in energy usage of 40.7%, 29%, 20.25%, and 11% over LEACH, EERBLC, DBR, and EECMR respectively. Packet Delivery Ratio-wise, EECRAP is 58.44%, 55.77%, 49.87%, and 7.61% dependable than DBR, LEACH, EERBLC, and EECMR respectively. Dead nodes make EECRAP 68.7%, 23.1%, 8.7%, and 7.6% more efficient than LEACH, DBR, EERBLC, and EECMR respectively.

Private automobiles are still a widely prevalent mode of transportation. Subsequently, traffic congestion on the roads has been more frequent and severe with the continuous rise in the numbers of cars on the road. The estimation of traffic flow, or conversely, traffic congestion identification, is of critical importance in a wide variety of applications, including intelligent transportation systems (ITS). Recently, artificial intelligence (AI) has been in the limelight for sophisticated ITS solutions. However, AI-based schemes are typically heavily dependent on the quantity and quality of data. Typical traffic data have been found to be insufficient and less efficient in AI-based ITS solutions. Advanced data cleaning and preprocessing methods offer a solution for this problem. Such techniques enable quality improvement and augmenting additional information in the traffic congestion dataset. One such efficient technique is the generative adversarial network (GAN), which has attracted much interest from the research community. This research work reports on the generation of a traffic congestion dataset with enhancement through GAN-based augmentation. The GAN-enhanced traffic congestion dataset is then used for training artificial intelligence (AI)-based models. In this research work, a five-layered convolutional neural network (CNN) deep learning model is proposed for traffic congestion classification. The performance of the proposed model is compared with that of a number of other well-known pretrained models, including ResNet-50 and DenseNet-121. Promising results present the efficacy of the proposed scheme using GAN-based data augmentation in a five-layered convolutional neural network (CNN) model for traffic congestion classification. The proposed technique attains accuracy of 98.63% compared with the accuracies of ResNet-50 and DenseNet-121, 90.59% and 93.15%, respectively. The proposed technique can be used for urban traffic planning and maintenance managers and stakeholders for the efficient deployment of intelligent transportation system (ITS).

In this study, an effort to synchronize and localize the underwater sensors simultaneously in a multi-hop environment is considered. Underwater Sensor Networks (UWSNs) has limited range and connectivity due to propagation delay and limited bandwidth. By using a multi-hop environment, we can extend the range and enhancing the network connectivity for providing more accurate localization and synchronization technique. We have established a connection between sensors point to point directed links and then analytically construct the model for the synchronization as a function of range, delay, and timestamps. Secondly, we have formulated the unconstrained optimization problem for localization by using a gradient technique. The proposed research scheme first synchronizes and then localizes the unknown nodes with known depth by using the single anchor node in a multi-hop scenario. According to the literature survey, this joint effort has never been addressed before; both localization and time synchronization for multi-hop scenarios are addressed separately. The proposed algorithm will also calculate the propagation delay that will be generated during message propagation for both localization and time synchronization. The numerical results are compared with some well-known techniques in terms of the number of nodes, localization error, synchronization error, total processing time up to four hops. Experimental results show that the proposed model outperforms in terms of localization and synchronization accuracy, but since this work has never been done before therefore, we compare our results with the techniques that had separately address this problem having some constraints.

The Underwater Acoustic Sensor Networks have gained significant attention because of their wide range of applications in submerged environments. However, ensuring reliable and energy-efficient communication in the submerged environment is challenging due to their distinctive characteristics such as limited energy resources, dynamic topology, extended propagation delays, and node mobility. Additionally, the void hole problem in submerged environments arises due to randomized node deployment. To curtail these issues, this paper introduces a novel way of strategically deploying the nodes based on the underwater depth parameters, which can reduce the likelihood of void hole occurrence. An optimal number of clusters based on the fixed transmission range of cluster heads is used to cater to extensive energy usage. In the proposed routing protocol, the path selection is based on the residual energy, link quality, and proximity to a higher number of nodes. Extensive simulations have been conducted by varying network parameters to analyze the network performance in terms of energy expenditure, packet delivery ratio, network throughput, number of dead nodes, and end-to-end delays. Also, the proposed work provides a performance comparison with some state-of-the-art protocols and exhibits promising results.