Underwater Acoustic Sensor Networks (UWSNs) are used to facilitate a variety of applications, such as ocean observation and offshore exploration. However, they are typically characterized by a number of inherent issues, i.e., high delay in transmission, high energy consumption, complicated deployment process, long propagation delay, and high signal attenuation. Moreover, the occurrence of void areas along the network routing path has the potential to severely impair overall performance. To solve these problems, particularly the routing void one, it is important to select a best forwarder node. The suggested solution starts by utilizing the Silhouette Score-based Taylor Fuzzy C-Means (S2-TFCM) clustering algorithm to efficiently cluster sensor nodes. Then, Cluster Head (CH) selection is done based on a new Binary Fire Hawks Optimization (BFHO) algorithm, based on significant parameters like residual energy, node degree, priority factor, and sink node distance. Lastly, an Evolutionary Game Theory-based model is utilized to determine the most appropriate forwarder node, taking into consideration factors like depth difference. Simulation outcomes reveal that the given method improves energy efficiency by 18.7%, improves throughput by 14.2%, and extends network lifetime by 21.5% compared to current schemes such as EGRC and BEEC, thereby proving effective for energy-efficient and reliable routing in UWSNs.

Summary Wireless sensor and actor networks (WSANs) consist of nodes associated in an ad hoc manner to perform sensing tasks for information gathering and acting functions on the basis of gathered information. Connectivity is an essential requirement of large‐scale wireless networks, and WSANs are supposed to stay connected. The nodes in hostile environments are prone to failures such as battery depletion, physical damage, or hardware malfunction. The failure of some nodes, like cut vertex nodes, can partition the network into multiple network segments. Most of the solutions for network partition recovery proposed in the literature depend on the assumption that the network is obstacle‐free. However, an obstacle‐free environment is not possible in real‐life situations. In the last few decades, UAVs or drones have been engaged in various applications such as industrial inspections, remote sensing, agriculture, military, disaster relief, and so forth, UAVs can be employed to strengthen the connections in wireless networks by coordinating with ground nodes since they can render services in rough areas where ground nodes cannot provide services. Thus, our research is based on using UAVs as relay nodes to reconnect the disjoint partitions. This paper proposes two algorithms: Drone assisted partition recovery algorithm (DAPRA) and drone assisted detection and partition recovery algorithm (DADPRA). In both algorithms, partitions are detected by the sink node. In DAPRA sink node determines the failed cut‐vertex node and sends UAV to the location of the failed cut‐vertex node. In DADPRA algorithm, UAV identifies the failed cut‐vertex node and reconnects the disjoint network segments. DAPRA and DADPRA are analyzed according to the state‐of‐the‐art parameters, that is, recovery and detection time, UAV's travel distance, and the total messages transmitted. The proposed algorithms are compared with similar Distributed Partition Detection and Recovery using UAV (DPDRU) approach. The simulation results show the proposed algorithms detect network partitioning in less time as compared to DPDRU approach.

Summary Wireless information routing beneath the ocean, or usually underwater, has supplied the most excellent technical methods of oceanic observations. Traditionally, ocean bottoms have been monitored by placing oceanographic sensors that collect data at specific and defined ocean zones. When the duties are performed, the oceanographic instruments are recovered. Because there is no collaborative exchange of collected data between the collecting point and the monitoring end, data cannot be observed remotely. Underwater sensor networks can be wirelessly connected with sensors and monitors without extensive cable. Underwater wireless sensor networks are what they are called (UWSNs). This article investigates the various aspects of UWSNs, such as their significance, applications, network design, demands, routing, deployments and challenges.

Summary Internet of recent decades considered cloud computing as the most effective and distributed platform. It is a comfortable and quick way to access shared resources over the Internet anytime. The major problem cloud customers face while choosing the resources for a particular application is QoS. In the cloud computing environment, various resources need to be effectively allocated on VMs by reducing makespan and synchronously increasing resource utilization. For that, a novel multi‐objective hybrid capuchin search with genetic algorithm (MHCSGA) based hierarchical resource allocation is established in this work. MHCSGA optimizes the multi‐objective functions like resource utilization, response time, makespan, execution time and throughput. Initially, partitioning around the K‐medoids clustering method is utilized to allocate the resources optimally. During clustering, the tasks are divided into two cluster groups then, the optimization is performed to attain an optimal resource allocation process. The experimental setup is executed using the JAVA tool. For the simulation process, the proposed work uses the GWA‐T‐12 Bitbrains dataset. The makespan achieved by proposed algorithm for 50, 100, 150, and 200 tasks are found to be 10.45, 17.6, 25.67, and 31.34, respectively. The comparison analysis proves that the developed model attains improved performance than the state‐of‐the‐art works.

A communication void is one of the most serious issues in any routing techniques. This issue occurs when a node does not have any forwarder node to forward the data packets towards the sink node or surface station. The presence of a void node affects the overall performance of routing techniques in terms of end‐to‐end delay, data loss, energy consumption, and so on. The primary objective of this work is to avoid the void region in underwater sensor networks (UWSNs). For the same purpose, this work introduces the void‐hole avoidance routing algorithm for UWSNs. This work avoids horizontal transmission, which further reduces the end‐to‐end delay. The residual energy, depth, holding time, and distance from the sending node to the forwarding node and forwarding node to the surface station are used as a matrix to select the best forwarder node. The simulation has been done on MATLAB to analyse the performance of the proposed algorithm. The proposed routing algorithm attained better performance using the metrics, namely packet delivery delay, number of dead nodes, and energy tax with the values of 0.9147, 48, and 72.14 by varying the transmission range, and 0.9184, 38, and 71.12 by varying the network size, respectively.

In today's scenario, underwater wireless sensor network (UWSN) has raised as a most famous research areas for the researchers due to its advancement in the real world. In UWSNs, the sensor nodes sense the environment, and then this sensed information is conveyed to one of the sink node and finally to the base station for processing. It is a known fact that nearly about 70% of the total earth surface is filled with the water. It is difficult for the humans to get the valuable information from the seabed. Major applications of UWSN are marine surveillance, sea monitoring, deep sea archaeology, oil monitoring etc. Major goal of this work is to provide the broad survey of underwater sensor networks with its application, deployment techniques and routing algorithms. This study also provides a comparative analysis of the deployment techniques and routing algorithms that will aid the researchers to find the research gaps in the deployment and routing techniques. Moreover, some of the UWSN applications also reviewed, which provides varied and valuable information about the UWSN approach. This study presents an overview about the conventional technologies of UWSN and its major focus is to present the significant research approaches towards the UWSNs’ applications, deployment techniques, and routing process.