## 1 Introduction  
The Internet of Underwater things (IoUTs) involves a group of sensor nodes that gather data from different sensors placed in the ocean's depths to further transfer them towards the positioned devices at the external level through a network of midway nodes. The IoUTs consists of sensors and relay nodes that use acoustic mediums for communication. It can support various applications such as underwater tracking, climate exposure and recording, oceanographic information, pollution detection, underwater surveillance, navy defense systems, offshore exploration, and disaster prevention. As one-third of our population is surrounded by water and millions of people live within 100 km of the coastal area [1], the IoUTs will play a vital role. State-of-the-art literature discusses the problems and limitations that interrupt the IoUTs' effectiveness, including bandwidth limitation, high energy consumption, inadequate energy resources, efficient routing, and increased distribution cost [2]. Dynamic topology and water current are fundamental parameters for controlling the mobility factor inside the underwater field. The deployment of nodes before and after the experiment are quite different. Therefore, it is difficult to predict which node is inactive/alive/dead in shallow and deep water. Basically, the dead nodes are the gaps of nodes when the communication is near to the destination and is overburdened. When these nodes are found frequently in network size, the void nodes become void regions that isolate some part of the network. The main reason for this event is the absence of the candidate nodes that take responsibility for those nodes that are dying or are going to die. In addition, compared to Terrestrial Wireless Sensor Networks (TWSNs), identifying the localization of nodes is also difficult as radio signals do not work in underwater scenarios. For this reason, one of the best substitutes is the use of acoustics links. Moreover, energy in batteries or other means is limited and difficult to replace or re-charge in underwater scenarios. In the literature, many routing schemes and algorithms have been reported to minimize the energy consumption of the nodes and identify dead nodes [3; 4]. The dead nodes are those nodes that are not able to deliver the data to sonobuoys. For quick data delivery, many routing and flooding schemes use the shortest path and routing tables, angle-based flooding, the three-hop reliability model, watchman-based flooding, depth and location-based routing, pressure, and cluster/subnet-based approaches [5; 6; 7; 8]. As nodes around the sonobuoys suffer, and the entire traffic load can die early, we propose the internodes adjustment mechanism for providing an alternative route by following the greedy forwarding approach to recover the data from the dead nodes. At the same time, Path Adjustment (PA) is performed by following the rotational orbit-based shape for alternative ways, collectively called Rotational Orbit-Based Inter-Node Adjustment (ROBINA). Moreover, its two sub-variants: Path-Adjustment (PA-ROBINA) and Path Loss (PL-ROBINA), were proposed in this article. The ROBINA uses geographic energy-efficient data communication while PA-ROBINA provides proper path adjustment to bypass the dead nodes to discern the Immediate Available Forwarder Node (I\({}_{\text{AFN}}\)) after dead nodes. In addition, the PL-ROBINA uses the path loss transmission model to divert the path according to desired (I\({}_{\text{AFN}}\)). Moreover, the mode of operation is based upon the inter node adjustment mecachnism. The relationship between rotation orbit and path selection is basically interlinked with each other. It is because of the rotation orbit-based pathway is used to find new node in the communication range. The path selection will automatically adjusted with the position of new finding node. The mathematical-based formulas were introduced to create a path with a maximum rotation covering the main proposed approach, ROBINA.