## 1 Introduction  
The Earth's surface, around 71.2% is covered by ocean. Approximately half of the population of this world lives under 100 km of seaside areas. Furthermore, the ocean hasbeen a source of nutrition products, and over time, has begun playing a vital role in the areas of defense, carriage, natural resources, and leisure. While water is indispensable for all humankind, a surprisingly massive area of the total volume of the ocean remains unexplored, i.e., less than 10% of it has been studied [1]. It is difficult for human beings to monitor volatile underwater events and high-water pressure. Because of the reasons mentioned earlier, unmanned exploration is necessary. As a result, Internet of Things-enabled acoustic sensor and actor networks (I-ASANs) have attracted the interest of researchers of late. In the last 10 years, much research has been conducted underwater for multiple purposes [2]. Due to potential underwater applications, this research is not limited to the exploration of finding minerals, oils, and monitoring of aquatic life but also is an experimental laboratory for acoustic experiments. Today, many real-time problems have been experimented on under water such that to predict floods and tsunami, scientific and commercial offshore exploration is conducted in deep and shallow water, etc. Many routing protocols work successfully in terrestrial wireless sensor networks (TWSNs) that cannot work steadily in underwater domains due to different communication channels like radio signals, which do not propagate underwater as this is only possible for acoustic signals. The topology of ASANs [3] is usually dynamic compared to TWSNs due to the deployment of frequent nodes and their localization. As these nodes move with mobile water [4], so energy is limited, and it is difficult to recharge the power resources in deep water. Therefore, high propagation delay and deployment cost, low bandwidth, and packet loss ratio are significant challenges in ASANs [5]. All the above challenges can be resolved if the data is smooth and sent rapidly towards the destination. Many well-known routing protocols are worked in this regard, like vector-based flooding [5] and angle-based flooding [6; 7] which calculate the angle with multi-sink architecture for reliable data delivery, but the efficient way does not achieve due to week support of intermediate nodes. The unavailability of the next intermediate nodes mean empty regions are created in the network, as well as data loss of previous nodes. In an upward direction, every sensor node computes the angle that is \(\pi/2\). On the basis of the angle, as mentioned earlier, the nodes forward the data packets. Data packets that reach any one of the surface sinks will be considered successfully delivered to the destination. The empty region is defined as the area in which nodes (usually present around the sink called sink neighboring nodes) fail to send the information towards the destination and create empty regions in this area. For these regions, the network traffic is stopped for the time being and isolate some part of the network. Ultimately data packet delivery and throughput is decreased and an extra energy tax is used for rescue the network especially in a large-scale area network like under water. Many secondary and rescue nodes are introduced inside the sink to recover the data [7; 8]. In [8], the watchman nodes are introduced to the recovery of lost data from the empty region of the underwater network in which the watchman was continuously monitoring the node's location and its energy status. However, these two approaches follow the monitoring approach, where the status of every node is checked by watchman nodes and corresponding angles of forwarding. The related parameters of the watchman are at distance from the node and angle cone for angle-based flooding. Both these parameters have a significant reason for high propagation and end-to-end delay, respectively.