## 1 Introduction  
Underwater Acoustic Networks (UANs) are gaining popularity because of its attractive updated monitoring applications like acoustic chemical waste monitoring, target tracking and detection for military applications, assisted navigation purposes, and monitoring the health of rare marine creatures [1; 2; 3; 4; 5]. The oceanic field is very large, deep and in different shapes like square, cylindrical, and rectangular. The sensor nodes used in the underwater environment are equipped with both acoustic and radio modems. The radio or light signals are used in terrestrial communication, whereas the acoustic signals are preferred in the underwater environment for data communication because of its long transmission range. The acoustic signals are less affected by scattering, signal attenuation, and absorption loss. While on the other hand, cause more delay in data communication as these signals are moving at the speed of 1500 m/s in the underwater environment. Moreover, an acoustic channel always faces problems like limited bandwidth, low transmission speed, and higher energy consumption [6; 7; 8]. Different models are discussed in [7; 9; 10; 11; 12; 13; 14] to reduce the energy consumption of Field Nodes (FNs) in the sensor network. In these schemes, the cluster-based architecture [3; 4; 15; 16; 17; 18] is proved to be more energy efficient. In UANs, a large number of battery-driven and application-specific wireless sensor nodes are deployed in the sensing field [6]. Some of the FNs are deployed closer to the Surface Sink (SS) while others are deployed away from the SS for complete coverage of the sensing field [19]. These FNs sense the information from underwater environments. Then, this sensed information is conveyed towards the SS. A FN, which is actively and constantly participating in the data relaying of other FNs will drain its battery earlier as compared to the fellow FNs [19; 20; 21; 22; 23]. The FNs deplete their batteries earlier can affect the network lifetime. Whereas, cooperative communication does not affect the SS as it is a powerful and more capable node than other FNs in term of storage, bandwidth, and battery lifetime. Currently, Mobile Sink (MS) and Autonomous Underwater Vehicle (AUV) based schemes are designed [9; 24; 25; 26; 27] for data aggregation. In these schemes, the AUV moves in a fixed path to collect data and stops at different places to collect the data. Some other approaches [10; 11; 24; 26; 28] are designed to alleviate the energy consumption UANs. However, for large underwater sensor networks, these defined approaches do not perform persuasively due to the long AUV path as it may add latency in data collection. Furthermore, due to the limitation on the battery capacity of AUVs the relative long trajectories could not be completed. The latency problem is handled in [29; 21; 30] by increasing the number of AUVs. Where each AUV moves in a different path and assists with other AUVs for complete network coverage. on the other hand, this may increase the operation cost and the mobility of AUVs further increase the water current movements that severely affects the communication between neighboring AUVs and the FNs. To overcome the issues of recent schemes [4; 9; 15; 24; 27; 29], we introduce a novel region-based scheme to collect data in the harsh underwater environment through the MS, which does not involve long transmission delay due to the very long trajectory for a complete network tour. We divide the Network Sensing Field (NSF) into regions for complete coverage based on the geographical nature of the water. In each region, a Region Head (RH) is assigned to control the communication between the Cluster Head (CH) and the MS. The data from RHs is collected through the MS which can free to move inside the sensing area, but for simplicity, we make Data Collection Points (DCPs) near the RH. The MS moves from top to the bottom and stops at DCP to collect information on each tour. The contributions of our designed framework are given as:  
1.