###### Abstract  
Aiming at the problem of being unable to meet some high computing power, high-precision applications due to the limited capacity of underwater sensor nodes, and the difficulty of low computation power, in this paper, we introduce the edge servers, known as base stations for underwater sensor nodes, and propose a scheme to process the computational tasks based on coalition game theory. This scheme provides functions such as cooperation among different base stations within the coalition, the smart division of tasks, and efficient computational offloading. In order to reduce the complexity of the algorithm, the artificial neural network model is introduced into the method. Each task is divided into sub-parts and fed to an artificial neural network for training, testing, and validation. In addition, the scheme delivers the computed task from base stations back to sink nodes via a shortened path to enhance the service reliability. Due to the mobility of the base station in the ocean, our proposed scheme takes into account the dynamic environment at the same time. The simulation results show that, compared with the existing state-of-the-art methods, the success rate of our proposed approach improves by 30% compared with the Greedy method. The total service time of our proposed approach decreases by 12.6% compared with the Greedy method and 31.2% compared with the Always-Migrate method. underwater sensors network; computational offloading; smart task division; coalition game theory; artificial neural network  
25 August  
cooperative computation between BSs, in which tasks are divided into multiple components and transmitted between BSs [7; 8]. This method can ensure a low processing time and high reliability. Therefore, the main difficulty in this scheme is how to select a proper task offloading and parallel computing algorithm. The problem becomes more severe when the communication ranges of BSs are overlapping. The service time will be further decreased by exploiting the methods of task partition and cooperative computing. The task division and cooperation among BSs are the techniques in which the task is divided into multiple components, and multiple BSs process the tasks in parallel. Furthermore, there is also a service risk session due to the movement of BSs, the limited communication range [9]. Due to the movement of nodes, the nodes which are communicating with each other can be out of their communication range, which causes an ongoing session to be interrupted or a service failure [10]. For this purpose, we propose a scheme to process the computational tasks based on coalition game theory. In the coalition game, to avoid the service risk, the coalition is formed by the nodes of USNs, SNs, and BSs in the communication range. The coalition game is used to cooperate between the BSs for task divisions [11]. In this paper, we propose the cooperation policy based on the coalition game theory to provide better service reliability by dividing and allocating in parallel the tasks between BSs. Consequently, the service reliability can be improved through cooperation between BSs. After the coalition is created and the task is ready to transmit from SN to BSs, the next issue is to reduce the communication cost [12; 13]. Hence, an efficient transmission mechanism of the computational task is proposed to minimize the communication time and improve the service reliability. In this scheme, each base station first finds the nearest neighbor base station within its transmission range. In the next phase, the BSs process the computational task sent by the SN. In addition, each BS chooses whether it partially or entirely executes computing tasks based on whether it establishes an alliance with other BSs. If the alliance has been established, the BS only processes part of the computing tasks and transmits the results of the computing process to another BS cooperating with it [14; 15]. Furthermore, artificial neural networks (ANNs) can be utilized to divide tasks intelligently.