## 1 Introduction  
Nearly 71% of the Earth's surface is covered with water. As a result, the deep ocean is a vast and mostly unexplored habitat on our planet. Recently, however, there has been a growing interest in exploring and monitoring ocean environments either for scientific exploration, for commercial exploitation, or for navy military surveillance. Underwater Wireless Sensor Networks (UWSNs), as ideal systems for this type of extensive monitoring and exploration missions, have become a new hot research area [1; 2]. UWSNs, employed in deep ocean exploration and underwater monitoring, are composed by three types of nodes: the master nodes, sensor nodes and mobile nodes, which together form a three dimensional (3D) network, popularly used in resource exploration, underwater monitoring and communication, etc. [3; 4]. Some requirements need to be met in order to fulfill functions such as ocean exploration and underwater monitoring. Firstly, since the sensor nodes are distributed in a wide area range, a wide range of monitoring is required. Secondly, to collect and process data effectively, UWSNs are required to have high data transmission capability, including short delay, higher bandwidth and good QoS. Finally, UWSNs require longer lifetime and strong dynamic adaptation ability. Sensor nodes are deployed in a dynamic ocean environment, therefore the systematic performances of an UWSN such as coverage, connectivity and lifetime are impacted by the influence of parameters, including detection radius, communication radius and energy consumption rate. Hence, in designing the UWSN scheme, the systematic performance for the mission needs to be analyzed and evaluated first [5]. Moreover, UWSNs need to have the adaptive ability to adjust the communication radius, detection radius of sensors or routing, and the topology of the network to achieve longer lifetime when the environment and mission change. Sensor nodes are deployed in a dynamic sea environment, and an UWSN is formed by networking to meet the needs of a specific mission. Therefore, these problems should be solved when the UWSN scheme is designed. On the one hand, an appropriate sensor node deployment strategy and networking mode are adopted to generate an UWSN which has superior system performance. On the other hand, through the evaluation of the system performance, the UWSN can optimally adjust sensor detection, communication radius and other parameters automatically to achieve longer lifetime or greater network coverage. Therefore, sensor node deployment, networking, and performance calculation of UWSN are challenging issues. This study is conducted based on the following assumptions:  
1. The node types of an UWSN include master node, sensor node and mobile node. The master node is a vessel used for data processing. Sensor nodes are different kinds of stationary and submerged floating sensors deployed underwater. Mobile nodes are underwater unmanned vehicles, such as unmanned underwater vehicles (UUVs) and autonomous underwater vehicles (AUVs). 2. In the beginning, sensor nodes are deployed randomly on the sea surface. When working, they can float on the ocean or be suspended in the ocean at certain depth. Sensor nodes are fixed in the water by anchoring. However, they can move in the vertical direction by changing their buoyancy, and slightly move back and forth horizontally with ocean current. 3. The detection range of sensor nodes is a uniform ball-body [6]. They can only detect the targets precisely within the range, but cannot detect the targets outside of it. Sensor nodes have three states: active state, startup state and sleep state. 4. The mobile nodes, mainly UUVs and AUVs carrying some sensors nodes, can redeploy and reconfigure their sensor nodes, and have the functions of resource exploration and environmental monitoring. Master nodes deploy sensor nodes and control the network. Based on the above assumptions, this paper focused on the problems. In our initial study, CDNM and SQPCM of UWSN are proposed. More importantly a NPAM is newly presented.