# Node Deployment of Marine Monitoring Networks: A Multiobjective Optimization Scheme
## 1 Introduction  
With the deepening of humans' understanding of the ocean, as well as the rapid development of science and technology, great attention has been paid to the ocean because of its huge economic potential and strategic importance. The increasing demand for the exploitation and utilization of marine resources calls for the wide deployment of marine monitoring networks. For example, a large number of drilling platforms have been built at sea to extract oil from the sea [1]. However, the exploitation of offshore oil resources also brings pollution risk to the marine environment. In 2010, the Gulf of Mexico oil spill accident led to serious harm to the marine ecosystem [2]. In such case, an underwater monitoring network is helpful to detect the oil spill and report the detection results in a timely manner. Another application is real-time monitoring for marine ranching, which isheavily dependent on the quality of marine environment to foster the marine fishery resources [3]. Thus motivated, a real-time Marine Monitoring Network (MMN) has become an important research topic for both academia and industry. Node deployment is one of the fundamental tasks for MMN, and also is an attractive research topic. During the past decade, many technologies and systems related to marine monitoring have been developed, such as a buoy for marine monitoring [4; 5], a prediction model of battery life [6], and a data acquisition and transmission system [7], which build a foundation for the implementation of the real-time MMN. According to the requirements of marine monitoring, various types sensors are deployed to monitor and measure different physical and chemical parameters such as water temperature, pressure, water direction and speed, salinity, turbidity, pH, oxygen density, and chlorophyll levels [4], and then the acquired data are transmitted back to the data center on land by relay nodes. The data acquired from the seabed far away from the coast needs to be relayed back to the data center through multi-layer relays. Compared with terrestrial monitoring networks, deployment of an MMN is more costly and complex due to the harsh marine environment in three-dimensional space. Most existing works of the deployment of marine monitoring sensor networks in the literature proposed different algorithms to improve the network coverage. In [8], a distributed node deployment algorithm was proposed to utilize the mobility of the anchor nodes to maximize the coverage of 3D underwater wireless sensor networks in dynamic ocean environments. In [9; 10; 11; 12], different algorithms were proposed to deploy sensors and surface gateways in a underwater sensor network. These works mainly focus on the deployment of underwater acoustic networks (UANs). To enable marine monitoring service, it is also critical to forward the data of the UAN towards the Base Station (BS) which is usually deployed in the shoreline of ocean. For efficient data communications over the large area of ocean, a multitier network that incorporates both underwater acoustic communications, radio communications above the water, and aerial relay over the air, is highly desirable. To the best of our knowledge, no existing work on the deployment of marine monitoring network study the deployment of an integrated multitier hierarchical network architecture, which includes underwater acoustic subnetwork, sea-surface wireless subnetwork, and air wireless subnetwork. In this paper, we first propose a three-tier hierarchical network architecture of MMN with support of edge computing (HMMN-EC), as shown in Figure 1, for an integrated sea-air-ground monitoring system.