# A Dynamic Surface Gateway Placement Scheme for Mobile Underwater Networks +
## 2 Related Work  
The sensing coverage problem has received significant attention in the context of wireless sensor networks [17; 18; 19; 20; 21]. Several algorithms [22; 23; 24] have been proposed to achieve full sensing coverage, while others [25; 26] seek to maximize the lifetime of a sensor network by keeping only a subset of the sensor nodes active at any given time. The deployment problem for UWSN is considered to be a grand challenge because one must overcome additional uncertainties posed by aquatic environments. A surface gateway deployment framework to reduce end-to-end delay and energy consumption was proposed in [15]. The authors formulate the deployment problem as an integer linear programming (ILP) problem, where candidate gateway positions are known. However, they also assume prior knowledge of the channel status, MAC protocols, routing protocols, and mobility patterns, limiting its applicability to networks experiencing more realistic conditions. In [27] the authors analyzes the impact of the sensing and transmission ranges on coverage, connectivity, and network diameter. They also provide conditions on the node transmission range needed to complete the desired degree of connectivity as well as the sensing range required to achieve a desired degree of coverage in a region containing a predetermined number of nodes. In [28] the authors investigate redeployment needs when sensor nodes passively drift away from an area where sensing coverage is to be maintained. To characterize the continuing drift of sensor nodes, a random walk model is assumed. The drift model is complemented by a corrective redeployment strategy to replace lost sensors. The primary goal of [29] was to improve the collective coverage of the network by repositioning underwater sensors. Instead of merely minimizing the total cost of moving all sensors during the repositioning process, this approach also seeks to minimize the maximum cost of repositioning a set of sensors assigned to a single AUV, ensuring that AUV deplete their fuel supplies more uniformly [30]. Both [28; 29] are focused on enhancing coverage by repositioning the underwater sensor nodes which is practically difficult to achieve. Adjust the depth of sensors after random deployment at the bottom of the ocean to achieve 1-coverage, which is defined as a deployment where each node is covered by at least one gateway. A central station directs the movements of each sensor after initial deployment. Thus, the approach is not fully distributed, requiring connectivity which was not explicitly addressed. Furthermore, detailed analysis of coverage performance based on their random approach was not provided. A Game Theory Field Design (GTFD) model of the deployment problem was proposed in [31]. This model is a two-player zero-sum matrix game between a network designer and an adversary vehicle, where the optimal strategy of the adversary is formulated as a linear programming problem. Solving this problem enables effective sensor node deployment. In [32] the authors proposes a distributed algorithm to improve coverage. In this approach, nodes are deployed randomly at the surface and are connected to buoys with wires. Nodes may then adjust their depths to improve coverage. However, it is unrealistically assumed that any two nodes can communicate through the buoys at the surface regardless of their transmission range. To improve coverage and provide connectivity with the surface stations, the authors [33] presented a distributed node deployment technique for UWSNs after random deployment on the seafloor. However, nodes are only capable of adjusting their depth. Depth adjustments are calculated based on a 2-dimensional projection of node coverage overlap, where graph coloring is applied to reduce the coverage problem from the 3-dimensional case. In [34] a multiple gateway deployment is proposed based on cuckoo optimization algorithm which performs periodic deployment to cope with nodes drifting.