# Node Depth Adjustment Based Target Tracking in UWSNs Using Improved Harmony Search
## 2 Related Work  
As for the development of UWSNs, the research of target tracking in UWSNs has gradually become an important issue. [PERSON] et al. [17] presented a two cluster-based distributed particle filter tracking algorithms. The first algorithm focused on tracking accuracy, while the second considered the balance between consumption and tracking accuracy. [PERSON] et al. combined a particle filter with an interacting multiple model to solve maneuvering target tracking in UWSNs [18]. [PERSON] et al. proposed and scrutinized two three-dimensional cluster-based UWSN architectures for identifying and tracking submerged moving intruders in [19]. The communication links from the intruder sensing nodes to the base station were based on radio frequency. As we know, energy consumption is also a key problem for target tracking UWSNs. To save energy consumption in UWSNs, [PERSON] et al. [20] proposed a wake-up/sleep scheme based underwater node selection algorithm to track targets efficiently. In [21], [PERSON] et al. proposed a tracking protocol considering energy consumptions in both the target and sensor nodes. The protocol was designed in two aspects. The first was the passive listening mechanism and duty-cycle strategy for targets and the second was detection-based ranging packet transmission for sensor nodes. To save on communication costs during the tracking process, we proposed an artificial measurement based energy-efficient filter that implements the trade-off between communication costs and tracking accuracy [22]. The sensor nodes would not send weak measurement to the fusion center. Instead, an artificial measurement would be generated in the fusion center to guarantee tracking accuracy. As shown in our previous work, we found that the geometry between nodes and target had a big effect on the accuracy of target tracking in UWSNs. Since a nonlinear measurement model was always utilized, the locations where distance measurements were collected had a profound effect on the tracking accuracy. When sensing nodes were close to each other, the measurements provided by them were similar, and we could not get enough useful information. However, when nodes were in different directions of targets, they could provide different information and improve tracking accuracy. Based on the effect, we designed several node selection algorithms to wake the best node combination for tracking tasks [12; 13; 23; 24]. We quantized the effect of geometry based on a posterior Cramer-Rao lower bound (PCRLB) and minimized PCRLB to get the best nodes. In the aforementioned results, the sensor nodes were considered static underwater. However, with static underwater nodes, a lot of nodes should be deployed to guarantee tracking accuracy. It was expensive and impractical to achieve such a network. In [25], [PERSON] et al. utilized sea surface deployed UWSNs to position underwater targets. An optimal node placement for underwater target positioning was proposed using the Fisher Information Matrix. However, the nodes were static after placement, which was not practical for tracking a moving target. In [8], an autonomous depth adjust system was designed and applied to UWSNs by [PERSON] et al. A winch based module was added to underwater nodes to enable depth adjustment and improve global sensing and communication ability. A lot of experiments showed great promise of the depth adjustment system. Based on node depth adjustment, [PERSON] et al. proposed a routing protocol for UWSNs to improve the data packet delivery ratio in UWSNs [10]. It switched topology of the network through depth adjustment of the void nodes. In addition, [PERSON] et al. proposed a depth adjustment based deployment algorithm in UWSNs using a two-dimensional convex Hull and spanning tree, improving network connectivity and coverage [11]. The depth adjustment strategy based on time markers was used to achieve the three-dimensional overall network deployment. However, node depth adjustment for target tracking has not been found in literature.