# Node Depth Adjustment Based Target Tracking in UWSNs Using Improved Harmony Search
## 1 Introduction  
Over 70% of the earth's surface is covered by oceans and rivers. Underwater research has great potential due to a lot of undeveloped resources. In recent years, underwater wireless sensor networks (UWSNs) have gradually become an important technology for human beings to explore and utilize underwater resources. As an extension of terrestrial wireless sensor networks, UWSNs can be used for disaster warning, environmental monitoring, underwater target detection and tracking [1; 2; 3; 4; 5]. In this paper, we focus on the problem of underwater target tracking using UWSNs. UWSNs can provide a reliable solution to underwater target tracking. For moving underwater targets, UWSNs with high-density sensing ability are needed to collect measurements and transmit estimated data. Most research about target tracking in UWSNs is based on densely and statically deployed underwater nodes. However, due to the high cost of underwater sensor nodes and complex underwater environments, we have to sparsely deploy nodes in practical 3D underwater areas [6; 7]. In addition, the deployment work of nodes is always completed before the network is put into service, and nodes will not change their position after being put into water. Such a network can achieve an accurate tracking performance when the target moves close enough to it, but if a target is detectedin an area with a low coverage rate, the network will fail to track it accurately, and it lacks the adaptive adjustment ability to dynamic events. Therefore, we need to improve the flexible sensing and communication ability of UWSNs. As we know, underwater nodes are floating underwater with the help of buoys and mooring lines. In this paper, we consider that the nodes are equipped with a node depth adjustment system. [PERSON] et al. [8] presented a depth adjustment system that connects to underwater nodes. [PERSON] et al. proposed a depth adjustment scheme to maximize the coverage in 3D space [9]. The depth adjustment for underwater nodes is always applied to issues of network deployment and routing protocol. It can improve the network coverage rate and data packet delivery ratio, thus increasing network reliability [10; 11]. Inspired by these research results, this paper proposes a novel scheme to improve target tracking accuracy. It is assumed that nodes are located at their original position when they are not working. Once some nodes are woken up by fusion center, they adjust their depth according to commands from the fusion center and sense the target at the optimal depth, in order to improve tracking performance. Thus, the key problem is how to determine optimal depth of nodes during the tracking task, which can be converted to a dynamic optimization problem under the constraint of moving range. In our previous work, we provided some solutions to select the optimal node cluster for target tracking [12; 13]. Fisher Information Matrix (FIM) and its inverse matrix posterior Cramer Rao Low Bound (PCRLB) can reflect estimation accuracy, and we employed them as criteria to select the optimal node cluster. Hence, we will take FIM as an objective function in this paper and compute the optimal nodes' depth in the framework of optimization problem. As an optimization problem, finding the global optimal analytical solution becomes extremely difficult. In theory, the optimal depth can be determined if we discretize the constraint moving range and perform an exhaustive search. However, this method is not practical because of the heavy computational burden. For this reason, an improved Harmony Search algorithm (HS) is proposed to solve this optimization problem. HS is a meta-heuristic optimization algorithm that mimics the improvisation process of music players. It is a population-based search algorithm that can successfully solve optimization problems [14; 15; 16]. In this paper, each harmony represents nodes' moving distance, which is related to final position of nodes. For traditional HS, a harmony memory matrix is randomly initialized within moving range.