Underwater mobile sensor networks (UMSNs) with free-floating sensors are more suitable for understanding the immense underwater environment. Target tracking, whose performance depends on sensor localization accuracy, is one of the broad applications of UMSNs. However, in UMSNs, sensors move with environmental forces, so their positions change continuously, which poses a challenge on the accuracy of sensor localization and target tracking. We propose a high-accuracy localization with mobility prediction (HLMP) algorithm to acquire relatively accurate sensor location estimates. The HLMP algorithm exploits sensor mobility characteristics and the multi-step Levinson-Durbin algorithm to predict future positions. Furthermore, we present a simultaneous localization and target tracking (SLAT) algorithm to update sensor locations based on measurements during the process of target tracking. Simulation results demonstrate that the HLMP algorithm can improve localization accuracy significantly with low energy consumption and that the SLAT algorithm can further decrease the sensor localization error. In addition, results prove that a better localization accuracy will synchronously improve the target tracking performance.

Target tracking is one of the broad applications of underwater wireless sensor networks (UWSNs). However, as a result of the temporal and spatial variability of acoustic channels, underwater acoustic communications suffer from an extremely limited bandwidth. In order to reduce network congestion, it is important to shorten the length of the data transmitted from local sensors to the fusion center by quantization. Although quantization can reduce bandwidth cost, it also brings about bad tracking performance as a result of information loss after quantization. To solve this problem, this paper proposes an optimal quantization-based target tracking scheme. It improves the tracking performance of low-bit quantized measurements by minimizing the additional covariance caused by quantization. The simulation demonstrates that our scheme performs much better than the conventional uniform quantization-based target tracking scheme and the increment of the data length affects our scheme only a little. Its tracking performance improves by only 4.4% from 2- to 3-bit, which means our scheme weakly depends on the number of data bits. Moreover, our scheme also weakly depends on the number of participate sensors, and it can work well in sparse sensor networks. In a 6 × 6 × 6 sensor network, compared with 4 × 4 × 4 sensor networks, the number of participant sensors increases by 334.92%, while the tracking accuracy using 1-bit quantized measurements improves by only 50.77%. Overall, our optimal quantization-based target tracking scheme can achieve the pursuit of data-efficiency, which fits the requirements of low-bandwidth UWSNs.

We study the problem of energy-efficient target tracking in underwater wireless sensor networks (UWSNs). Since sensors of UWSNs are battery-powered, it is impracticable to replace the batteries when exhausted. This means that the battery life affects the lifetime of the whole network. In order to extend the network lifetime, it is worth reducing the energy consumption on the premise of sufficient tracking accuracy. This paper proposes an energy-efficient filter that implements the tradeoff between communication cost and tracking accuracy. Under the distributed fusion framework, local sensors should not send their weak information to the fusion center if their measurement residuals are smaller than the pre-given threshold. In order to guarantee the target tracking accuracy, artificial measurements are generated to compensate for those unsent real measurements. Then, an adaptive scheme is derived to take full advantages of the artificial measurements-based filter in terms of energy-efficiency. Furthermore, a computationally efficient optimal sensor selection scheme is proposed to improve tracking accuracy on the premise of employing the same number of sensors. Simulation demonstrates that our scheme has superior advantages in the tradeoff between communication cost and tracking accuracy. It saves much energy while loosing little tracking accuracy or improves tracking performance with less additional energy cost.

Target tracking is one of the broad applications of underwater wireless sensor networks (UWSNs). Sensors of UWSNs are battery-powered and it is impracticable to replace the batteries when exhausted. That means the batteries life affecting the lifetime of whole networks. So, it is worth reducing the energy consumption on the premise of satisfactory tracking accuracy. This paper proposes an adaptive sensor scheduling scheme that implements for accurately and energy-efficiently tracking a maneuvering target detected by UWSNs. This scheme employs multi-sensor to achieve the tracking task. A priori criterion is presented to select the best sensor group and best fusion sensor from candidate sensors. The criterion is generated from the algorithm combining interacting multiple model with extended Kalman filters (IMM-EKF). For reducing the energy consumption, the sampling interval is variable according to whether the tracking accuracy is satisfactory or not at each time step. Simulation demonstrates that selecting best sensor group can improve the tracking accuracy significantly, and selecting best fusion sensor and appropriate sampling interval can reduce the energy consumption significantly.

Since underwater nodes provide measurements for target tracking based on underwater wireless sensor networks (UWSNs), node topology, which is made up of the underwater nodes, may affect the performance of target tracking. But all the existing target tracking schemes do not consider this effect. This paper studies the effect of node topology on the target tracking in UWSNs. Firstly, by using the knowledge of geometry, the effects of four typical topologies on target tracking based on UWSNs are analyzed qualitatively. The four typical topologies include four nodes form a square, four nodes are in line, four nodes are close to each other, and four nodes form a regular tetrahedron. Secondly, to evaluate the arbitrary topology, the relationship between the posterior Cramer-Rao lower bound (PCRLB) and node's position is derived. Thirdly, our target tracking scheme consisting of the optimal topology selection scheme by minimizing PCRLB, the optimal fusion center selection scheme by minimizing energy consumption, and the multi-sensor particle filter (PF) is designed. Last, simulation results show the effectiveness of the proposed scheme.