Optimal Quantization Scheme for Data-Efficient Target Tracking via UWSNs Using Quantized Measurements

Abstract

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.

Wireless Sensor Placement Optimization for Bridge Health Monitoring: A Critical Review

“…In practical structural health monitoring (SHM) systems, there may be challenges in accurately distinguishing identified mode shapes from one another, thereby potentially compromising the precision of vibration analysis [81][82][83][84]. Hence, it is advisable to utilize measures of information effectiveness, such as the modal assurance criterion (MAC) [22,25,27,[85][86][87][88][89], modal strain energy (MSE) [21,23,24,90,91], singular value decomposition ratio (SVDR) [26,[92][93][94], least square method (LSM) [95][96][97], and Fisher information matrix (FIM) [98][99][100][101][102][103][104], to assess the linear independence of identified mode shapes. The various modal evaluation criterias are detailed in Table 5 and Figures 13-17 as shown.…”

“…[ [98][99][100][101][102][103][104] (LSM) of deviations. the fitting point to straight line on the coordinate system should be the smallest.…”

Distributed Fusion of Optimally Quantized Local Tracker Estimates for Underwater Wireless Sensor Networks

“…Moreover, the approximate minimum means square error (MMSE) approach for state estimation with the quantized measurements is presented in [10]. In a recent communication [11], a method for tracking the targets is proposed, which uses the readily updated optimum quantization thresholds based on real-time target states. The authors in [12] presented a quantizer with adaptive threshold levels.…”

“…Quantization is performed on received measurements in several application domains (data communications, wireless communications, and digital filtering etc.) to accommodate limited bandwidth requirements [6]- [11].…”

Underwater Acoustic Target Tracking: A Review

“…However, the quantized threshold is fixed. Considering most quantization methods in WSNs need complex computation, a simplified objective function for determining the optimal quantization threshold offline is proposed in [ 103 ]. By minimizing the expectation of additional error covariance incurred by quantized measurements, the function optimal quantization factor is derived.…”