Rate-Distributed Spatial Filtering Based Noise Reduction in Wireless Acoustic Sensor Networks

Abstract

In wireless acoustic sensor networks (WASNs), sensors typically have a limited energy budget as they are often battery driven. Energy efficiency is therefore essential to the design of algorithms in WASNs. One way to reduce energy costs is to only select the sensors which are most informative, a problem known as sensor selection. In this way, only sensors that significantly contribute to the task at hand will be involved. In this work, we consider a more general approach, which is based on rate-distributed spatial filtering. Together with the distance over which transmission takes place, bit rate directly influences the energy consumption. We try to minimize the battery usage due to transmission, while constraining the noise reduction performance. This results in an efficient rate allocation strategy, which depends on the underlying signal statistics, as well as the distance from sensors to a fusion center (FC). Under the utilization of a linearly constrained minimum variance (LCMV) beamformer, the problem is derived as a semi-definite program. Furthermore, we show that rate allocation is more general than sensor selection, and sensor selection can be seen as a special case of the presented rate-allocation solution, e.g., the best microphone subset can be determined by thresholding the rates. Finally, numerical simulations for the application of estimating several target sources in a WASN demonstrate that the proposed method outperforms the microphone subset selection based approaches in the sense of energy usage, and we find that the sensors close to the FC and close to point sources are allocated with higher rates.

Elite Immune Ant Colony Optimization-Based Task Allocation for Maximizing Task Execution Efficiency in Agricultural Wireless Sensor Networks

“…Exhaustive search can achieve high task execution efficiency compared with other schemes, but its computational complexity is too high for practical implementations. For this reason, many heuristics have been proposed for the practical situation [16], such as genetic algorithm (GA), ant colony optimization (ACO), and particle swarm optimization. Aiming at this problem in heterogeneous wireless sensor networks, GA is first found and its capability is evaluated through the task execution efficiency in [17].…”

“…In equation (16), the matrix W ij is the urgency degree of the j th task being assigned, and it is composed of m identical matrixes, which is w 1 w 2 w 3 ⋯ w n ½ . According to (3) and (16), g ij is a matrix of benefit value that can be calculated in (17). 4.5.…”

Relative Acoustic Transfer Function Estimation in Wireless Acoustic Sensor Networks

“…Secondly, based on the framework presented in [19], we develop for both the CS and CW approach a model-driven rate-distribution algorithm for RTF estimation in WASNs, referred to as MDRD-CS and MDRD-CW. The model-driven problems are formulated by minimizing the total transmission costs between all microphone nodes and the FC and constraining the expected RTF estimation performance.…”

“…To achieve reliable transmissions, b k bits per sample at most can be transmitted from microphone k to the FC at each frequency bin. Based on the channel SNR (29) and the capacity (30), we can formulate the transmitted energy as [9], [10], [19], [20], [33]…”

“…We recommend to utilize randomized rounding, since this technique can guarantee that the integer solution obtained in this way always satisfies the performance requirement. The randomized rounding technique is detailed in [19], [38], the complexity of which is linear in K.…”