Fusion in sensor networks with communication constraints

Abstract

In this paper, we address the problem of optimizing the detection performance of sensor networks under communication constraints on the common access channel. Our work helps understanding tradeoffs between sensor network parameters like number of sensors, degree of quantization at each local sensor, and SNR. Traditionally, this problem is tackled using asymptotic assumptions on the number of sensors, an approach that leads to the abstraction of important details such as the structure of the fusion center. We adopt a non-asymptotic approach and optimize both, the sensing and the fusion sides with respect to the probability of detection error. We show that the optimal fusion rule has an interesting structure similar to the majority-voting rule. In addition, we study the convergence with respect to the number of sensors of the performance of the fusion rule. We show that convergence is SNR dependent and that, in low-SNR environments, asymptotics may require a large number of sensors.

A Cross-Layer Design for Decentralized Detection in Tree Sensor Networks

“…This causes higher probability of information loss due to the high communication rate. (2) In-network processing, where information is compressed by calculating its Log Likelihood Ratio (LLR), then the LLR is quantized before transmission using a limited number of quantization bits. This scheme reduces the communication rate and increases the probability of successful transmission, but suffers from irrecoverable loss of information caused by the in-network processing.…”

“…[1], [2]. Protocols for communication layers have to be co-designed to optimize the detection performance.…”

Collaborative Environmental Monitoring with Hierarchical Wireless Sensor Networks

“…First, through theoretical analysis and simulation validation, we make the following recommendations on whether to collaborate or not: when each cluster head has a large amount of data to process (namely, each cluster contains a large number of sensor nodes) and multi-hop relay is necessary to communicate with a fusion center (namely, cluster heads have limited communication range), decentralized data processing among cluster heads is more efficient; otherwise centralized decision-making with the aid of a fusion center can be advantageous. Previous work, such as (Rabbat and Nowak, 2004;Aldosari and Moura, 2004), has suggested similar network design principles in the context of decentralized infrastructures: when each sensor node collects a large amount of data or the size of the network is large, collaborative processing is more efficient than centralized decision-making. This paper extends the conclusions to hierarchical networks, and compares decentralized versus centralized processing among cluster heads rather than among all sensor nodes.…”

Centralized and decentralized detection with cost-constrained measurements

“…Detection and estimation problems considering system resource constraints have extensively been studied in the literature [4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20]. In [4], measurement cost minimization is performed under various estimation accuracy constraints.…”

“…It is shown that using non-orthogonal communication between local sensors and the fusion center improves fusion performance monotonically. In [19], the optimization of detection performance of a sensor network is studied under communication constraints, and it is found that the optimal fusion rule is similar to the majority-voting rule for binary decentralized detection.…”