Sequential Distributed Detection in Energy-Constrained Wireless Sensor Networks

TL;DR

This paper introduces a novel energy-efficient sequential distributed detection method for wireless sensor networks that encodes overshoot information into transmission delays, improving detection performance under resource constraints.

Contribution

It proposes a new approach using pulse position modulation to encode overshoot in level-triggered sampling, enhancing detection accuracy in energy-constrained sensor networks.

Findings

Significant reduction in detection delay compared to traditional methods

Effective encoding of overshoot improves decision accuracy

Applicable to various target detection models in sensor networks

Abstract

The recently proposed sequential distributed detector based on level-triggered sampling operates as simple as the decision fusion techniques and at the same time performs as well as the data fusion techniques. Hence, it is well suited for resource-constrained wireless sensor networks. However, in practical cases where sensors observe discrete-time signals, the random overshoot above or below the sampling thresholds considerably degrades the performance of the considered detector. We propose, for systems with stringent energy constraints, a novel approach to tackle this problem by encoding the overshoot into the time delay between the sampling time and the transmission time. Specifically, each sensor computes the local log-likelihood ratio (LLR) and samples it using level-triggered sampling. Then, it transmits a single pulse to the fusion center (FC) after a transmission delay that is proportional to the overshoot, as in pulse position modulation (PPM). The FC, upon receiving a bit decodes the corresponding overshoot and recovers the transmitted LLR value. It then updates the approximate global LLR and compares it with two threshold to either make a decision or to continue the sequential process. We analyze the asymptotic average detection delay performance of the proposed scheme. We then apply the proposed sequential scheme to target detection in wireless sensor networks under the four Swerling fluctuating target models. It is seen that the proposed sequential distributed detector offers significant performance advantage over conventional decision fusion techniques.