Spectrum and Energy Efficient Multiple Access for Detection in Wireless Sensor Networks

TL;DR

This paper introduces SEEMA, a spectrum and energy efficient multiple access protocol for wireless sensor networks that improves detection performance by selective transmission based on observation informativeness, simplifying sensor design.

Contribution

The paper proposes a novel SEEMA protocol that enables energy and bandwidth-efficient detection in WSNs using simple sensors and superposition coding, with theoretical performance analysis.

Findings

SEEMA achieves significant transmission savings and bandwidth efficiency.

The protocol provides exponential decay of error probability with network size.

Performance is validated for various observation correlation scenarios.

Abstract

We consider a binary hypothesis testing problem using Wireless Sensor Networks (WSNs). The decision is made by a fusion center and is based on received data from the sensors. We focus on a spectrum and energy efficient transmission scheme used to reduce the spectrum usage and energy consumption during the detection task. We propose a Spectrum and Energy Efficient Multiple Access (SEEMA) transmission protocol that performs a censoring-type transmission based on the density of observations using multiple access channels (MAC). Specifically, in SEEMA, only sensors with highly informative observations transmit their data in each data collection. The sensors transmit a common shaping waveform and the fusion center receives a superposition of the analog transmitted signals. SEEMA has important advantages for detection tasks in WSNs. First, it is highly energy and bandwidth efficient due to transmission savings and narrowband transmission over MAC. Second, it can be implemented by simple dumb sensors (oblivious to observation statistics, and local data processing is not required) which simplifies the implementation as compared to existing MAC transmission schemes for detection in WSNs. We establish a finite sample analysis and an asymptotic analysis of the error probability with respect to the network size and provide system design conditions to obtain the exponential decay of the error. Specific performance analysis is developed for common non-i.i.d. observation scenarios, including local i.i.d. observations, and Markovian correlated observations. Numerical examples demonstrate SEEMA performance.