Neighbor Discovery in a Wireless Sensor Network: Multipacket Reception Capability and Physical-Layer Signal Processing

TL;DR

This paper enhances neighbor discovery in wireless sensor networks by incorporating physical layer signal processing and random set theory, enabling more accurate detection amidst interference and uncertainty.

Contribution

It introduces physical layer considerations into neighbor discovery evaluation and applies random set theory to handle environmental uncertainties.

Findings

Physical layer modeling improves discovery performance assessment.

Random set theory outperforms classical detection methods under uncertainty.

The approach effectively manages unknown neighbor counts and signal parameters.

Abstract

In randomly deployed networks, such as sensor networks, an important problem for each node is to discover its \textit{neighbor} nodes so that the connectivity amongst nodes can be established. In this paper, we consider this problem by incorporating the physical layer parameters in contrast to the most of the previous work which assumed a collision channel. Specifically, the pilot signals that nodes transmit are successfully decoded if the strength of the received signal relative to the interference is sufficiently high. Thus, each node must extract signal parameter information from the superposition of an unknown number of received signals. This problem falls naturally in the purview of random set theory (RST) which generalizes standard probability theory by assigning \textit{sets}, rather than values, to random outcomes. The contributions in the paper are twofold: first, we introduce the realistic effect of physical layer considerations in the evaluation of the performance of \textit{logical} discovery algorithms; such an introduction is necessary for the accurate assessment of how an algorithm performs. Secondly, given the \textit{double} uncertainty of the environment (that is, the lack of knowledge of the number of neighbors along with the lack of knowledge of the individual signal parameters), we adopt the viewpoint of RST and demonstrate its advantage relative to classical matched filter detection method.