BER vs SINR: Selection of a metric for physical interference models for flooding messaging and WSN recovery

Abstract

Ensuring the reliable delivery of alarm messages under emergency conditions is a critical challenge in wireless sensor networks (WSNs), particularly when employing flood transmission mode, as the simultaneous, unsynchronized broadcasting by multiple transmitters leads to mutual interference in channels. In the existing literature, the physical communication channel is typically evaluated using either the Signal-to-Interference-plus-Noise Ratio (SINR) or the Bit Error Rate (BER). However, the cumulative nature of SINR makes it impossible to discern the individual contribution of each node to the aggregate interference, while estimating BER through bit error counting requires significant time and computational resources. This article presents an analysis of the impact of mutual interference on both BER and SINR functions, aiming to facilitate their rational integration for optimizing message routing strategies. The proposed analytical model describes BER as a function of environmental parameters, including Signal-to-Noise Ratio (SNR), Interference-to-Noise Ratio (INR), Signal and Interference Orthogonality Coefficient (SIOC) and the Rician K-factor as signal fading depth. The algorithm for constructing rapid data routing with minimal relay nodes is based on estimates of packet loss rates with erroneous bits. Meanwhile, route segment lengths correspond to retransmission frequency thresholds—specifically, no more than two packet requests per segment. An optimized algorithm for the accelerated selection of backup node locations relies on branch-and-bound method. This methodology reduces the initial set of solution options by a factor of 3.5 in the presented example. For a priori uncertainty conditions regarding the Rician K-factor, we propose to select the backup node channel with the minimal BER based on indirect estimates of SNR and INR. The research significantly contributes to the development and application of interference-resistant WSNs in critical applications, such as emergency response and industrial monitoring systems.