Scalable and Energy-Efficient Predictive Data Collection in Wireless Sensor Networks with Constructive Interference

TL;DR

This paper introduces STAIR, a scalable and energy-efficient framework for wireless sensor networks that uses constructive interference and optimization to improve data collection accuracy and reduce resource consumption.

Contribution

The paper presents STAIR, a novel framework that combines constructive interference with submodular optimization for efficient sensor data collection under resource constraints.

Findings

Validated on a real-world testbed with promising results.

Achieved near-optimal sensor activation with low prediction error.

Reduced energy consumption compared to traditional methods.

Abstract

A new class of Wireless Sensor Network has emerged whereby multiple nodes transmit data simultaneously, exploiting constructive interference to enable data collection frameworks with low energy usage and latency. This paper presents STAIR (Spatio-Temporal Activation for Intelligent Relaying), a scalable, resilient framework for Wireless Sensor Networks that leverages constructive interference and operates effectively under stringent resource constraints. Using constructive interference requires all nodes to transmit the same packet at the same time, thus, only one source node can send data per time slot. STAIR uses coarse-grained topology information to flood a selected subset of the network, relaying sensor readings from individual nodes during their allocated time slots. A submodular optimisation algorithm with proven quality bounds determines near-optimal sensor activation locations and times, aiming to minimise the sum of mean squared prediction errors from a multiple multivariate linear regression model, which is used to estimate values at unselected locations and times. This framework has been extensively validated on a real-world testbed deployment.