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

2025-08-11

AI Summary

To address the challenge of scalable, energy-efficient, and robust predictive data collection in wireless sensor networks (WSNs), this paper proposes STAIR—a novel framework that jointly leverages constructive interference communication and submodular optimization to design a spatiotemporal node activation mechanism achieving near-optimal scheduling with only coarse-grained topology information. STAIR further introduces a multivariate linear regression model enabling high-fidelity reconstruction of unobserved spatial locations and temporal instances under sparse spatiotemporal sampling. Theoretical analysis provides performance guarantees on solution quality. Extensive evaluation on a real-world testbed demonstrates that, compared to state-of-the-art baselines, STAIR reduces the total mean squared prediction error by 32.7%, improves energy efficiency by 2.1×, and decreases data collection latency by 41.5%. These results confirm STAIR’s suitability for large-scale, resource-constrained WSN deployments.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning
• Reasoning under Uncertainty: Stochastic Optimization

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Data management and stream processing for Web, mobile and wireless applications
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines

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.

Problem

• Enables energy-efficient data collection using constructive interference
• Optimizes sensor activation with submodular algorithm for minimal error
• Scalable framework for WSNs under strict resource constraints

Innovation

• Leverages constructive interference for energy efficiency
• Uses submodular optimization for sensor activation
• Employs multivariate regression for data prediction