Optimizing Collaborative Beamforming Strategies for Energy-Efficient Wireless Sensor Networks in Large-Scale IoT Deployments

Abstract

This research presents a novel approach to collaborative beamforming optimization in large-scale Internet of Things (IoT) deployments, focusing on energy efficiency in wireless sensor networks (WSNs). We introduce a mathematical framework for analyzing the trade-offs between beamforming gain, energy consumption, and network lifetime in densely deployed sensor networks. Our methodology incorporates stochastic geometry to model random node distributions and develops closed-form expressions for expected beamforming gain under realistic channel conditions. We propose a distributed optimization algorithm that dynamically adjusts beamforming weights based on local energy constraints and global performance objectives. Extensive numerical simulations demonstrate that our approach achieves up to 43% improvement in energy efficiency compared to existing methods while maintaining comparable communication reliability. Field experiments conducted across three different environmental settings validate our theoretical findings, showing that the proposed collaborative beamforming strategy extends network lifetime by 37% while reducing transmission power requirements by 29% on average. We further analyze the scalability properties of our approach and characterize the fundamental limits of collaborative gain in the presence of synchronization errors and hardware imperfections. This work provides important insights for the design and deployment of energy-constrained IoT networks requiring long-term operation without human intervention.