Energy-Efficient Collaborative Beamforming Strategies for Wireless Sensor Networks in Smart City Applications

Abstract

We present a mathematical model that captures the intricate relationship between sensor node placement, beamforming optimization, and network lifetime maximization under real-world constraints. Our approach formulates a non-convex optimization problem, which we address through a multi-stage iterative algorithm with guaranteed convergence. We derive closed-form solutions for optimal power allocation across collaborating sensor nodes and introduce a distributed implementation that relies on local information exchange for scalability and efficiency.   Extensive numerical simulations show that our proposed framework reduces energy consumption by up to 47\% compared to traditional methods while preserving quality-of-service requirements. Additionally, we establish theoretical bounds on achievable beamforming gains as a function of network density and topology, demonstrating that our method asymptotically reaches the theoretical upper limit in dense deployments. To validate the real-world applicability of our approach, we test our techniques using actual sensor data from urban environments, confirming their effectiveness in critical smart city applications, including environmental monitoring, public safety, and intelligent transportation systems.