Energy-Constrained Signal Detection and Routing in Wireless Sensor Networks Using Joint Statistical Optimization

In wireless sensor networks (WSNs), energy limitations remain a primary challenge to the reliability and longevity of deployed systems, particularly in remote or inaccessible environments. Traditional approaches often treat signal detection and data routing as separate optimization problems, leading to inefficiencies in resource utilization. This paper presents a unified framework that integrates statistical signal detection theory with optimal routing strategies under strict energy constraints. By formulating the problem as a joint statistical optimization task, the model captures the trade-offs between detection accuracy, routing cost, and energy consumption. The proposed approach uses Bayesian decision theory for signal detection and a convex optimization scheme for routing paths that minimize overall energy expenditure while preserving detection performance. Theoretical analyses demonstrate that the joint framework outperforms decoupled strategies in both detection reliability and network lifetime. Comprehensive simulations are conducted on dynamically varying network topologies to evaluate the model’s performance under different node densities, noise levels, and energy budgets. Results demonstrate consistent and substantial improvements in packet delivery ratio, false detection rate, and energy consumption compared to benchmark methods, with energy savings exceeding 35% while maintaining detection probability above 0.93. Furthermore, the algorithm adapts to fluctuating signal-to-noise ratios and network degradation over time, making it robust for real-world deployment scenarios such as disaster response, structural health monitoring, and smart agriculture. This integrated approach offers a promising pathway for future WSN designs that demand both accuracy and sustainability.