Optimization-Based Approaches for Minimizing Deployment Costs for Wireless Sensor Networks with Bounded Estimation Errors

Abstract

As wireless sensor networks have become more prevalent, data from sensors in daily life are constantly being recorded. Due to cost or energy consumption considerations, optimization-based approaches are proposed to reduce deployed sensors and yield results within the error tolerance. The correlation-aware method is also designed in a mathematical model that combines theoretical and practical perspectives. The sensor deployment strategies, including XGBoost, Pearson correlation, and Lagrangian Relaxation (LR), are determined to minimize deployment costs while maintaining estimation errors below a given threshold. Moreover, the results significantly ensure the accuracy of the gathered information while minimizing the cost of deployment and maximizing the lifetime of the WSN. Furthermore, the proposed solution can be readily applied to sensor distribution problems in various fields.

Toward Integrated Large-Scale Environmental Monitoring Using WSN/UAV/Crowdsensing: A Review of Applications, Signal Processing, and Future Perspectives

“…Besides the approximation strategies, another important aspect concerns the nature of the physical field to be estimated. If x can be assumed to be a spatio-temporally stationary stochastic process, as is the case for instance in flooding and long-term precipitation monitoring, more advanced approaches can be used to combine multiple snapshots using a proper deployment of fixed sensors that guarantee a cost-effective solution in terms of both processing time and energy [306,307]. On the other hand, if the physical field x is assumed to be non-stationary, a condition experienced in crowdsensing and UAV-based environmental monitoring tasks due to sensor mobility, higher-order statistics need to be computed from multiple snapshots and included in the optimization problem to obtain a proper dynamic deployment of the sensors, so that the dynamics of the physical field can be accurately tracked [308].…”