CESI: Sparse Input Spatial Interpolation for Heterogeneous and Noisy Hybrid Wireless Sensor Networks

This chapter delves into the complexities of spatial interpolation in hybrid wireless sensor networks (HWSNs), which combine sensors of varying costs to balance budget and deployment density. The text highlights the challenges posed by low-cost sensors, which often compromise accuracy and reliability, leading to highly heterogeneous and noisy datasets. Traditional spatial interpolation models, designed for homogeneous, high-quality sensor data, struggle with these modern IoT protocols that favor narrow, sparse data formats. The chapter introduces the Context Encoder Spatial Interpolation (CESI) Model, one of the first spatial interpolation models tailored for narrow-format sparse input. CESI effectively addresses the heterogeneity in HWSN datasets and achieves significant performance gains. The model includes a self-supervised context embedding module that uses variational inference to learn the probabilistic encoding of input observations, improving robustness against noise and universality across different tasks. The chapter also discusses the challenges of sparse input, including the high dimensional nature and sensitivity to noise, and presents the CESI model's superior performance compared to baselines on three publicly available real-world HWSN datasets. The text concludes by emphasizing the potential of the CESI Model for broader applications in environmental monitoring, urban planning, and other domains reliant on sparse spatial data.