Modern AIoT, used in industry and smart cities, depends on fast, reliable data generated by numerous wireless sensors. These sensors often have to contend with the energy limitations of battery-powered devices. In practice, low-power devices face three interrelated challenges: MEMS noise and distortion; unpredictable delays and losses on low-energy links; and limited edge processing capabilities. These factors create misaligned or missing samples that degrade sensor fusion and real-time analytics. This thesis presents a wireless sensor network framework that improves data quality by enforcing precise time alignment while supporting embedded workloads. A central hub ingests two data sources: Bluetooth Low Energy (BLE) motion streams from sensor nodes and HTTP streams that carry labels and lower-rate signals.