Edge-Kalman Filter for Improved Quality of Service in Indoor Spatial Temporal Systems

Indoor Spatial Temporal Systems (ISTS) enable real-time location-aware services in applications like indoor navigation, healthcare monitoring, and emergency response. They rely on technologies such as Bluetooth, Wi-Fi, and Zigbee to estimate distances from Received Signal Strength Indicator (RSSI) signals, which are prone to aleatoric uncertainty. Traditional ISTS mitigate this by applying noise filtering at the cloud layer but introduces transmission latency. Recent work shifts filtering to the edge to reduce transmission latency. However, frequent noise parameter updates in the filtering process lead to a high computational load, which increases communication latency and degrades the Quality of Service (QoS). To address this, we propose a lightweight edge-Kalman Filter (eKF) that updates noise parameters only when a significant change in density ratio is detected using the Kullback-Leibler Importance Estimation Procedure (KLIEP). Experimental results show that eKF reduces computational overhead, enhances distance estimation accuracy, and scales across technologies, demonstrating its potential to improve real-time QoS in ISTS.