Blending lossy and lossless data compression methods to support health data streaming in smart cities

Abstract

The digital transformation process has significantly boosted the widespread adoption of telemedicine and the utilization of wearable devices for vital signs remote monitoring. However, implementing a system for continuous monitoring of the population’s vital signs, with data being streamed from various locations within a smart city context, faces significant challenges. These challenges are related to bandwidth consumption, communication latency, and storage capacity due to the large volume of data. To overcome these challenges, a common practice consists in modeling an edge-fog-cloud layered architecture. The literature lacks software solutions capable of managing the simultaneous transmission of various vital signs data from geographically distributed individuals while maintaining the ability to generate health notifications promptly. In this context, we propose the VSAC (Vital Sign Adaptive Compressor) model, which combines lossy and lossless data compression algorithms in a layered architecture to support healthcare demands in a smart city. The main contribution is how we blend both strategies: we first use lossy compression to collect only valuable vital sign data for everyone, applying lossless algorithms afterwards to reduce the number of bytes before sending it to higher layers. We provide a real-time processing protocol that facilitates the collection of heterogeneous data distributed across different city regions. After executing a VSAC prototype, the results indicate that orchestrating the aforementioned two data compression algorithms is more efficient than conventional data reduction methods. In particular, we obtained gains of up to 42% when measuring the compression rate metric.