Strategies for data supply in high-granularity data trade in smart cities

Abstract

The smart city infrastructures, such as digital platforms, edge computing, and fast 5G/6G networks, bring new possibilities to use near-real-time sensor data in digital twins, AR applications, and Machine-to-Machine applications. In addition, AI offers new capabilities for data analytics, data adaptation, event/anomaly detection, and prediction. However, novel data supply and use strategies are needed when going toward higher-granularity data trade, in which a high volume of short-term data products is traded automatically in dynamic environments. This paper presents offering-driven data supply (ODS), demand-driven data supply (DDS), event and offering-driven data supply (EODS), and event and demand-driven data supply (EDDS) strategies for high-granularity data trade. Computer simulation was used as a method to evaluate the use of these strategies in supply of air quality data for four user groups with different requirements for the data quality, freshness, and price. The simulation results were stored as CSV files and analyzed and visualized in Excel. The simulation results and SWOT-analysis of the suggested strategies show that the choice between the strategies is case-specific. DDS increased efficiency in data supply in the simulated scenarios. There was higher profit and revenues and lower costs in DDS than in ODS. However, there are use cases that require the use of ODS, as DDS does not offer ready prepared data for instant use of data. EDDS increased efficiency in data supply in the simulated scenarios. The costs were lower in EODS, but EDDS produced clearly higher revenues and profits.