Evaluating Urban Network Activity Hotspots through Granular Cluster Analysis of Spatio-Temporal Data

Multi-access Edge Computing (MEC) is expected to play an essential role in enabling 5G (and beyond) technologies and services. This has driven numerous micro-datacenter (μDC) deployment studies in the literature, with a common goal of addressing the optimal μDC placement and dimensioning problems. Along this line, this paper aims at clustering subareas with similar network activity dynamics, to find a good hotspots' representation over the urban area. Leveraging common Machine Learning (ML) and statistics principles, the main contribution of this paper is two-fold: (i) the definition and selection of dynamicity features based on real telecommunications datasets; and (ii) the granular cluster evaluation and analysis based on agglomerative hierarchical clustering. Three feature sets (containing 20, 12 and 8 features, respectively) are evaluated at varying precision levels, showing interesting trends on the number of clusters, heatmaps and intra-cluster correlation. These could potentially provide some valuable indications on the placement and dimensioning of the μ DCs.