Semantic positioning via structured sparsity models

Semantic positioning is a new paradigm emerging with the Internet-of-Things (IoT) technology and its application to context-aware services in smart-spaces. Specifically, it refers to the problem of detecting user actions and locations based on prior characterization of the space and sensed data. Differently from classic positioning, input data are measurements of the interaction between human and sensors and location information is not a vector of coordinates but a a point in a topological map. In this paper, we tackle this challenge with a mere passive monitoring system in order to preserve user privacy, handle device heterogeneity, energy efficiency and utilizing low-complexity sensors that are able to capture events generated by human actions. We develop a structured sparsity model based on the notion of discrete Radon transforms on homogeneous space in order to construct mappings from events to actions and from actions to semantic locations. We propose algorithms for human activity detection and semantic positioning. Specifically, the Least Absolute Residual and Shrinkage Operator (LARSO) for human action detection, and a mixed-norm optimization to perform semantic positioning. Simulation results are shown to validate the proposed model and compare different algorithms.