# SaSTL: Spatial Aggregation Signal Temporal Logic for Runtime Monitoring in Smart Cities

## TL;DR

SaSTL introduces new spatial operators to extend STL for smart city monitoring, enabling expressive, efficient, and scalable runtime safety and performance verification using large-scale sensor data.

## Contribution

SaSTL develops novel spatial aggregation and counting operators, along with efficient parallel monitoring algorithms, to enhance expressiveness and scalability for smart city requirements.

## Key findings

- SaSTL covers more city requirements than existing logics.
- SaSTL reduces monitoring computation time significantly.
- SaSTL improves safety and performance in smart city simulations.

## Abstract

We present SaSTL -- a novel Spatial Aggregation Signal Temporal Logic -- for the efficient runtime monitoring of safety and performance requirements in smart cities. We first describe a study of over 1,000 smart city requirements, some of which can not be specified using existing logic such as Signal Temporal Logic (STL) and its variants. To tackle this limitation, we develop two new logical operators in SaSTL to augment STL for expressing spatial aggregation and spatial counting characteristics that are commonly found in real city requirements. We also develop efficient monitoring algorithms that can check a SaSTL requirement in parallel over multiple data streams (e.g., generated by multiple sensors distributed spatially in a city). We evaluate our SaSTL monitor by applying to two case studies with large-scale real city sensing data (e.g., up to 10,000 sensors in one requirement). The results show that SaSTL has a much higher coverage expressiveness than other spatial-temporal logics, and with a significant reduction of computation time for monitoring requirements. We also demonstrate that the SaSTL monitor can help improve the safety and performance of smart cities via simulated experiments.