# Back to the Source: an Online Approach for Sensor Placement and Source Localization

## TL;DR

This paper introduces an online method for source localization in networks that adaptively deploys sensors to identify the origin of an infection or rumor efficiently, even during ongoing outbreaks, with minimal sensor use.

## Contribution

It presents the first online adaptive sensor placement approach for source localization that works in real-time and on general network topologies.

## Key findings

- Effective source localization with few sensors
- Works during ongoing epidemics
- Robust to random transmission delays

## Abstract

Source localization, the act of finding the originator of a disease or rumor in a network, has become an important problem in sociology and epidemiology. The localization is done using the infection state and time of infection of a few designated sensor nodes; however, maintaining sensors can be very costly in practice. We propose the first online approach to source localization: We deploy a priori only a small number of sensors (which reveal if they are reached by an infection) and then iteratively choose the best location to place new sensors in order to localize the source. This approach allows for source localization with a very small number of sensors; moreover, the source can be found while the epidemic is still ongoing. Our method applies to a general network topology and performs well even with random transmission delays.