On the role of multi-objective optimization in risk mitigation for critical infrastructures with robotic sensor networks

Abstract

The use of robotic sensor networks (RSNs) for Territorial Security (TerrSec) applications has earned an increasing popularity in recent years. In Critical Infrastructure Protection (CIP) applications, the RSN goal is to provide the information needed to maintain a secure perimeter around the desired infrastructure and efficiently coordinate a corporate response to any event that arises in the monitored region. Such a response will only involve the most suitable robotic nodes and must successfully counter any detected vulnerability in the system. This paper is a preliminary study of the role played by multi-objective optimization (MOO) in the elicitation of responses from a risk-aware RSN that is deployed around a critical infrastructure. Contrary to previous studies showcasing a pre-optimization auctioning scheme, where the RSN nodes bid on the basis of their knowledge of the event, we introduce a post-optimization auctioning scheme in which the nodes place their bids knowing what their final positions along the perimeter will be, hence calling for a more informed decision at the node level. The impact of the pre- vs. post-optimization stage in a first-price sealed bid auction system over the risk mitigation strategies elicited by the RSN is evaluated and discussed. Empirical results reveal that the pre-optimization auctioning is more suitable for dense RSNs whereas the post-optimization one is preferred in sparse RSNs. To the best of our knowledge, this is the first attempt to assess the role of MOO in risk mitigation for CIP with RSNs.