Point coverage with heterogeneous sensor networks: A robust optimization approach under target location uncertainty

Abstract

In this study, we consider the point coverage problem and aim to locate heterogeneous sensor networks under target uncertainty. Target location uncertainty phenomenon is commonly seen in some fields such as military and security, where only probabilistic information regarding target location is usually available via intelligence, historical data, and expert opinions. In these circumstances, possible scenarios are usually generated and several courses of action are developed to address these scenarios. Taking into account this real-life phenomenon as well as the realistic constraints associated with it, we firstly develop an integer nonlinear program for locating a heterogeneous sensor network with hub-spoke topology for a given target scenario. In this topology, wireless sensors constitute the lower level network, while the hubs constitute the upper level by collecting data from the sensors and fusioning the transmitted detection data. Secondly, we propose a linear approximation for the nonlinear model which provides computational efficiency for solving large size problem instances. Then, utilizing the p-robustness concept, we develop a robust counterpart of the deterministic formulation which accounts for multiple scenarios simultaneously and generates a compromise solution that ensures a predefined threshold for regret percentages of individual scenarios. For the first time in the point coverage literature, we define the heterogeneous sensor network location problem within target location uncertainty concept and propose an efficient robust optimization approach for solving it. Finally, we present an illustrative case study to show the applicability of the proposed robust solution approach.