SWARM-IS: Adaptive sensor swarm search strategy and simulation framework for predator surveillance and control

Abstract

Effective monitoring of predator eradication and control efforts critically depends on reliable detection across diverse and heterogeneous environments. However, existing search strategies do not take full advantage of the developed AI-enabled sensing technologies. In this paper, we present SWARM-IS, a novel adaptive sensor swarm strategy that dynamically optimises the spatial deployment of predator-detecting sensors based on their collective detection histories. The method leverages collective swarm information to iteratively guide sensor placement, enabling responsive adaptation to evolving environmental and ecological conditions. To evaluate the proposed approach, we developed a modular surveillance simulation framework that models varying predator densities, spatial ecology, and sensor detection efficacy. The performance of SWARM-IS is compared against conventional systematic and random search strategies across a range of ecological scenarios. The framework is generalisable and can be readily adapted to other predator surveillance and management contexts. Using this framework, we conducted a case study on brushtail possum surveillance under diverse ecological conditions and detection-efficacy regimes. Results demonstrate that the proposed adaptive swarm-based strategy can achieve up to a 40% absolute improvement in the number of individuals removed from the site compared to conventional approaches, highlighting its potential to enhance predator monitoring and, consequently, improve management outcomes.