Terrain-influenced incremental watchtower expansion for wildfire detection.

Abstract

Optimizing the effectiveness of early wildfire detection systems is of significant interest to the community. To this end, watchtower-based wildfire observations are continuing to be practical, often in conjunction with state-of-the-art technologies, such as automated vision systems and sensor networks. One of the major challenges that the community faces is the optimal expansion of existing systems, particularly in multiple stages due to various practical, political and financial constraints. The notion of incremental watchtower expansion while preserving or making minimal changes to an existing system is a challenging task, particularly while meeting coverage and financial constraints. Conventionally and historically, this problem has been treated as a multi-objective optimization problem, and as such, currently employed methods are predominantly focused on the full-fledged optimization problem, where the problem is re-solved every time during the expansion process. In this paper, for the first time, we propose an alternative approach, by treating the expansion as a submodular set-function maximization problem. By theoretically proving that the expansion problem is a submodular set-function maximization problem, we provide four different models and matching algorithms to handle various cases that arise during the incremental expansion process. Our evaluation of the proposed approach on a practical dataset from a forest park in China, namely, the NanJing forest park, shows that our algorithms can provide an excellent coverage by integrating visibility analysis and location allocation while meeting the stringent budgetary requirements. The proposed approach can be adapted to areas of other countries.