A Bio-Inspired Deployment Method for Data Collection Networks in Wide White Areas

摘要

Wide white areas are defined as large regions with very little to no infrastructure. For example, deserts and large forest areas fall in this category. Many strategic phenomena and activities take place in these areas (e.g. mining, environmental monitoring) which necessitate data collection and analysis. In this context, we propose a network deployment scheme which aims at efficiently linking sparse points of interest in a very wide white area. The goal of the method is to minimize the cost of the deployment while providing a fault tolerant network. The proposed method is based on an algorithm which mimics the evolution of a type of mold called physarum. Our deployment problem is close to a Minimum Steiner Tree (MST) problem known to be NP-hard, we thus compare our results to a heuristic of MST.