Data collection path planning with spatially correlated measurements using growing self-organizing array

Abstract

Data collection path planning is a problem to determine a cost-efficient path to read the most valuable data from a given set of sensors. The problem can be formulated as a variant of the combinatorial optimization problems that are called the price-collecting traveling salesman problem or the orienteering problem in a case of the explicitly limited travel budget. In these problems, each location is associated with a reward characterizing the importance of the data from the particular sensor location. The used simplifying assumption is to consider the measurements at particular locations independent, which may be valid, e.g., for very distant locations. However, measurements taken from spatially close locations can be correlated, and data collected from one location may also include information about the nearby locations. Then, the particular importance of the data depends on the currently selected sensors to be visited by the data collection path, and the travel cost can be saved by avoiding visitation of the locations that do not provide added value to the collected data. This is a computationally challenging problem because of mutual dependency on the cost of data collection path and the possibly collected rewards along such a path. A novel solution based on unsupervised learning method called the Growing Self-Organizing Array (GSOA) is proposed to address computational challenges of these problems and provide a solution in tens of milliseconds using conventional computational resources. Moreover, the employed GSOA-based approach allows to exploit capability to retrieve data by wireless communication or remote sensing, and thus further save the travel cost.