Optimal Sensor Placement for Monitoring of Spatial Networks

Abstract

The problem of optimal placement of sensors for monitoring a spatial network (e.g., a road network with moving ground targets or intruders) is considered in this paper. In particular, the optimization of locations of a set of sensors (that can each obtain measurements in a local region around the sensor location) is considered so as to maximize an overall sensor coverage metric defined over the spatial network. The sensor coverage optimality metric for spatial network coverage is based on a novel formulation of a sensor influence wave based on a spatiotemporal model of the measurement reach of a set of sensors given a spatial network topology, a probabilistic model of target movements on the network, and spatial weight maps that model the relative importance/utility of different locations in the spatial region. The sensor placement optimization is based on an iterative genetic algorithm for the optimization of a scalar metric computed from the spatial integration of the sensor influence wave. The efficacy of the proposed approach is demonstrated through simulation studies for several road network geometries. Note to Practitioners —This paper considers the problem of finding optimal locations for sensors for monitoring a spatial network for moving targets (e.g., a road network with moving ground targets or intruders). Sensor-based monitoring of a spatial region is relevant in a variety of applications (including, in general, such diverse application areas as traffic monitoring in transportation applications, intruder monitoring, surveillance, power systems monitoring, structural health monitoring, etc). This paper offers two primary novel aspects: an optimality metric formulation for target monitoring effectiveness on spatial networks and an iterative genetic algorithm-based method for optimization of the sensor placement configuration. The proposed approach enables addressing of multiple spatial criteria within a unified framework, including the probabilistic characterizations of spatial target movements over the network and the models of relative importance/utility of different locations/areas in the spatial region.