Abstract
This paper focuses on optimal sensor deployment for indoor localization with a multi-objective evolutionary algorithm. Our goal is to obtain an algorithm to deploy sensors taking the number of sensors, accuracy and coverage into account. Contrary to most works in the literature, we consider the presence of obstacles in the region of interest (ROI) that can cause occlusions between the target and some sensors. In addition, we aim to obtain all of the Pareto optimal solutions regarding the number of sensors, coverage and accuracy. To deal with a variable number of sensors, we add speciation and structural mutations to the well-known non-dominated sorting genetic algorithm (NSGA-II). Speciation allows one to keep the evolution of sensor sets under control and to apply genetic operators to them so that they compete with other sets of the same size. We show some case studies of the sensor placement of an infrared range-difference indoor positioning system with a fairly complex model of the error of the measurements. The results obtained by our algorithm are compared to sensor placement patterns obtained with random deployment to highlight the relevance of using such a deployment algorithm.
Highlights
Sensor placement is an important task in the design of indoor positioning systems, since the amount of sensors and their location affect the accuracy and the cost of the whole system
The methods that we propose in this paper can be applied to any positioning system based on the range-difference of arrival (RDOA)
In [29], we placed a fixed number of sensors for localization with range-difference measurements while considering several criteria related to accuracy, whereas in [30], we considered a variable amount of sensors, as well as non-line of sight (NLOS) conditions
Summary
Sensor placement is an important task in the design of indoor positioning systems, since the amount of sensors and their location affect the accuracy and the cost of the whole system. Among available methods for estimating the position of a target, range-based localization systems use anchor nodes and measurements that can be converted into distances or distance differences; e.g., time of arrival (TOA), time-difference of arrival (TDOA), received signal strength (RSS), etc. Once we have obtained the information about the distances between the target and the anchor nodes, we can perform trilateration or multilateration to estimate the actual position of the target. The methods that we propose in this paper can be applied to any positioning system based on the range-difference of arrival (RDOA). It does not matter if the target is an emitter or a receiver; we consider that the target is an emitter and that the anchors are sensors. We refer to anchors and sensors as the same thing
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