## Relevant Contributions Related to Sensor Placement  
Sensor placement is an important task in sensor network-based applications such as the design of indoor positioning systems, since the number of sensors and their location affect the accuracy and cost of the whole system. Among the 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 the information on the distances between the target and the anchor nodes have been obtained, trilateration or multilateration can be used to estimate the position of the target (considering that the target is an emitter and the anchors are sensors). Thus, it is very important that the placement of sensors; in a realistic situation, it is needed for placing sensors to cover the whole area, or not, in order to obtain the best accuracy in certain areas. Placing sensors should minimize the position error bound, which is a widely used metric.  
In Reference [1], after reviewing the state-of-the-art of sensor placement for localization, authors have reached the neediness to resort to heuristic methods when deploying sensors in a complex scenario, considering complex functions for the measurement error model or focusing on a whole region of interest (ROI) instead of a single target. In the work, multi-objective evolutionary optimization is applied to obtain the optimum sensor placement. Inspired by works in the literature, the well-known non-dominated sorting genetic algorithm (NSGA-II) has been adapted to solve the sensor placement problem for target localization. The work is a research continuation of previous works from the same authors, where they used a standard multi-objective genetic algorithm to place sensors after considering multiple criteria: placing a fixed number of sensors for localization with range-difference measurements; considering a variable number of sensors, as well as non-line of sight (NLOS) conditions; etc. Applying the algorithm considering a variable number of sensors, without modifications, causes severe problems. Thus, the authors have had to modify the original NSGA-II by adding speciation and evolving subpopulations according to the size of different sensor sets. The obtained results show a considerable improvement over the standard NSGA-II.  
The advantages of this work compared to other works in optimum sensor placement for target localization include the multi-objective optimization of different metrics from the Cramer-Rao lower bound; the unconstrained position of the sensors; the consideration of obstacles that can cause occlusions to NLOS sensors; and that the number of sensors can vary within an interval.  
Since conflicting objectives have been optimized, the authors obtained a set of Pareto optimal solutions. This supposes the greatest advantage of multi-objective optimization, since every optimal solution has been obtained quantifying the values of those objectives. This information can be used by the resource manager according to the current needs and availability. Until now, there are not any other researchers that address the sensor placement problem for localization in a similar way. Most of the multi-objective optimization works referenced in surveys focus on sensor deployment for optimizing coverage and energy management, and those that deal with target tracking only address the sensor-scheduling problem.