## 2 Materials and Methods
### Motivation and Problem Formation  
This work follows the clear vision of enabling the fulfillment of tasks, such as identification and sensing parameters of a moving object solely by backscattered visible light, without the necessity of placing wireless sensor nodes on the object itself. We were able to show that the tasks of identification and speed estimation can be achieved in such a way,by utilizing retroreflective foils on the moving object, as shown in [14,15]. In the course of advancing our proposed backscattered VLS approach, we were able to show in [15] that the highly accurate but computationally demanding algorithm utilized in [14] can be replaced by the supervised ML method of random forest. Nevertheless, motivated by our vision, we identified certain issues and limitations in our previous works that need to be addressed in order to take a step further toward applications in which these tasks, that otherwise would require battery- or accumulator-powered devices for communication and sensing, can be achieved in a backscattered \"green\" way. In this section, we describe the identified problems, which are then addressed in detail in Sections 2.3-2.6.  
As described in Section 1, our backscattered VLS approach is based on a setup in which the light sources and the photosensitive devices are placed in close vicinity to each other, thus enabling the integration of these components into a compact module, like a luminaire. This approach has the advantage that the installation effort can be reduced since only the existing light source needs to be replaced with a luminaire that integrates also the photosensitive devices. This advantage was weakened to a certain extent in [15] by the necessity of an actively powered light barrier alongside the train tracks in the experimental setup. Overcoming this necessity is the first goal of this work, formulated as follows:  
1. No additional active powered components are placed in the infrastructure of the setup (alongside the tracks).
2. In [15], we restricted the movement direction of the utilized moving object (Lego train) to be always in the same direction. Therefore, extending the sensing capabilities to be able to distinguish between two moving directions is the second goal of this work. Extend the sensing capabilities to determine the moving direction of the train.
3. Although we were able to achieve a classification accuracy of 98.8% in [15], one shortcoming was that 86.66% of the misclassifications was caused by the two different red foils out of the, in total, used 8 retroreflective foils. This issue led to the third main motivation of this work. Improve the classification accuracy, especially in regard to the utilized red foils.  
In combination with the defined goal of improving the classification accuracy, we can formulate the fourth goal, that this improvement of classification accuracy should be achieved over a larger distance between the moving object and the VLS unit, whilst not extensively increasing the energy demand of the system.  
4. Increase the distance between the moving object and the VLS unit, whilst improving the overall classification accuracy.
5. Last but not least, the fifth goal of the work presented in this manuscript is to extend the application scenario and investigate the achievable classification accuracy in a use case where additional ambient light, besides the light source on the VLS unit itself, is present. Investigate the effects of additional ambient light and outline a method to distinguish between these use cases.  
To summarize, the motivation of this work is to extend the sensing tasks to be performed to achieve a higher classification accuracy over a larger distance, without an extensive increase in energy demand of the system, and to simplify the experimental setup in order to reduce the installation effort.