Accurate indoor positioning is becoming increasingly important, especially in highly automated industrial environments with robots. In addition, LED-based lighting is also being used more and more frequently in such application fields. In the present work, the possibility to exploit the LED lighting infrastructure with a novel approach for implementing an accurate indoor positioning system is investigated. For this purpose, a demonstrator luminaire LEDPOS is proposed and evaluated that combines visible light sensing based on backscattered reflections to accurately estimate the two-dimensional position of a retroreflective foil at the floor while providing simultaneously an unimpaired room illumination. In particular, the same LED elements are shared for illumination and for the sensing functionality. Furthermore, the algorithm for data evaluation and position determination is based on a machine learning approach that is implemented on the edge in the luminaire. Thus, the presented approach allows for a simple and cost-efficient implementation in different applications. The experimental characterization of the LEDPOS demonstrator in a real-world scenario shows that a very good positioning accuracy can be achieved, in which the average error for the two-dimensional position of the retroreflective foil within an area of 0.64 m ² remains in the range of 3 cm.

Identification and sensing are two of the main tasks a wireless sensor node has to perform in an Internet of Things (IoT) environment. Placing active powered nodes on objects is the most usual approach for the fulfillment of these functions. With the expected massive increase of connected things, there are several issues on the horizon that hamper the further deployment of this approach in an energy efficient, sustainable way, like the usage of environmentally hazardous batteries or accumulators, as well as the required electrical energy for their operation. In this work, we propose a novel approach for performing the tasks of identification and sensing, applying visible light sensing (VLS) based on light emitting diode (LED) illumination and utilizing retroreflective foils mounted on a moving object. This low cost hardware is combined with a self-developed, low complex software algorithm with minimal training effort. Our results show that successful identification and sensing of the speed of a moving object can be achieved with a correct estimation rate of 99.92%. The used foils are commercially available and pose no threat to the environment and there is no need for active sensors on the moving object and no requirement of wireless radio frequency communication. All of this is achievable whilst undisturbed illumination is still provided.

Making the Internet of Things “green” has become a major research focus in recent years. The anticipated massive increase in the numbers of sensor and communication devices makes this endeavor even more important, resulting in various solution approaches ranging from energy harvesting to energy efficient routing schemes. In this work, we propose a system that can perform some of the main tasks of the Internet of Things, namely identification and sensing of an indoor moving object, by the means of visible light sensing in combination with off-the-shelf retroreflective foils, without the necessity to place any actively powered components on the object itself. By utilizing the supervised machine learning approach of random forest, we show that these two tasks can be fulfilled with up to 99.96% accuracy. Based on our previous findings in this regard, we propose some advancements and improvements of the overall system, yielding better results in parallel with an increased complexity of the system. Furthermore, we expand the number of performable tasks toward additional movement direction determination. The achieved results demonstrate the applicability of visible light sensing and its potentials for a “green” Internet of Things.

We present a novel approach to perform passive visible light sensing of retroreflective foils mounted on a moving object by utilizing low-cost hardware combined with a self-developed, low complex software algorithm with minimal training effort for successful classification. Therewith, we show the feasibility of utilizing the visible light spectrum not only for illumination, but also to perform sensing tasks, which consequently will lead to less energy consumption, no need for active sensors on the moving object, and finally no necessity of wireless radio frequency communication between the object and the processing device.