Indoor localization using radio frequency is challenging because of multipath propagation and complex phenomenon that make standard positioning techniques, such as multilateration, inaccurate most of the time. However, knowing information about the environment can help overcome these issues. In this paper, we use Machine Learning to classify the environment between a Hall and a Corridor in order to use a more precise propagation model for Received Signal Strength (RSS) based localization. The classified samples are series of RSS values taken from RFID tags. Our method achieves 98.57% accuracy. The novelty of this approach is that it uses only a small series of RSS and thus is lighter than the existing techniques. To achieve this result, we use quadruplets of tags, we compare the best quadruplets for classification that we found with the best quadruplets for localization.
Nowadays, Indoor positioning is achieved through many techniques that all face different specific
issues [
Environment classification in an indoor localization scenario is traditionally achieved using Radio
Frequency (RF) signature [
In this paper, we present a new classification method based on only a small series of RSS measures
from RFID tags that can classify the environment between a hall, and a corridor. Previous papers have
shown that using only RSS gives poor classification results [
In the end, our solution is easier to apply than the previous ones in a practical scenario. It can be added to existing RSS based location systems with little effort to improve its accuracy as it allows using a specific propagation model for each environment.
We also discuss the best positioning of tags for classification purposes and compare it with the best
positioning for localization that was found in [
This paper is organized as follows, section 2 presents the classified environments and the measures. The classification methods with a single tag and quadruplets are presented in section 3. In section 4 we discuss the best tags’ quadruplets characteristics, and we compare them with the best quadruplets for localization. We conclude in section 5 with a discussion of the presented results and future work.
Experiments are performed in a hall of approximatively 205 square meters, and in a corridor of dimensions 30 m x 2 m, which can be seen in Fig. 1. Tags are alternatively placed at a 1.3 meter and a 2.1 meters height. Both environments are meshed with sixty active RFID tags.
We needed to verify that the measurements taken in the narrowest part of the hall were sufficiently different from the ones in the corridor to be labeled as hall measurement. Indeed the dimensions of these parts were similar. We used k-means and hierachical clustering to do so, with the conclusion that the natural labelling of the data was indeed the best one. 2.2.
For this work we used active UHF-RFID tags at 433 MHz band. The tags can be detected from as
far as 20 meters in an indoor environment. A bidirectional traffic detection RFID reader, having two ¼
wave antennas separated by 10.0 cm, is considered for the experiment. RS232 connector is used to
connect the reader to the computer. The tag model “Coin ID" and reader model “UTP Diff 2”, used for
this work are from RFID manufacturer Ela Innovation [
= 30.84 − 0.632 ∗ (1) Where
is the received power in dBm. RSSI is the received signal strength in decimal. 2.3.
The measurements that we will be considering here are similar to the ones presented in [
Our baseline scenario is the one of a moving agent only able to record Signal Strength at regular intervals, e.g. a robot or a pedestrian carrying a smartphone and walking in an indoor environment. The relative distance between successive RSS measures needs to be the same for each sample, in order for them to always describe the same type of information.
In a practical use scenario this can be achieved using dead reckoning or with the help of an
accelerometer for example. However, these kinds of techniques are only suitable for a few steps and
quickly become inaccurate due to sensor drift [
Moreover, reducing the amount of data for each sample implies less calculation and energy consumption. Considering that our solution aim to be used for real time localization on embedded systems these parameters are important to consider.
Therefore, for optimization and accuracy reasons, our classification method needs to use the less possible data. Moreover, each samples’ measures are related to a single tag.
The samples we use for Machine Learning are therefore series of 2x6 RSS measures taken along a line, as shown in Fig. 2. Distance between antennas is 10 cm, six measures separated by 30.0 cm approximately correspond to at least two measures per wavelength at 433 MHz, which gives a good compromise between a reduced data set and the amount of information. We used a total of 1200 samples distributed as follow, 70% for the training set and 30% for the test set. We used oversampling to balance the quantity of samples between the hall and the corridor.
To classify the two considered environments (hall and corridor), we established a set of features that describe our samples and discriminate them efficiently. We extracted mean, max, min, root mean square and other classical statistics functions of our measures set. We also extracted two subsets of data: the difference between the two antennas at a given position and the difference between two pairs of adjacent measures. The same statistics functions are applied for these two subsets. This allows the algorithms to take into account the sudden RSS variations that are characteristic of the multipath and of the fading of this kind of environments. A total of 14 features were extracted for each data sample.
Three algorithms’ results are compared in Table 1. The best classification accuracy is achieved with the Classification Tree algorithm (82% of prediction accuracy). However, this system only considers the data coming from one tag, we improve it in subsection 3.2 by considering multiple tags for classification.
One important information to notice is that we do not use tags’ ID information to perform the classification, even though it could contain useful clues about the environment. As said in section 2.2, our tags have a 20 meters range, which means a single tag’s signal can be received in many different rooms. Moreover, using ID information would require an additional pre-deployment step. Thus, we decided not to take this information into account in this first iteration of our solution. However, when deployed as a practical solution, ID information could indeed be useful to perform sanity check on the model output.
As our classification method will be used in a localization scenario using multilateration with RSS, we can assume that at least four tags will always be available for this purpose. Thus, we built a classification method that considers the output of our previous algorithms for quadruplets. We choose the new classification output by majority voting. With 60 tags there are 487635 possible quadruplets, the accuracy of our method mainly depends on the choice of goods quadruplets. Therefore, different selection criteria were considered to assess the quadruplet efficiency.
As shown in Table 2, choosing randomly from available tags does not significantly improve the classification accuracy, in some cases it even degrades it compared to single tag classification (82% for the best single tag classification). On the one hand, choosing the four tags that have the lowest mean RSS gives good results (higher than 85%) and on the contrary selecting tags with the highest mean considerably weakens the model (less than 70%). Indeed, high RSS value from a tag tends to indicate that the tag is close and therefore that the dominant path is generally the line-of-sight path, which is the same in all environments, contrary to the multipaths that depend on the environment geometry, building materials, etc. Therefore, as a high RSS value generally implies that the line-of-sight path is dominant in the signal, it means that the considered sample does not contain a lot of environment-related information, which weakens the classification accuracy. On the other hand, signal from tags that are too far are strongly attenuated and the signal strength is around the noise floor level, making it not suitable for classification. Selecting the tags that are closest to the 35th-percentile allows to find a good trade-off between strength of the signal and a sufficient distance between the sensor and the quadruplets tags. It gives very satisfying results, from 91.43% with Bagged Trees algorithm to 98.57% with k-NN algorithm.
In this paper we meshed both environments with sixty tags. However, this high density is not desirable in a practical usage scenario due to the cost of the tags as well as interference issues that reduce the tags’ signal range. Therefore, we need to identify the optimal placement of tags, i.e. the tags’ quadruplets that give the best classification results, to reduce their number. This is the subject of this section. 4.1.
We take two criteria to rank quadruplets. Firstly their accuracy (acc), i.e. the ratio of correct classifications. Secondly their visibility (vis), i.e. the number of samples they are able to classify within the test set. The visibility notion is important because a high accuracy on very few samples is not statistically significant. For example, a quadruplet that has an accuracy of 100% on only two of the test set samples will be ranked lower than a quadruplet that has an accuracy of 95% on thirty test set samples. We thus define the following score to rank quadruplets : = ( − ) ∗ log( ) ∗ − 1 (2)
Where acc is the accuracy, vis the visibility and C a threshold used to set below 0 the rankScore of quadruplets that have a very low accuracy. It was set to 0.4 for this article’s analysis so that the scores of quadruplets that achieve less than 40% classification accuracy are negative.
The three best quadruplets for classification in the corridor environment for each mentioned algorithm appear on Fig. 4. They are very similar. They all share: • •
A triangle shape, moreover, the angles that are adjacent to the side with three tags are always both acute.
Two very close tags.
One noticeable point is that the corridor is symmetric whereas the best quadruplet layouts are not. This could be explained by an electromagnetic disturbance that is not caused by the corridor geometry, e.g. a particularly important presence of ferro-magnetic materials in a wall.
Best Quadruplets in the hall environment appear on Fig. 5. The best quadruplets are not strictly identical for each algorithm; however, they share common characteristics which are: • •
Omniscience, i.e., at least one tag has a direct line-of-sight for the whole hall.
Alignment, i.e. three tags always approximately share a common straight line. Just like in the corridor, hall quadruplets have a triangular shape and two tags at least are close to each other.
Moreover, there is no significant correlation between the length of the quadruplets’ maximal diagonal and their rankScore, however best tags, i.e. the top 20% of rankScore values, tend to be clustered closely with a span of around 14.56 m long, as shown in Fig. 6. 4.2.
In [
According to [
Best quadruplets for positioning in the hall described in [
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We built an indoor environment classification method that can recognize a hall and a corridor with
98.57 % accuracy. As a comparison the method presented in [
We identified best classification tags’ quadruplets characteristics for both environments. Good tags’ quadruplets for classification correspond to an extend to the good tags for localization which confirms our solution can be use in pair with a traditional localization method, such as received RSS, with little additional deployment effort.
Our future work will be to expand the classification method to other environments and to test its performances with other type of signals such as WiFi as it is more widespread than RFID and thus requires less deployment effort. It will also be necessary to test our method in a localization scenario to confirm its value.