=Paper=
{{Paper
|id=Vol-2578/BMDA1
|storemode=property
|title=Dynamic Wi-Fi RSSI normalization in unmapped locations
|pdfUrl=https://ceur-ws.org/Vol-2578/BMDA1.pdf
|volume=Vol-2578
|authors=Hanna Kavalionak,Massimo Tosato,Paolo Barsocchi,Franco Maria Nardini
|dblpUrl=https://dblp.org/rec/conf/edbt/KavalionakTBN20
}}
==Dynamic Wi-Fi RSSI normalization in unmapped locations==
https://ceur-ws.org/Vol-2578/BMDA1.pdf
Dynamic Wi-Fi RSSI normalization in unmapped locations
Hanna Kavalionak Massimo Tosato
National Research Council of Italy Cloud4Wi
Pisa, Italy Pisa, Italy
hanna.kavalionak@isti.cnr.it mtosato@cloud4wi.com
Paolo Barsocchi Franco Maria Nardini
National Research Council of Italy National Research Council of Italy
Pisa, Italy Pisa, Italy
paolo.barsocchi@isti.cnr.it francomaria.nardini@isti.cnr.it
ABSTRACT of particular interest when the high localization precision is not
With the growing availability of open access WLAN networks, required.
we assisted to the increase of marketing services that are based There are a lot of commercial companies that are working
on the data collected from the WLAN access points. The identi- with the data of commercial venues in order to provide market-
fication of visitors of a commercial venue using WLAN data is ing products to the clients. These companies are buying probes
one of the issues to create successful marketing products. One of data from giant wireless infrastructure providers, such as Meraki,
the ways to separate visitors is to analyse the RSSI of the mobile Ruckus and Aerohive, and process this data into the marketing
devices signals coming to various access points at the venue. Nev- product. The marketing product motivating the paper originates
ertheless, the indoor signal distortion makes RSSI based methods from a company mission to help retailers in brick and mortars
unreliable. stores to build an omni-channel communication with their cus-
In this work we propose the algorithm for the WLAN based tomers. For example, one use case could be to trigger a specific
RSSI normalization in uncontrolled environments. Our approach marketing action on a defined behaviour such as a push up noti-
is based on the two steps, where at first based on the collected fication on the proprietary app when the customers enters the
data we detect the devices whose RSSI can be taken as a basic one. store or to send a discount or promotion email to a customer
At the second step the algorithm allows based on the previously who has just left the location for retaining. As many real world
detected basic RSSI to normalize the received signal from mobile scenarios, the raw data contains much ’noise’ and is in need of
devices. We provide the analysis of a real dataset of WLAN probes semantic attribution to be able to aim the right communication
collected in several real commercial venues in Italy. to the right target: the visitors.
As a matter of fact, one of the key aspect for this data pro-
cessing is actually to separate the visitors of the venue from the
1 INTRODUCTION passers-by, where the visitor is usually considered a person that
enters the venue and spends some time (and hopefully money)
Localization is becoming a more and more important feature inside. One of the ways to separate visitors from passers-by is by
in the mechanisms used for the location-based services and dif- analysing the Received Signal Strength Indicator (RSSI) of mobile
ferent location-based business models [13, 14]. One of the most WiFi devices carried by nearby persons. The RSSI indicates the
commonly used and precise mechanism for the localization, the power present in the received radio signal from remote mobile
Global Navigation Satellite Systems (GNSS), has strong limita- devices and typically decreases with the distance between the
tions in indoor environments. Hence, in order to organise the receiver and the transmitter of the signal. The RSSI parameter is
localization in indoor environments, researchers and industry very volatile and highly depends on the environment. The signal
approached the problem by using different signal technologies, in a propagation channel is affected by path loss and multi-path
such as Radio Frequency Identification tags, Ultra Wide-Band or effects, which result in RSSI variation and attenuation [3, 11, 12].
WLAN. This is an important factor especially for commercial venues,
While the technologies based on the Radio Frequency Tags in which the high fluctuation of people makes the problem of
and Bluetooth Low Energy show promising results in indoor correctly distinguish between visitors and passers-by based on
localization [2, 7], it is WLAN that is of our particular inter- RSSI even more challenging.
est. Indoor localization based on WLAN technologies does not Most of the existing WLAN solutions are based on the knowl-
show significant precision benefits in comparison with other edge of the access points (APs) position, network devices that
approaches. However, in the last decades the availability and allow other Wi-Fi devices to connect to the wired network [4, 6].
distribution of free internet zones in the cities and commercial Also, the typical solutions rely on the fixed indoor layout and do
venues have significantly increased. Therefore, the application not consider the people fluctuations [3].
of WLAN based localization does not require additional infras- In this paper, we focus on the characterization and possible
tructure deployment and can be based on the already existing adjustments of RSSI based indoor localization for WLAN com-
infrastructure. This characteristic makes this method to be one munication. More precisely, our contribution can be summarized
of the most appealing for small and medium size enterprises, also as follows. We propose an algorithm that dynamically adjusts
considering its flexibility and applicability. The approach can be the RSSI of the received probes from mobile devices. During the
adjustment phase the RSSI of probes of selected anchor devices
Copyright © 2020 for this paper by its author(s). Published in the Workshop Proceed- are used as a reference to evaluate the path loss in the propaga-
ings of the EDBT/ICDT 2020 Joint Conference (March 30-April 2, 2020, Copenhagen,
Denmark) on CEUR-WS.org. Use permitted under Creative Commons License At- tion channel. In general, an anchor device is (i)active during the
tribution 4.0 International (CC BY 4.0). closing hours of the venue, (ii)does not change its location, and
(iii)it is also present during the opening hours of the same day. the indoor environment and some controlled off-line measure-
We also describe the algorithm for the anchor devices selection. ments using the existing APs. By comparison, in the scenario
In our work we provide the analyses of a real dataset of WLAN described in our work we have no knowledge about the area and
probes collected in several real commercial venues in Italy. This no control over the APs.
dataset is provided by the company Cloud4Wi for research pur- One more aspect described in the literature is the possibility of
poses. We provide the evaluation results for the anchors distri- attacks in case of fingerprinting approach. For example the work
bution and presences for different types of commercial venues. of Richter et al.[10] discusses different attack types and compare
We also evaluate the correlation of RSSI of anchor devices on the positioning performance of RSS-based fingerprinting under these
number of incoming probes to an AP. attacks via simulations. The authors also present the simulator
The benefit of described approach is that the algorithm relies for realistic RSS predictions for the simulation environments.
only on the device probes received by AP and do not require Aboelnaga et al.[1] describe an algorithm to identify attacked APs
deployment of additional hardware. Moreover, the adjustment and make accurate localization in the presence of attacks. These
phase can be executed at runtime or offline. This important char- works are concentrated on the evaluation of different attacks
acteristics are essential to follow the wireless channel variance types on the performance of RSS. Moreover, in order to detect
during the day due for instance to the high fluctuation of people. and evaluate the attacks the authors rely on previously collected
While the described solution does not aim for a high precision fingerprints datasets.
localization, it can significantly improve the quality of marketing Our work differs from most of the state of the art because we
solutions proposed on the data processing market. do not improve the precision or reliability of the indoor localiza-
The remainder of this paper is organized as follows. Section tion methods. Instead, the main scope of our work is to provide
2 offers an overview of the related work. Section 3 provides the an approach that could, with minimum monetary investments,
definition of the problem statement, whereas Section 4 presents improve marketing solutions provided by companies based on
the algorithm for anchors selection and dynamic RSSI adjustment. the collected data. As we describe in Section3, we deal with en-
In Section 5 we evaluate the anchors selection algorithm based vironment layouts that are not known and not controlled. In
on the real WLAN data. Finally, we conclude the paper in Section our work we rely only on the sniffed data of probes from APs
6 by discussing the results and the future work. in a totally passive fashion. To decrease the impact of path loss
effect in propagation channel on the RSSI based localization we
propose to rely on a set of dynamically detected anchors. Mon-
2 RELATED WORK itoring the changes of RSSI of these anchors and applying the
Indoor localization is a mature research field. Nevertheless, the correction coefficient for the sniffed RSSI on APs allows to adjust
growing distribution of wireless infrastructure and open access the RSSI-based localization.
internet connections raises old and brings new challenges. Re-
cent overviews on technologies and techniques can be found on
existing surveys [8, 13, 14]. 3 PROBLEM STATEMENT
Most of the available literature in the field relies on some The wireless radio signal passing through propagation chan-
known data about indoor layout. This data can include the shape nel experiences path loss and multi-pass effects influencing on
of positioning measurement, points coordinates or fingerprints the RSSI sniffed by the receiver. These effects are caused by dif-
[4, 5, 8], maps of the area [2, 7, 8], some known scenario [7]. ferent factors like obstacles in the propagation channel, signal
For example the work of Nikoukar et al. [7] describes the study reflection on the walls and floors, etc. In order to estimate the
of low-energy Bluetooth advertisement channels. The authors distance between transmitter and receiver some applications rely
conduct extensive experiments in four different environments. on the strength value of the received signal RSSI. Hence, the
The work describes the study of the effect of the environment deformation of the signal passing through propagation channel
noise and interference on the signal propagation conditions. Our can significantly decrease the level of the provided service.
work is also connected with the studies of signal deformation in Regarding the importance of the signal deformation problem
a propagation channel. Nevertheless, the authors consider the for the distance measurement, various models have been pro-
controlled environment in their studies, while in our case the posed to compute the strength of the signal depending on the
setup layout and the indoor conditions are fully uncontrolled. distance [9]: (i) the free-space propagation model, (ii) the two-ray
Another work that studies the propagation channel is the work model and (iii) the log-normal shadowing model (LNSM). For
of R. Faragher and I. Papapanagiotou [2]. The authors provide a our application, these models usually do unrealistic assumptions
detailed study of Bluetooth Low Energy (BLE) fingerprinting. In or have too high requirements in terms of knowledge of the
their investigations the authors rely on the deployed network of layout. For example the free-space propagation model assumes
19 beacons. While the study provides the quantitative comparison the environment to be obstacle-free. A more promising models
of BLE technology with the WiFi one, it still relies on a controlled based on RSSI for the indoor localization is LNSM. However, it
network of hardware devices that has to be installed. heavily relies on the knowledge about the indoor environment.
The work of Shrestha et al. [11] describes an approach for in- This knowledge includes positions of APs, mapping of the area,
door localization with WLAN signal and unknown access point environment parameters like temperature, humidity, etc.
locations. The authors formulate the problem of WLAN position- These requirements are important for correct indoor local-
ing as a deconvolution problem and investigate three deconvolu- ization and cannot be ignored in case of precise modeling. Nev-
tion methods with different path loss models. The work describes ertheless, in real applications it is not always possible to have
the comparison of the proposed approach with the fingerprint- full knowledge about the environment and the company-owned
ing one. Nevertheless, the authors relies on two stages approach data usually is very limited. Moreover, the environmental factors
where on the first training stage is required the information about change over time.
In our work we consider, as an use case, a company that pro- (3) the probes of the candidate must be detected in both the
vides marketing products to commercial venues. The product of closing hours of the venues and the next consecutive open-
the company is based on the processing data of probes sniffed ing hours. With this requirement we eliminate those de-
from APs placed in some venues. As a medium size company it vices that are, for some reasons, active only during the
does not provide the services of hardware deployment and relies closing hours of the venue. For example it can be spe-
on the APs installation and services provided by big market play- cific security appliance that is activated when no physical
ers like Meraki, Ruckus and Aerohive. The company does not guardians are presented.
have the knowledge about the position of APs. Also, the com- At the end of the first step we have a list of anchor devices
pany does not have the knowledge about the indoor layout of the that can be used to adjust RSSI of the mobile devices during the
venue. The indoor layout of the venue is periodically changing. opening hours. Each anchor is assigned to some APs. The corre-
The venues tend to have fluctuation of visitors during the day sponding RSSI detected in the closing hours corresponds to the
and visitors inside the venue can significantly influence on the basic RSSI, i.e., RSSI 0 , between the anchor A and the correspond-
deformation of the signal in the propagation channel. ing AP. Hence, for the following opening hours of the venue this
At the same time, in order to provide services linked to its mis- RSSI 0 can be assigned as the basic RSSI for the adjustments for
sion, the company has to distinguish the probes of visitors of the the propagation channel between anchor A and the assigned AP.
venue from the probes of passers-by. This selection has to work Based on the detected anchors for the RSSI adjustments of
under constantly changing conditions inside the propagation mobile devices we describe the second step of the proposed al-
channel. To monitor the signal strength loss in the propagation gorithm, which we call dynamic RSSI adjustment. In order to
channel one could organise a message exchange between the simplify the explanation we consider an example with one AP
available APs in the venue. Nevertheless, as we mentioned before, and one anchor device (Figure 1):
the mid-size company does not deploy the hardware itself and
(1) During the opening hours, every time the anchor’s probe
hence cannot influence on the protocols of messages exchange
reaches the AP (RSSIa1 ), the adjustment coefficient α is
between APs.
re-computed by considering the RSSI of the new received
In this paper we propose an approach that relies only on the
probe:
sniffed data of probes from APs. The proposed approach is based RSSIa1
on the monitoring of the RSSI changes between transmitter and α=
RSSI 0
receiver in time. These changes correspond to the changes of
(2) The adjustment coefficient α is applied to all mobile de-
transmitting conditions in propagation channel between these
vices probes (RSSIm1 and RSSIm2 on the Figure 1) coming
two devices.
to this AP until the next anchor probe arrives:
One of the first questions to answer is how to detect the fixed
transmitters, anchors, based on the sniffed data of probes. The RSSI = α ∗ RSSIm 1
second question is how to adjust the RSSI of sniffed probes from
This algorithm permits to cope with the unavoidable channel
visitors mobile devices in order to level out the influence of side
variation estimating the α parameter. Indeed, α = 1 means that
factors, like people fluctuation, on the visitor/passer-by detection.
there are no channel variation, α < 1 means that the RSSI value
We acknowledge that the proposed solution cannot be applied
decreases with respect to the adjustment phase, or increases
in cases when high precision is required. Nevertheless, we believe
when α > 1, but in the last two cases the channel has changed.
the proposed adjustment can improve the existing marketing
products without additional costs for hardware deployment. No anchors detected. An additional situation we would like to
discuss is the case when there are no detected anchor devices
during the opening hours of the venue. For example this can
4 ALGORITHM AND MODEL happen in case of very small venues or in case of possible black
outs, or for some other reasons that are difficult to estimate.
The algorithm for the dynamic adjustment of RSSI is composed by Every time the algorithm does the calculation of α for AP
two steps, which can be broadly summarized as the following. The based on the signals received from the anchor, it can save this
first step is dedicated to detecting the devices that can possibly value together with the current number of incoming probes to
play the role of anchors. The second step applies the anchors this AP. In cases when no anchors are detected, the algorithm
RSSI data to adjust the RSSI data from the mobile devices. can apply the α estimation based on the current level of incoming
In order to be selected as an anchor, a candidate device has to probes and the α versus number of probes collected statistics. We
meet the following requirements: leave this improvement for our future work investigations.
(1) the probes of the candidate device have to be presented 5 EVALUATION
in the closing hours of the venue. We assume in these We have used 2.4 GBs of textual data representing a month (May
hours there is minimum additional noise in the area of 2018) of raw data from 6 different locations in Italy. This data has
investigation. In this case we can measure the basic level been collected by Cloud4Wi1 a SaaS company providing behav-
of RSSI for the anchor device. ioral analytic and an omni-channel communication with their
(2) the standard deviation of RSSI probes for the anchor can- customers to retailers worldwide. Each line, representing a single
didate in the closing hours of the venue has to be low. We request-to-send/clear-to-send (RTS/CTS) handshaking exchange
argue that a low standard deviation relates to those devices between a device (which has the Wi-Fi enabled) and an AP in a
that have a fixed position over time. These devices can location, contains information about the time of interaction, the
be represented by printers, TVs or some venue security access point id, the device id and the RSSI of the signal.
appliances.
1 https://cloud4wi.com/
Closing hours
Ancor Mobile 1 Mobile 2 AP
detection)
(Ancor
RSSI_0
RSSI_0
RSSI_a1
a=RSSI_a1/RSSI_0
RSSI_m1
RSSI=a*RSSI_m1
Opening hours
adjustment)
RSSI_a1
(RSSI
a=RSSI_a1/RSSI_0
RSSI_m2
RSSI=a*RSSI_m2
RSSI_a1
a=RSSI_a1/RSSI_0
Figure 1: An example of dynamic RSSI adjustment
5.1 Evaluation setup selected time interval also includes the preparation time
In order to understand the possibility of applying the described for the staff and security checking period after a venue
method, we provide an analysis of different types of venues to closing for clients.
see the statistic on the perspective anchor devices. We arbitrarily The parameters described above are derived empirically and
separated the locations in groups to see if any difference between are subject for evaluation and studies in a future work.
the groupings could have helped in better understanding the
context of the analysis. The locations can be split on the basis of Presences of anchor devices
their size (both in traffic and in meters) and of their positioning. Based on the described constrains we have evaluated the pres-
The two categories that we decided to implement are: ences of anchor candidates for different categories of locations
(1) Single standing city venues. The venues that are located in Figure 2.
more inhabited areas such as a city centre. In the following As we can see in all the venues there are devices that can be
experiments we address these locations as A, B, C. Venues exploit as anchors. At the same time we can see that some venues
A and C are located in seasonal touristic cities. Venue B have much more anchors then others. We explain this by the
is a city shop located close to the factory building. particularity of the location of the venue. For example, the venues
(2) Commercial centers. The venues are located in dedicated A and C are placed in the city center of summer touristic cities
shopping areas such as malls or shopping villages. In the (Figure 2a). Since we consider the month of May, which is just
following experiments we address these locations as D, E, before the start of summer season, we can see the lack of available
F. anchors. At the same time, the venue B is sharing the location
with the factory and so we can see an high presence of available
5.2 Data preparation and analyses anchor candidates. Figure 2b shows the evaluation of the number
As we already described in Section 4 in order to select the anchor of available anchor candidates for the venues located in the places
devices between the variety of available probes we apply the such as malls or shopping villages. As we can see all three venues
following numerical constrains: shows availability of the anchor candidates. According to the
• The anchor candidate should have at least 4 probes during processed data, the venue F has higher availability of anchors
the closing hours. As closing hours of the venue we have in comparison with other venues. It can be explained by the
considered deep night hours from 00 : 00AM to 04 : 00AM. particularity of the specific area organization (security devices,
Since all the locations are placed in the same country, Italy, etc.).
these conditions are applied to all of them.
• In order to be sure the candidate device is not changing Distribution of anchors per APs
its position over time we fix its standard deviation of RSSI In order to evaluate the distribution of the anchors inside the
to be less then 3. Empirically we found that the maximum area we have computed the number of available anchors per
of 3 − 4% standard deviation from the RSSI allows to iden- APs deployed in the venue. The results of the evaluation are
tify good anchor candidates. An extensive study of this presented in Figure 3. Different dots style and color correspond
parameter is out of the scope of this paper, and we leave to the different APs statistic.
such study for future work. As we can see on the Figure 3 and as we have already men-
• We control the presence of anchor candidates during the tioned on the previous Figure 2 the venues B, D and F have
opening hours of the same day. As opening hours for the significantly higher the number of available anchor candidates.
venue we have considered the time interval between 8AM This can be also explained by the number of available APs in-
and 10PM. We have preferred to be more inclusive for side the location. The number of APs can be used as indirect
opening hours interval in order to evaluate the maximum characterization for the venue size. An high number of APs is
possible anchor candidates. Nevertheless, the real opening also connected with heterogeneity of the anchor candidates pres-
hours can be different from the one mentioned here. The ences. For example on Figure 3b we can see that some of the APs
350 200 D
175 E
300 F
Number of anchor devices
Number of anchor devices
150
250
A 125
200
B 100
150 C
75
100
50
50 25
0 0
May 01 May 08 May 15 May 22 May 29 May 01 May 08 May 15 May 22 May 29
Date Date
(a) Single standing city venue. (b) Commercial centers.
Figure 2: Number of available anchor devices for two types of venue location.
60 60 60
50 50 50
Number of anchor devices
Number of anchor devices
Number of anchor devices
40 40 40
30 30 30
20 20 20
10 10 10
0 0 0
May 01 May 08 May 15 May 22 May 29 May 01 May 08 May 15 May 22 May 29 May 01 May 08 May 15 May 22 May 29
(a) Venue A (b) Venue B (c) Venue C
60 60 60
50 50 50
Number of anchor devices
Number of anchor devices
Number of anchor devices
40 40 40
30 30 30
20 20 20
10 10 10
0 0 0
May 01 May 08 May 15 May 22 May 29 May 01 May 08 May 15 May 22 May 29 May 01 May 08 May 15 May 22 May 29
(d) Venue D (e) Venue E (f) Venue F
Figure 3: The distribution of the anchor devices between available APs in the venues.
have a much lower number of available anchors in comparison 4). The Figure 4 shows some randomly selected days and APs
with the rest of the APs. By comparison, in Figure 3f there are for the evaluation. For the sake of presentation we selected the
some APs that show an higher anchors availability then the rest. anchors with the higher number of available probes during the
The rest of the venues with low number of APs show a more day.
uniform distribution of anchors. The heterogeneity of the APs The results show that the anchor candidates, as expected, have
distribution can correlate with the size of the venues. When the a relatively stable RSSI for the closing hours of the venue and
size of the venue is relatively small the available APs cover the show an high deviation during the venue opening hours. The
most important zones. Instead in case of big size venue and high difference in RSSI measured in closing and opening hours is
number of APs its distribution can be less uniform. Some APs higher than 50% and goes more than 60% in some cases (Figure
can be placed behind obstacle or in less popular spots of the 4h) The RSSI measured on the APs reacts on the changes in the
commercial venue. propagation channel, for example due to the clients presences.
This is important to notice since our approach is based on this
Signal strength versus number of probes assumption.
In order to evaluate the impact of clients fluctuation on the Figure 5 shows the statistic for number of incoming probes
transmitter-receiver propagation channel we have evaluated the to different APs. The APs on the figures are the same as the
RSSI changes during the day for some venues/APs/anchors(Figure ones presented on the Figure 4. This choice is done for easing
(a) Venue A. AP 88:15:44:bc:63:50 (b) Venue A. AP 88:15:44:bc:63:50 (c) Venue A. AP 88:15:44:bc:63:50
(d) Venue B. AP 88:15:44:a9:0a:85 (e) Venue B. AP 88:15:44:a9:0a:85 (f) Venue B. AP 88:15:44:a9:0a:85
(g) Venue D. AP 0c:8d:db:93:18:9b (h) Venue D. AP 0c:8d:db:93:27:82 (i) Venue D. AP 0c:8d:db:93:26:d4
Figure 4: Signal strength RSSI of anchors versus number of probes per AP. 24 hours statistics.
the comparison between the statistics for the same date and AP. dataset shows the availability of anchor devices in all considered
In fact by comparing both sets of figures we can see that the locations. The presence of anchor devices is homogeneous for
variation of RSSI measured for anchors on the AP (Figure 3) most of the available APs. The results of data evaluation confirm
correlates with the variation of the number of incoming probes that the anchor devices present a low deviation for RSSI in closed
to this AP. venue hours and an high deviation in opening hours. This con-
This is especially noticeable when compared Figures 4a,4b,4c firms the impact the people fluctuation does on the propagation
with the corresponding Figures 5a,5b,5c. The shape of the RSSI channel properties.
variation is following the shape of the incoming probes to the This work is our first attempt for the dynamic adjustment
same AP. An high number incoming probes to the AP indirectly of RSSI measures in fully uncontrolled environment in case of
indicates an increase of the number of clients in the area. In turn, WLAN based localization. The described evaluation is based on
an high number of clients in the area increases the probability the empirically derived parameters. These parameters are subject
that some of them are positioned between the transmitter and for deeper evaluation and studies in the future work.
receiver devices, which leads to the variation of the incoming As future work, we plan to deploy a controlled test environ-
RSSI. ment where we can evaluate the described approach and test the
possible variation for the parameters. We also plan to evaluate
6 CONCLUSION AND FUTURE WORK the possibility to introduce the additional small devices that could
interact with available APs. We expect such kind of devices could
In this work we propose an algorithm for dynamic adjustment of
significantly increase the effectiveness of the proposed approach
RSSI measured on APs. The proposed approach is based on two
and also can be important in case of lack of available anchor
steps. The first step selects the anchor devices, that are used as a
candidates.
reference point for RSSI. The second step is an actual dynamic
adjustment of measured RSSI values of mobile devices. We have
evaluated 2.4 GBs of textual data representing a month (May 7 ACKNOWLEDGEMENTS
2018) of raw data from 6 different locations in Italy. This dataset This work was conducted under the project "Wi-Fi Analytics
has been collected by Cloud4Wi company. The analysis of this for Marketing Optimization Strategies (WAMOS)" with partial
200 225
88:15:44:bc:63:50 88:15:44:bc:63:50 88:15:44:bc:63:50
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00:00 03:00 06:00 09:00 12:00 15:00 18:00 21:00 00:00 03:00 06:00 09:00 12:00 15:00 18:00 21:00 00:00 03:00 06:00 09:00 12:00 15:00 18:00 21:00
07-May 29-May 30-May
2018-05-07 2018-05-29 2018-05-30
(a) Venue A. AP 88:15:44:bc:63:50 (b) Venue A. AP 88:15:44:bc:63:50 (c) Venue A. AP 88:15:44:bc:63:50
900 800
88:15:44:a9:0a:85 88:15:44:a9:0a:85 88:15:44:a9:0a:85
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01-May 03-May 12-May
2018-05-01 2018-05-03 2018-05-12
(d) Venue B. AP 88:15:44:a9:0a:85 (e) Venue B. AP 88:15:44:a9:0a:85 (f) Venue B. AP 88:15:44:a9:0a:85
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0c:8d:db:93:18:9b 0c:8d:db:93:27:82 0c:8d:db:93:26:d4
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08-May 12-May 31-May
2018-05-08 2018-05-12 2018-05-31
(g) Venue D. AP 0c:8d:db:93:18:9b (h) Venue D. AP 0c:8d:db:93:27:82 (i) Venue D. AP 0c:8d:db:93:26:d4
Figure 5: Number of received probes per AP. 24 hours statistics.
financial support of Tuscany region and in collaboration and [8] Henri Nurminen, Marzieh Dashti, and Robert Piché. 2017. A Survey on Wire-
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