=Paper=
{{Paper
|id=Vol-1982/paper4
|storemode=property
|title=Localization and Monitoring System based on BLE Fingerprint Method
|pdfUrl=https://ceur-ws.org/Vol-1982/paper4.pdf
|volume=Vol-1982
|authors=Annalisa Longo,Maria Rizzi,Davide Amendolare,Sante Stanisci,Ruggero Russo,Gianpaolo Cice,Matteo D'Aloia
|dblpUrl=https://dblp.org/rec/conf/aiia/LongoRASRCD17
}}
==Localization and Monitoring System based on BLE Fingerprint Method==
https://ceur-ws.org/Vol-1982/paper4.pdf
Localization and monitoring system based on BLE
fingerprint method
Annalisa Longo1, Maria Rizzi2, Davide Amendolare1, Sante Stanisci1, Ruggero
Russo1, Gianpaolo Cice1 and Matteo D’Aloia1
1
MASVIS SRL, Conversano, Italy
matteo.daloia@masvis.com
2
Politecnico di Bari - Dipartimento di Ingegneria Elettrica e dell’Informazione, Bari, Italy
maria.rizzi@poliba.it
Abstract. Among techniques designated for indoor localization, wireless fingerprinting is the
most emerging because of the widespread deployment of wireless networks. Moreover, position-
ing methods based on received signal strength indicator fingerprint are attractive for their accu-
racy and independence from the radio propagation model. This paper describes an implementa-
tion of Bluetooth Low Energy positioning method based on fingerprint technique, according to
Wi-Fi localization techniques. Adopting the received signal strength indicator and an accurate
model, a localization system with a good accuracy is obtained. The method feature of not limiting
the freedom and privacy of users, makes it advisable for elderly behavior monitoring.
Keywords: BLE, indoor localization, Wi-Fi positioning, fingerprint, elderly, monitoring
1 Introduction
Advances in medical diagnostics and treatments have produced lengthening life ex-
pectancy and, consequently, rise in elderly people in many nations. In fact, health tech-
nology contributes both to disease prevention and to limit the decay of a person func-
tions which corresponds to increase in life expectancy [1-6]. Many elderly live alone in
their home because they are creatures of habit. Unfortunately, their deteriorated physi-
cal functions severely reduce their mobility and their self-sufficiency. Therefore, el-
derly are prone to abrupt health problems such as falls, sudden illness, loss of con-
sciousness, etc. Modern advances in the fields of information technology, communica-
tion/sensor networks and electronic devices are enabling the development of techno-
logical solutions for elderly taking care [7-8]. Many efforts have attempted to monitor
the location where old people live by applying intelligent technologies, such as image
surveillance, sound detection or vibration sensing for the identification of fall or trip
hazards [9, 10]. For this reason, indoor positioning systems have rapidly developed by
using new technologies and methods. One of the most popular positioning technologies
based on 2D modeling is the fingerprint on Wi-Fi which can be used in both outdoor
and indoor environment. Because of Wi-Fi localization system need at least three Wi-
D. Impedovo and G. Pirlo (Eds.), Workshop on Artificial Intelligence with Application in Health, Bari, Italy, November 14, 2017.
Copyright held by the authors.
Fi routers in the monitoring area, it is not advisable for elderly monitoring in their home.
Compared with Wi-Fi localization system, Bluetooth Low Energy (BLE) fingerprinting
is a low-cost technology in fact it need only to install beacons in the monitoring area
that are battery operated cheap devices. Moreover, these systems are characterized by
high precision but the algorithm complexity and the computational consumption is rel-
atively high [11]. Like all indoor positioning systems, fingerprint technology is influ-
enced by the environment such as multipath effects, number of access (AP or BC) and
reference points (RP), presence of mobile devices, etc.
BLE fingerprinting is characterized by an off-line phase followed by an on-line phase
[12]. During the off-line stage fingerprint data are collected to build a model; in fact the
location dependent characteristics of a signal acquired at known locations are stored in
a database or radio map. For the model development, both accurate and empirical mod-
els can be used [11], [13]. Adopting empirical modeling, the measure of the signal
strength received from different beacons (BC) at the location of every RP is performed
and stored in the database with the information of RP location. This modeling method
has a high level of accuracy even if the workload is, often, large for the need of many
data samples. For accurate modeling, the accurate position of BCs is necessary. More-
over, for the radio map generation, the signal strength of several important positions is
only needed. Because of complexity and limitations of indoor environment which
causes scattering, reflection, and refraction of the propagated RF signal, the main task
of accurate modeling is to define a channel propagation model able to describe indoor
signal fading properly. To remove invalid samples and improve accuracy of the posi-
tioning method, the fingerprint database has to be filtered since the acquired signal
strength is corrupted by noise generated by the indoor environment. Both deterministic
denoising and probabilistic denoising methods can be used.
In the online phase, collected signal strengths are compared to the signal strength at the
user location to estimate the user position adopting deterministic or probabilistic posi-
tioning algorithms. These type of procedures differ mostly because deterministic algo-
rithms match the signal strength of corresponding fingerprint data by using the posi-
tioning algorithm to obtain the final position of the user, while probabilistic algorithms
store the probability distribution of the signal strength during a certain time in the fin-
gerprint database and then the probability position of the user location is evaluated by
the Bayesian theory system.
In this paper, a monitoring system for elderly people based on BLE technology is pre-
sented. The method performs indoor positioning adopting Wi-Fi fingerprinting tech-
nique based on Received Signal Strength Indicator (RSSI). After a brief description of
the BLE technology, the implemented system for the indoor localization is detailed.
The method performance is evaluated and some conclusions are drawn out.
2 Bluetooth Low Energy technology
Bluetooth low energy technology combines a standardized technology designed for
ultra-low-power batteries and a new sensor-based data collection framework. It
boomed in the mobile device market and uses devices working at 2.4 GHz and within
the Industrial, Scientific and Medical (ISM) band. BLE technology employs a variable
connection interval that can be set from a few milliseconds to several seconds depend-
ing on the application. In addition, because it features a very rapid connection, BLE
technology can normally be in a "not connected" state (saving power) where the two
ends of a link are aware of each other, but only link up when absolutely necessary and
then for as short a time as possible. Benefits of BLE technology are [14]:
• Low power consumption: nowadays, in order to reduce the cost of system infra-
structure, the most important requirement for all electronic systems is a low power
consumption;
• Indoor positioning: BLE technology is designed specifically for this purpose;
• Supported by smartphones: this feature is very important in view of trends in the
mobile telephone systems;
• Very long life battery: this is a very important aspect for all portable systems. A
long life battery enables a long life system operation and, consequently, the possi-
bility to replace batteries after a few months of use. As a result, system maintenance
costs decrease;
• Low cost: cost control is one of the requirement/constraint in designing an elec-
tronic system. The adoption of low-cost hardware guarantees cost reduction for sys-
tem implementation.
3 Adopted method and measurements
The purpose of the implemented system is to localize the positions of one receiver
(i.e. smartphone) in the physical area of interest using the BLE technology. In BLE
fingerprint technique, the definition of a radio frequency map is necessary which com-
bines geographical coordinates (2-Dimension Cartesian Space) and RSSI values re-
ceived by the tag and transmitted by several beacons (BC). The location of beacons is
a key point because it must be chosen in such a way that at any time the tag is within
the radio range of at least one beacon. The generic RSSI vector received by a receiver
is denoted as t = (t1, t2,…, tn), where tj, (1≤j≤n) denotes the RSSI value from the jth
beacon and n is the number of beacons in the area of interest. Adopting m RPs in the
offline phase, the RSSI vector received at the ith RP (1≤i≤m) is denoted as si, where si
= (si1, si2,…sin).
The implemented method is composed of an off-line phase, in which the fingerprint
data base is constructed adopting an accurate model, and an on-line phase in which a
deterministic positioning algorithm is implemented to localize the receiver inside the
area under test.
In the implemented system, four beacons and a mobile receiver are used inside an in-
door environment of about 133 m2 (fig.1).
In order to reduce the effort dedicated to off-line measurements, a virtual fingerprint
database is calculated using the Multi-Wall Multi-Floor indoor propagation model
whose effectiveness was investigated [15]. The RSSI database is composed of 250000
elements (m=250000) (fig 1).
Fig.1 RadioMap of beacons positionated inside the indoor area under study
To simulate the RSSI signals measured by the receiver if it is at the jth RP, the sj vector
was corrupted by random values (noise) which are different for all its components.
As previously mentioned, the signals read by the receiver is called t, where t = (t1, t2,…,
tn) and ti, = sij +cij.
This assumption is justified because errors can corrupt the RSSI values measured by
the receiver. The Mersenne Twister algorithm is adopted for the generation of random
values. In particular,
cij = (b-a) × rand (1) + a (1)
where a and b are the boundary of the signal corrupted by the error
a = sij-MaxErr × MSV (2)
b = sij+ MaxErr × MSV (3)
and Maximum Scale Value(MSV) is the maximum signal value assumed to be -25dB.
MaxErr is in the range [0,1], assuming that 100% is the maximum read error that can
be done by the receiver.
For software implementation (online phase), all the sj points are evaluated with the aim
to determine if the sj point is a probably matching point or not.
The criteria adopted is based on the successful condition that each value of t is in sj
range that is for each sj, ti has to be in sij range. The range sij is a window of size equal
to 50% of the ti value and centered around si value.
For j=1..m , sj is a matching point for t if:
𝑠1𝑗 − 0.5 × 𝑡1 ≤ 𝑡1 ≤ 𝑠1𝑗 + 0.5 × 𝑡1
𝑠2𝑗 − 0.5 × 𝑡2 ≤ 𝑡2 ≤ 𝑠2𝑗 + 0.5 × 𝑡2
…
{𝑠𝑛𝑗 − 0.5 × 𝑡𝑛 ≤ 𝑡𝑛 ≤ 𝑠𝑛𝑗 + 0.5 × 𝑡𝑛
The implemented procedure localizes all the s points inside the area under test which
should represent the receiver position.
In fig.2, the receiver real position is indicated by a circle while the gray areas represent
the receiver localizations obtained by the implemented procedure. In the figure is easily
identify k cluster of probable points.
Fig.2 Coarse procedure outputs
The second step consists to evaluating the numerical consistence of each cluster. Only
the cluster with the maximum point consistence is considered in the next step.
The third step is the evaluation of the cluster centroid.
For an accurate positioning of the receiver, the k-means clustering algorithm with the
Euclidean distance measurement technique, is adopted. The used procedure partitions
a set of n objects into k clusters so that the resulting intra-cluster similarity is high but
the inter-cluster similarity is low. The procedure defines one centroid for each cluster.
Generally speaking, a centroid is an artificial point in the space of records which repre-
sents an average location of the particular cluster. Its coordinates are averages of attrib-
ute values of all examples that belong to the cluster.
The k-means algorithm uses an heuristic method to find centroid seeds for k-means
clustering. Assuming k different clusters, the algorithm chooses the seeds as follows:
1. It selects an observation uniformly at random from the data set, X. The chosen
observation is the first centroid, and is denoted with 𝑐1 .
2. It computes the distances from each observation to 𝑐1 and denotes the distance be-
tween 𝑐𝑗 and the observation m with d(𝑥𝑚 , 𝑐𝑗 ).
3. It selects the next centroid 𝑐2 at random from X with probability
𝑑2 (𝑥𝑚 , 𝑐1 )
∑𝑛𝑗=1 𝑑2 (𝑥𝑗 , 𝑐1 )
4. For the choice of center j, the procedure:
a) Computes the distances from each observation to each centroid and as-
signs each observation to its closest centroid.
b) Selects centroid j at random from X with probability
𝑑2 (𝑥𝑚 , 𝑐𝑝 )
∑{ℎ,𝑥ℎ ∈ 𝐶𝑝 } 𝑑2 (𝑥ℎ , 𝑐𝑝 )
for m = 1, …, n and p = 1, …, j-1
where 𝐶𝑝 is the set of all observations closest to centroid 𝑐𝑝 and 𝑥𝑚 be-
longs to 𝐶𝑝 . That is, the algorithm selects each subsequent center with a
probability proportional to the distance from itself to the closest center
already chosen.
5. Repeat step 4 until k centroids are chosen.
The estimated localization point is considered the cluster centroid.
For the evaluation of algorithm robustness towards noise (such as effects of building
layout, construction material, moving objects, reflecting surfaces, temperature changes
day, electromagnetic field interference, etc.), several simulations were carried. Assum-
ing a transmission power level of each beacon equal to -25dBm, average error distance
values less than 1,4m are obtained for noise values not exceeding 20% of maximum
scale value (MaxErr=0.2) (fig.3).
a)
b)
c)
Circle = real position
Square= simulated position
Fig.3 Results obtained with a noise level of 5% (a), 20% (b) and 40% (c) of the maximum
scale value (MaxErr=0.05, 0.2, 0.4).
4 Conclusion
In this paper, a BLE fingerprint method using Received Signal Strength Indicator
for indoor localization is presented. In the off-line phase an accurate model is adopted
for the radio map construction, in the on-line phase a deterministic positioning algo-
rithm is implemented to localize the receiver inside the test area. The obtained results
show the method validity.
ACKNOWLEDGMENT
The work has been developed within the framework of Masvis’s Internal Research
Project titled: “Sistema di localizzazione indoor, in tecnologia BLE, basato su misure
di RSSI con beacon mobile e stazioni di rilevamento fisse”.
References
1. Rizzi M., D’Aloia M., Guaragnella C., Castagnolo B., Health Care Improvement: Compar-
ative Analysis of Two CAD Systems in Mammographic Screening, IEEE Trans. Syst., Man,
Cybern. A, Syst., Humans 42, 1385-1395 (2012)
2. Yuan Y., Giger M.L., Li H., Bhooshan N., Sennett C.A., Correlative Analysis of FFDM and
DCE-MRI for Improved Breast CADx, J. Med. Biol. Eng., 32, 42-50 (2012).
3. Rizzi M., D’Aloia M., Computer Aided System for Breast Cancer Diagnosis, Biomed. Eng-
App. Bas C. 26,1450033 (2014)
4. Rizzi M., D’Aloia M., Cice G., Computer aided evaluation (CAE) of morphologic changes
in pigmented skin lesions, Lect. Notes Comput. Sc. (including subseries Lecture Notes in
Artificial Intelligence and Lecture Notes in Bioinformatics) 9281, 250-257 (2015)
5. Pirlo G., Impedovo D., On the measurement of local stability of handwriting: An application
to static signature verification, 2010 IEEE Workshop on Biometric Measurements and Sys-
tems for Security and Medical Applications, 41-44, (2010)
6. Rizzi M., D’Aloia M., Castagnolo B., Semiconductor detectors and principles of radiation-
matter interaction. Journal of Applied Sciences 10, 3141-3155 (2010)
7. Hsu C.C., Chen J.H., A Novel Sensor-Assisted RFID-Based Indoor Tracking System for the
Elderly Living Alone, Sensors 11, 10094-10113 (2012)
8. Impedovo D., Pirlo G., Updating Knowledge in Feedback-Based Multi-classifier Systems,
2011 Int. Conference on Document Analysis and Recognition, Beijing, 227-231 (2011)
9. Bakar U.A.B.U.A., Ghayvat H., Hasanm S.F., Mukhopadhyay S.C., Activity and Anomaly
Detection in Smart Home: A Survey. In: Mukhopadhyay S. (eds) Next Generation Sensors
and Systems. Smart Sensors, Measurement and Instrumentation 16. Springer, Cham (2016)
10. D'Aloia M., Cortone F., Cice G., Russo R., Rizzi M., Longo A., Improving energy efficiency
in building system using a novel people localization system, 2016 IEEE Workshop on En-
vironmental, Energy, and Structural Monitoring Systems, EESMS 2016; Bari (Italy), 13-14
June (2016)
11. Xia S., Liu Y., Yuan G., Zhu M., Wang Z., Indoor fingerprint positioning based on Wi-Fi:
an overview, ISPRS Int. J. Geo-Inf. 6, 135, (2017)
12. Suining H., Gary Chan S. H., Wi-Fi Fingerprint-Based Indoor Positioning: Recent Advances
and Comparisons, IEEE Commun. Surv Tut, 18, 466-490 (2016)
13. Swangmuang N.; Krishnamurthy P.V., On clustering RSS fingerprints for improving scala-
bility of performance prediction of indoor positioning systems, 1st ACM Int. Work. on Mo-
bile Entity Localization and Tracking in GPS-Less Environments, San Francisco, (USA),
61-66 (2008)
14. Lee J.S., Dong M. F., Sun Y.H., A preliminary study of low power wireless technologies:
ZigBee and Bluetooth Low Energy, 10th IEEE Conf. on. Industrial Electronics and Applica-
tions (ICIEA), (2015)
15. Caso G., De Nardis L., On the Applicability of Multi-wall Multi-floor Propagation Models
to WiFi Fingerprinting Indoor Positioning, Future Access Enablers for Ubiquitous and In-
telligent Infrastructures: Lecture Notes of the Institute for Computer Sciences, Social Infor-
matics and Telecommunications Engineering, Springer, (2015)