Geolocalization in Smart Environment

Joseph Merhej

, Jacques Demerjian

, Karla Fares

, Jacques Bou Abdo

and

Abdallah Makhoul

LARIFA-EDST Laboratory, Faculty of Sciences, Lebanese University, Fanar, Lebanon

Department of Computer Science, Notre Dame University, Deir el Qamar, Lebanon

FEMTO-ST Institute, University of Bourgogne Franche-Comté, Belfort, France

Keywords: Wireless Sensor Network, Collaborative Wireless Sensor Network, Localization, RSSI, Ultrasound.

Abstract: Nowadays, wireless indoor positioning systems have become very familiar, and widespread all over the world.

They are successfully used in many applications including tracking objects e.g. Firemen who usually face

life-threatening situations. Indoor positioning systems become critically convenient in such scenarios. This

paper deals with the tracking of a group of firemen during their mission in order to have a real-time visibility

of their coordinates. These firemen are armed by smart sensors and are, at the same time, active in a smart

environment containing referenced nodes. This paper will propose two approaches: ‘Centralized Emission’,

and ‘Broadcast Emission’ and will describe the proposed method to calculate the firemen’s coordinates.

1 INTRODUCTION

Sensors have recently played an important role in

monitoring objects in a specific environment. These

sensors are small in size, have low power

consumption, and can be easily integrated into a

network to create a Sensor Network. Wireless Sensor

Network (WSN), a set of distributed devices / sensors

used to monitor the environment, also uses a gateway

providing wireless connection. By enhancing

technologies, sensors will have the ability to

cooperate and exchange information between each

other, so that WSN becomes Collaborative Wireless

Sensor Network (CWSN). Wearable Sensor Network

is a special case of CWSN, where the sensors are

mounted on/worn by individuals.

Nodes cooperate to solve the problem of tracking

objects and people. Many techniques and methods are

used to compute the position of an object in its

environment. This process is called “localization”.

This paper concentrates on localization in WSN

and CWSN. The localization of sensors in a

WSN/CWSN faces many problems such as the

complexity/topology of the network itself, the signal

propagation, the reflection problems, the obstacles,

etc.

The localization problem has been studied

thoroughly in literature and many algorithms were

proposed to resolve the complexity of the localization

problem.

Our study on WSN in the localization field, can

be used in different scenarios to track the localization

of people or devices (firemen, policemen, soldiers,

vehicles, etc.) during their works. Saving lost person

or device requires locating him first and this is the aim

of this work. Our study will treat the case of localizing

firemen moving in an indoor environments, with

emergent obstacles i.e. The obstacles’ positions are

predefined. Many problems faces our study to

calculate the coordinates of each node/device in such

a mobile, distributed, dynamic, and complex network.

Because our study deals with indoor localization

environment, some existing techniques like Global

Positioning System (GPS) are not suitable, and that is

why some other techniques will be used as described

in this paper.

Our approach proposes ‘Centralized Emission’

and ‘Broadcast Emission’ used to calculate the

coordinates of the mobile nodes according to a

beacon (fixed node). In the ‘Centralized Emission’,

each node sends a request to its corresponding beacon

which computes the coordinates of the emitter node

and sends the computed coordinates to a controller

beacon. On the other hand, in the ‘Broadcast

Emission’, each node sends its request to the beacon

and all its neighbors existing in its range. Once the

request is received, the beacon computes the

coordinates of each node and sends it to the controller

beacon.

The remaining of this paper is organized as fol-

108

Merhej, J., Demerjian, J., Fares, K., Abdo, J. and Makhoul, A.

Geolocalization in Smart Environment.

DOI: 10.5220/0007472601080115

In Proceedings of the 8th International Conference on Sensor Networks (SENSORNETS 2019), pages 108-115

ISBN: 978-989-758-355-1

lows: Section II presents the computing techniques

and methods used to localize a target. Section III

illustrates the state-of-the-art proposals and describes

the existing systems for Indoor Positioning Systems

(IPS), their advantages, and disadvantages. Section

IV discusses our approach to estimate the localization

in a Wearable Sensor Network. Finally, Section V

summarizes the paper.

2 LOCALIZATION METHODS

AND TECHNIQUES

In this section, we describe various measurement

methods and localization techniques used by existing

CWSN indoor localization algorithms (Zhang, 2010).

2.1 Measurement Methods

2.1.1 Time-Of-Arrival (TOA)

The distance between the transmitting node and the

receiving one is deduced from the transmission time

delay and the corresponding speed of signals. The

distance can be calculated as follows

R=Time*Speed

Where R is the distance between the sender and the

receiver, Speed is the signal’s traveling speed and

Time is the amount of time spent by the signal

traveling from the sender to the receiver. A

combination of TOA and Ultra Wide Band (UWB)

has been used to guarantee a higher precision (Falsi,

2006), because TOA technique has a restrict

requirement of synchronization, this inefficiency can

be resolved by UWB that uses short pulse duration to

filter out the signals caused by reflections (Cheong,

2005).

2.1.2 Time-Difference-Of-Arrival (TDOA)

This method uses two kinds of radio transmitting

signals. The time difference between these two kinds

of signals is used to reconstruct the transmitting

node’s position. The equation is:



Where C1 is the speed of one kind of radio signals,

C2 the speed of another kind of radio signals, t1 and

t2 are the time for these two signals to travel from one

node to another, R is the distance between sender and

receiver. The author of (Takabayashi, 2008) uses the

Time Difference of Arrival (TDOA) method with

Extended Kalman Filter (EKF), and this approach is

suitable in environments where the number of

beacons is not sufficient.

2.1.3 Round Trip Time (RTT)

This method solves the problem of synchronization

incurred by the use of TOA method (Mailaender,

2007). The equation is:

*)( speedt





is the time needed for a signal to travel from one

node to another and back again, ∆t is the time delay

required by the hardware to operate at the receiving

node, while speed is the speed of the transmitting

signal.

2.1.4 Angle-Of-Arrival (AOA)

The authors of (Linde, 2006) (Niculescu, 2003)

determine the direction of propagation of a radio-

frequency by measuring the TDOA at individual

elements of the array antennas. Consequently, the

AOA can be calculated. Therefore, no time

synchronization between nodes is required.

2.2 Localization Techniques

2.2.1 Trilateration

It uses three fixed non-collinear reference node to

calculate the position of a target node (in 2D) as

shown in Fig 1. Authors of (Han, 2007) confirmed

that trilateration can best demonstrate its advantages

when the three reference nodes are deployed as

equilateral triangle.













yyxx

)()(



Figure 1: Trilateration–based Positioning.

2.2.2 Triangulation

The position of a target node can be obtained by the

intersection of several pairs of angles direction lines.

Compared to trilateration only two reference nodes

can track the target as shown in Fig 2. The

Geolocalization in Smart Environment

109

comparison between the different measurement

methods will be clearly shown in Table 1.

Figure 2: Triangulation-based positioning.

Table 1: Comparison Between Different Methods.

Methods Accuracy Cost Energy Efficiency Size of HW

TOA

Medium High Low Large

TDOA

High

Low High Large

AOA Low High Medium Large

RTT

High

High High Large

Figure 3: Localization-based Techniques.

3 RELATED WORK

Several studies tackled the problem of localization to

estimate the coordinates of each node/device in a

complex network. In this section, we will cover with

more details the recent and existing algorithms for

Indoor Positioning System (IPS) and show their

advantages and disadvantages as well.

The Active badge (Want, 1992) (Harter, 1994) is

used to locate individuals in a building. It estimates

their location based on their badges that transmit a

unique infra-red signal every 15 seconds, and each

room in the building is equipped with a network of

sensors which detects these transmissions. The

location can be determined according to the

information delivered by these sensors. The

advantage of this algorithm is the privacy of the

address, whereas its disadvantages are the low

accuracy, long transmission period, and the

influences from fluorescent light and sunlight.

Based on the IR technique, the Firefly system

(Firefly Motion Capture System, 2008) (Firefly

Motion Tracking System User's guide, 1999) comes

with a controller tag, and other several tags in

addition to one array of cameras, is used to track a

person’s or vehicle’s motion. The tag controller

which is carried by the tracked person, is small in size,

light in weight, and battery equipped. Tags are IR

emitters and mounted on different tracked parts of the

person. The array camera receives the IR signals sent

by tags fixed on different parts of the person and

estimates his 3D position. The advantage of this

algorithm is the small measurement delay of 3ms

whereas its disadvantages are that it uses a wire to

connect tags and the coverage area is limited to 7m.

The Optotrak algorithm (Optotrak, 2008) (States,

2006) uses three cameras as a linear array to track 3D

position of various markers on an object. The markers

mounted on different parts of a target, and emit IR

light that is detected by the cameras to estimate their

position. The system uses the triangulation technique

to estimate the position. The advantage of this

algorithm is the high accuracy which is able to

manage relative motion on the different parts of an

object but it is limited to line-of-sight requirement.

The IRIS-LPS approach (Aitenbichler, 2003) is an

optical IR local positioning system. Stereo-Cameras

receive IR signals from a tag mounted by a target

object to measure the AOA, and calculate the position

of the tag using triangulation technique. The main

advantage of this approach is the coverage area it has,

which is larger than that of Firefly and Optotrak, in

addition to this, it is cheap and easy to be installed and

maintained. Moreover, the IRIS-LPS is a multi-tag

track approach but it is subject to interference from

florescent light and sunlight.

The Active Bat system (Active Bat, 2008) (Ward,

1997) uses Ultrasonic technology and triangulation

technique to measure the location of the tag carried

by a person. Tags broadcast periodically a short pulse

of Ultrasound that is received by a matrix of ceiling

mounted receivers at known positions. The distance

between a tag and three receivers is needed to

calculate the 3D position of the tag based on the

multilateration principle. The main advantage is that

it covers a large area and provides 3-D positioning,

but it is subject to the reflection of obstacles and it

uses a large number of receivers on the ceiling.

The Cricket algorithm (Priyantha, 2000) (Das,

2005) uses TOA measuring method and triangulation

technique to locate a target. It uses an ultrasound

emitter as infrastructure, and a receiver carried on

each target. The target owns its location information

and decides how to publish it. The emitters also

transmit RF messages in order to synchronize the

TOA measurement. Its advantages are the address

privacy, the low cost, and the decentralized

administration, but it has high energy consumption.

The Sonitor algorithm (Sonitor, 2008) can locate

SENSORNETS 2019 - 8th International Conference on Sensor Networks

110

people and devices in real time. In the ultrasound IPS,

tags attached to people are tracked by a wireless

detector fixed in various places in an indoor area. The

tracked tag transmits ultrasound signals with a unique

identifier; once received by a detector in the same

place, the detector forwards the information through

the existing LAN or WLAN to a central positioning

calculation element. Its advantage is energy

efficiency though it has a low accuracy level.

The WhereNet algorithm (WhereNet, 2008) is a

Real Time Location Systems (RTLS). It has tags,

location antennas, location processors, servers, and

Ports. Tags are attached to their objects like

persons/devices. Location antennas mounted on the

ceiling at fixed positions receive the signals emitted

from tags and forward the data to the location

processor that perform location calculation and can

track many tags at the same time. Finally, the location

processor transmits the tags’ positions to the server

where ports send low frequency electromagnetic

signals to the tags to indicate their behaviors. The

advantage of this process is the uniquely identified

equipment and person. But it needs several

infrastructure components.

The RADAR algorithm (Bahl, 2000) uses the

existing WLAN, signal strength and signal-to-noise

ratio with the triangulation technique. It can provide

2-D absolute position information. The advantage, is

the reuse of the existing WLAN infrastructure but it

has a low accuracy level, and no privacy

consideration. The located node needs to be equipped

with WLAN technique which is difficult to be applied

because the locate node is light in weight, and has a

limited time energy.

The EKAHAU algorithm (Ekahau, 2008) uses the

existing indoor WLAN infrastructure to monitor the

motion of Wi-Fi tags. The triangulation technique is

used to locate any Wi-Fi enabled device, while the

RSSI values of the transmitted RF are used to

determine the location of the devices. This system

offers 2-D position information. The advantage is the

low cost and power level of the battery but it has a

low level of accuracy because it needs a lot of

calibration, it can only provide 2-D location

information. The comparison between the described

algorithms is presented in Table 2.

In (Ahmadi, 2017), the authors mentioned that

RSSI is widely used because of its availability in most

wireless devices. They also mentioned that range-

based localization category (e.g. AOA, TDOA,

UWB) are expensive in power and delay; while

range-free localization (e.g. neighborhood, and hop)

have limited accuracy. So, they proposed a new

algorithm that merges the learning regression tree

approach with filtering method using RSSI metrics.

Based on artificial intelligence, the learning tree is

used to estimate the position of a mobile device, then

an advanced Particle Filter (PF) is used to minimize

the error of the estimated computed position. The

experience shows that the proposed algorithm is

accurate, and robust to environmental change. In

addition, the PF is robust to noisy environment and

has a low error localization.

In order to reduce the cost of Indoor Localization

Systems (ILS), the authors of (Li, 2018) proposed

PLILS based on a cheap and widely used commercial

chip which supplies four discrete power levels. The

localization employs the idea of fingerprint. PLILS

consists of one reader, reference nodes, and mobile

target nodes. Every reference node broadcasts a data

packet (data fields, identity, etc.) periodically, the

target nodes will receive, process the broadcast

packets, create one specific form, and send it to the

reader for positioning themselves. In addition, to

avoid the large localization errors, a new algorithm

called SOM is used to divide the constructed map of

the target region into several sub-regions. This cost-

effective approach has an accuracy of 1m. Other

studies reached a minimum accuracy of 2m such as in

(Gunathillake, 2016),

4 OUR APPROACH FOR

LOCALIZATION IN

WEARABLE SENSOR

NETWORK

Nowadays, the concept of a smart building is in a

perpetual progress. Many studies were done on the

core of this topic. Our approach will use this concept

in dealing with localization in a Wearable Sensor

Network. The plan of this smart building will be

composed of sensors having a defined range of

capture in predefined areas/locations. Our approach

on localization using Wearable Sensor Network, can

be used by firemen, police, army, etc. Our used

scenario represents a group of firemen during an

indoor firefighting mission. The target is to have a

real-time visibility of the status of each fireman

specially his location, by calculating the coordinates

(X, Y) of each fireman (node) in such mobile

(variable node), distributed (the nodes are in many

places within the environment), dynamic (many

nodes can be added or removed from the network)

and complex network.

Geolocalization in Smart Environment

111

Table 2: Description and Comparison Between Different

Existing Algorithms.

System

Name

Accuracy

Security &

Privac

Cost

Technology/

Method

Active

Badge

Room

Level

Reasonable price

with cheap tag and

sensors

Ultra-sound /

RSS

Firefly 3.0 mm No

A tag controller and

32 ta

Infrared / Not

available

Optotrak 0.1-0.5 mm No Expensive

Infrared / Not

available

IRIS-LPS

16 cm out

of 100 m²

Less than Firefly

and

Optotra

Infrared /

Triangulation

Active Bat

3 cm out

1000 m²

No Expensive

Infrared /

Multilateratiom

Cricket 10 cm Yes Cheap

Ultra-sound,

RF / TOA and

Trian

ulation

Sonitor

Room

Level

No Cheap

Ultra-sound /

Not available

WhereNet

2 to 3 m

No Ex

ensive RFID / TDOA

RADAR

2.26 m out

of 213

Research-oriented

solution, no

roducts

WLAN /

Trian

ulation

EKAHAU 1

No Chea

WLAN / RS

As mentioned in the previous section, many

algorithms and methods were proposed and discussed

in order to track and compute the position of a target

in indoor environment. Existing algorithms are based

on one or more technology such as RF, RSS, UWB,

WLAN, Bluetooth, etc. and many other computing

methods like TOA, TDOA, RTOA and AOA, etc.

These proposed algorithms had their conveniences

and inconveniences according to the used network,

technologies, and methods.

WLAN (IEEE 802.11) is very popular in public

hotspots and enterprise locations. During the last few

years, it has a high rate of 11.54 to 108Mbps, and a

range of 50 to 100m, and an update rate of a few

seconds. IEEE 802.11 is the dominant WLAN. Many

algorithms based on WLAN (RADAR, DIT, etc.) or

Bluetooth (Topaz that integrate IR with the Bluetooth

positioning and communication) seems to be suitable

for our approach but the limitation of these algorithms

is their low-ability to overpass obstacles.

The Bluetooth (IEEE 802.15) technology is

suitable for our approach because it is highly

ubiquitous (embedded in many devices) and it can

support many other networking services. Moreover,

Bluetooth tags are small sized transceivers, and have

a unique ID that can be used to locate each tag.

Bluetooth was designed to exchange a lot of data at

close range. In 2011, when the Bluetooth Low Energy

(BLE) was developed, it had the advantage of low

power consumption but with lower bandwidth, so it

can be largely used for a device that exchanges a

small amount of data periodically which is more

suitable in our case in both proposed scenarios

(Centralized and Broadcast emissions), the range of

this device is about 100 m but it depends on the

surrounding, radio performance, and antennas. The

comparison between Bluetooth and Bluetooth Low

Energy will be described in Table 3.

As mentioned by the authors of (Piwowarczyk,

2013) and (Zhou, 2010), the placement of the beacons

can influence the accuracy of the localizations, that’s

why we suggest to use flexible beacons that can rotate

around their axes in order to have a wide area of

coverage, so that we reduce the repetition of signals

request. As a result, this method will reduce the

energy consumption, the network traffic, and the re-

computation of the localization position. In fact,

recent study demonstrates that the transmission

power of the BLE beacons has a significant impact

on the overall range of the beacons, it is assumed that

adjusting the transmit power of the BLE beacon has

an effect on the beacon’s range and their ability to

overpass obstacles such as walls. Our approach will

use the following technologies:

 BLE having the advantage of low power

consumption.

 Flexible beacons having the ability of rotating

around their axes for better visibility.

 Enhancing the transmission power of the BLE

beacon to increase its ability to overpass obstacles

such as walls, but this overpass will affect the

accuracy in positioning the tags.

Table 3: Comparison between Bluetooth V2.1 and BLE.

Bluetooth V2.1

Bluetooth Low Energy

(BLE)

Ran

Up to 100m Up to 100m

Max range

(free field)

Around 100m

(Class 2 outdoor)

Around 100m

(Outdoors)

Fre

uenc

2.402-2.481 GHz 2.402-2.481 GHz

Max data rate

1-3 Mbit/s 1-Mbit/s

Application

throu

0.7-2.1 Mbit/s

Up to 305 Kbit/s

Topologies

Point-to-point,

Scatternet

Point-to-point,

Mesh networ

Network

standard

IEEE 802.15.1 IEEE 802.15.1

The simulated environment where the armed

firemen (tag holders) are acting, is composed of a

room (20*20 m) having reference nodes called

beacons that will be able to rotate in a horizontal plan,

a Controller Beacon (CB) that memorizes the

localization of every node within the range of each

beacon. Many characteristics should be taken into

consideration to success our implantation.

 Fast: The aim is to have a fast request and

response while emission depends on the mode of

transmission and the used hardware.

 Smart: The algorithm should compute and

estimate the position of each node with fewer

errors.

SENSORNETS 2019 - 8th International Conference on Sensor Networks

112

 Scalable: The number of nodes is variable so the

network should be able to accept any changes

(adding or removing any node).

To avoid synchronization problem with the beacons,

the CB, in every lap of time, will send a request to all

beacons asking them to get the position of all active

firemen existing in their range.

4.1 Centralized Emission

For the centralized emission, each beacon will send a

signal (RSSI, Ultra-Sound or Radio Frequency

Signal) to all the nodes that are in its range. Once

received, each node will reply by sending the

following information to their related beacon: (Sensor

ID, TOS (Time of Sending), Frequency, Sent flag,

Received flag).

Once the beacon captures the signal, it estimates

the coordinates of the node S (emitter) by using the

Time of Arrival (TOA) and the Angle of Arrival

(AOA) by applying the following formula and as

shown in Fig. 4.





(f: frequency of transmission;

TOSTOAt 

)

):(cos AOAD







sinD

Y 

Figure 4: Centralized Emission Scenario.

The concerned beacon will send the result to the

CB in order to update its routing table by saving the

computed coordinates of each node referenced by its

ID. Then, it sends a response to the node in question

that updates from its side the flag received and sets it

to ‘True’. By this, it guarantees that the signal is well

captured. The CB will be considered as a reference to

be contacted at any time by the mission’s responsible.

This scenario gives us a real-time visibility about

the coordinates of each beacon with fewer errors, but

on the other hand, it has an inconvenience which is:

once the mobile node is not able to receive a response

from the beacon upon its request because it is out of

the beacon’s range due to its mobility, it is obliged to

resend the request to another beacon and this will

cause a loss of time and energy.

4.2 Broadcast Emission

The same procedure is applied as described in the

centralized emission, but the difference is after

computing the coordinates of the sender, the beacon

will broadcast the information to all other nodes

existing in its range. This scenario will be repeated

every time the beacon computes a new position of the

same node or a new one. It is also repeated by every

beacon. As shown in Fig 5.

This scenario is very efficient and accurate

because any node at any time has the updated

coordinates of all the nodes in the network. As a

result, in both scenarios, the CB will have, at any

time, a general overview of the coordinates of each

node and their related beacon.

Figure 5: Broadcast Emission Scenario.

As a summary, the CB will own in its database a

general table as indicated in the Table 4.

Table 4: Information on a CB about each Target Position

According to each Related Beacon.

Beacon# Ta

ID An

le X Y Time

1 2 30° 10 8 t1

1 5 30° 20 16 t1

2 7 20° 30 90 t1

1 4 40° 50 70 t2

3 4 40° 60 70 t2

4 1 80° 90 70 t2

………… … …

These two scenarios are suitable for the

characteristics previously mentioned: ‘fast’, ‘smart’

and ‘scalable’ depending on the complexity of the

hardware.

A Matlab simulation shows the number of nodes

covered and tracked by each beacon and shows the

accuracy of our approach. We suppose that we have

20 firemen that are acting randomly in a room (20*20

m) with three obstacles and four beacons as shown in

Fig. 4 and Fig. 5. The beacons had a 30° angle of view

and can rotate horizontally around their axes 30°each

time. The Matlab simulation shows the result and

compares our two scenarios in terms of delay, energy

consumption, tracking, and accuracy as shown in Fig.

6, Fig. 7, Fig. 8, and Fig. 9.

Geolocalization in Smart Environment

113

Figure 6: Delay comparison between the centralized and the

broadcast emissions.

Figure 7: Energy comparison between the centralized and

the Broadcast emissions.

As shown in Fig. 8, the number of targets that has

been tracked is 18 whereas the number of firemen was

20. So if we change the parameter of the rotation of

the angle to be 20° at a time instead of 30° and based

on the “broadcast emission”, we can track all the

existing firemen in the environment.

Figure 8: Tracking of the target inside the room.

Figure 9: Accuracy of each scenario and comparison

between our simulation and the RADAR and EKAHAU

one.

As shown in Fig. 9, the X axis represents the

distance between the beacon and the devise existing

in its range. The Y axis represents the errors. We

simulate the accuracy of each proposed scenario

(Centralized and Broadcast). Then, we repeat the

same simulation with a rotation angle of 20 degree.

As a result, the accuracy will increase each time the

rotation angle of the beacons is small. Finally, we

compare our approach in both scenarios with the

Radar’s and EKAHAU’s one. The position will be

more accurate by increasing the number of beacons.

Comparing our algorithm to the existing ones

described in Table 1, we have shown that our

algorithm is more efficient in term of delay (Fig. 6),

energy consumption (Fig. 7), tracking accuracy (Fig.

8 and Fig. 9), and overpassing obstacles based on the

technologies we used (BLE, flexible beacon,

enhancing power transmission).

5 CONCLUSION

In this paper, we discussed indoor localization for

CBSN in smart environment. We proposed 2

approaches a single-hop approach (centralized

emission) and a multi-hop one (Broadcast emission).

The proposed approaches were compared against

existing algorithms on delay, power consumption and

accuracy. Our proposed approaches are very

convenient on power consumption and delay and

have very good accuracy, thus providing a very

competitive alternative.

REFERENCES

Active Bat website 2008 Available:

http://www.cl.cam.ac.uk/research/dtg/attarchive/bat/ .

Ahmadi, H., Viani, F. and Bouallegue, R., 2018. An

accurate prediction method for moving target

localization and tracking in wireless sensor networks.

Ad Hoc Networks, 70, pp.14-22.

Aitenbichler, E. and Muhlhauser, M., 2003, May. An IR

local positioning system for smart items and devices. In

Distributed Computing Systems Workshops, 2003.

Proceedings. 23rd International Conference on (pp.

334-339). IEEE.

Bahl, P. and Padmanabhan, V.N., 2000. RADAR: An in-

building RF-based user location and tracking system. In

INFOCOM 2000. Nineteenth Annual Joint Conference

of the IEEE Computer and Communications Societies.

Proceedings. IEEE (Vol. 2, pp. 775-784). IEEE.

Cheong, P., Rabbachin, A., Montillet, J.P., Yu, K. and

Oppermann, I., 2005, September. Synchronization,

TOA and position estimation for low-complexity LDR

SENSORNETS 2019 - 8th International Conference on Sensor Networks

114

UWB devices. In Ultra-Wideband, 2005. ICU 2005.

2005 IEEE International Conference on (pp. 480-484).

IEEE.

Das, S., Gleason, C., Shen, S., Goddard, S. and Pérez, L.C.,

2005, May. 2d tracking performance evaluation using

the cricket location-support system. In Electro

Information Technology, 2005 IEEE International

Conference on (pp. 6-pp). IEEE.

EKAHAU website 2008 Available:

http://www.ekahau.com/.

Falsi, C., Dardari, D., Mucchi, L. and Win, M.Z., 2006.

Time of arrival estimation for UWB localizers in

realistic environments. EURASIP Journal on Advances

in Signal Processing, 2006(1), p.032082.

Firefly Motion Capture System 2008 [online] Available:

http://www.cybernet.com/interactive/firefly/index.htm

Firefly Motion Tracking System User's guide 1999

Available:

http://www.gesturecentral.com/firefly/FireflyUserGuid

e.pdf

Gunathillake A., V. Savkin A., Jayasumana A. and

Seneviratne A. (2016). Sensor Localization using

Signal Receiving Probability and Procrustes Analysis.

In Proceedings of the 5th International Conference on

Sensor Networks - SENSORNETS, ISBN 978-989-758-

169-4, pages 113-120. DOI:

10.5220/0005671301130120

Harter, A. and Hopper, A., 1994. A distributed location

system for the active office. IEEE network, 8(1), pp.62-

70.

Han, G., Choi, D. and Lim, W., 2007, October. A novel

reference node selection algorithm based on

trilateration for indoor sensor networks. In Computer

and Information Technology, 2007. CIT 2007. 7th IEEE

International Conference on (pp. 1003-1008). IEEE.

Li, X., Yang, Y., Cai, J., Deng, Y., Yang, J., Zhou, X. and

Tan, L., 2018. Plils: A Practical Indoor Localization

System through Less Expensive Wireless Chips via

Subregion Clustering. Sensors, 18(1), p.205.

Linde, H., 2006. On aspects of indoor localization.

Mailaender, L., 2007, September. Comparing geo-location

bounds for TOA, TDOA, and round-trip TOA. In

Personal, Indoor and Mobile Radio Communications,

2007. PIMRC 2007. IEEE 18th International

Symposium on (pp. 1-5). IEEE.

Niculescu, D. and Nath, B., 2003, March. Ad hoc

positioning system (APS) using AOA. In INFOCOM

2003. Twenty-Second Annual Joint Conference of the

IEEE Computer and Communications. IEEE Societies

(Vol. 3, pp. 1734-1743). IEEE.

Optotrak 2008 Available: http://www.ndigital.com/ .

Piwowarczyk, K., Korbel, P. and Kacprzak, T., 2013,

September. Analysis of the influence of radio beacon

placement on the accuracy of indoor positioning

system. In Computer Science and Information Systems

(FedCSIS), 2013 Federated Conference on (pp. 889-

894). IEEE.

Priyantha, N.B., Chakraborty, A. and Balakrishnan, H.,

2000, August. The cricket location-support system. In

Proceedings of the 6th annual international conference

on Mobile computing and networking (pp. 32-43).

ACM.

States, R.A. and Pappas, E., 2006. Precision and

repeatability of the Optotrak 3020 motion measurement

system. Journal of medical engineering & technology,

30(1), pp.11-16.

Sonitor website 2008 Available: http://www.sonitor.com/.

Takabayashi, Y., Matsuzaki, T., Kameda, H. and Ito, M.,

2008, August. Target tracking using TDOA/FDOA

measurements in the distributed sensor network. In

SICE Annual Conference, 2008 (pp. 3441-3446). IEEE.

Want, R., Hopper, A., Falcao, V. and Gibbons, J., 1992. The

active badge location system. ACM Transactions on

Information Systems (TOIS), 10(1), pp.91-102.

Ward, A., Jones, A. and Hopper, A., 1997. A new location

technique for the active office. IEEE Personal

communications, 4(5), pp.42-47.

Wherenet website 2008 Available: http://www.

wherenet.com/.

Zhang, D., Xia, F., Yang, Z., Yao, L. and Zhao, W., 2010,

May. Localization technologies for indoor human

tracking. In Future Information Technology

(FutureTech), 2010 5th International Conference on

(pp. 1-6). IEEE.

Zhou, J., Shi, J. and Qu, X., 2010. Landmark placement for

wireless localization in rectangular-shaped industrial

facilities. IEEE Transactions on Vehicular Technology,

59(6), pp.3081-3090.

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