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International Journal of Computer Engineering & Technology (IJCET) Volume 8, Issue 6, Nov-Dec 2017, pp. 23–35, Article ID: IJCET_08_06_003

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SURVEY ON INDOOR LOCALIZATION:

EVALUATION PERFORMANCE OF

BLUETOOTH LOW ENERGY AND

FINGERPRINTING BASED INDOOR

LOCALIZATION SYSTEM

Gemechu Wako Samu

Department of Computer science and Engineering,

Symbiosis International University, Institute of Technology, Pune, Maharashtra, India

Prachi Kadam

Ass. Prof., Department of Computer science and Engineering,

Symbiosis International University, Institute of Technology, Pune, Maharashtra, India

ABSTRACT

Location-based systems are significantly trending issue in IoT fields, as it comes

up with services such as navigation and direction to use it for guiding those in need of

assistance. While GPS provides reliable outdoor localization, indoor localization

system is still challenging and many technologies have been proposed. Indoor

localization systems are being developed since last two decades, by making use of

radio frequency, ultrasound or infrared based signal and other technical

advancements in IoT, to provide location and navigation service to the users.

However, most of them rely on customized hardware or presume some dedicated

infrastructure. The main objective of this survey paper is to provide the reader with a

review of the main technologies explored in the literature to solve any indoor

localization issues. Moreover, some of the common used indoor localization

algorithms along with their measurement methods for position estimation in indoor

environments are presented and discussed. Finally, one of the localization algorithms,

fingerprinting algorithm based BLE indoor localization scenario will be discussed.

Key words: Indoor Localization, Internet of Things, BLE, Ibeacon, Fingerprinting

Algorithm.

Cite this Article: Gemechu Wako Samu and Prachi Kadam, Survey on Indoor

Localization: Evaluation Performance of Bluetooth Low Energy and Fingerprinting

Based Indoor Localization System. International Journal of Computer Engineering &

Technology, 8(6), 2017, pp. 23–35.

http://www.iaeme.com/ijcet/issues.asp?JType=IJCET&VType=8&IType=6

Gemechu Wako Samu and Prachi Kadam


1. INTRODUCTION

Internet of things is a very highly contributing and an important part of the new era of

technology, have come up rapidly with both theory and practice ever since it has been

proposed. This on a regular basis has resulted in many applications such as Smart city,

Industrial Internet, Smart home, Smart Retail, intelligent environmental monitoring, IOT in

Healthcare and of course, location-based services. For location-based service, outdoor and

indoor localization are two common ways of the service. GPS works very well in the outdoor

environment, but in case of indoor localization, the signal from the GPS satellites is weak to

enter into buildings, which makes it hard for GPS to function in indoor localization

environment. Moreover, locating position information in indoor situations is most challenging

because of quite a few reasons like; errors by multipath and Non-Line-of-Sight conditions,

the presence of moving people that modify the indoor propagation channel, greater density of

obstacles that cause a high attenuation and signal scattering, demand of a higher precision and

accuracy. Fortunately, over last two decades, important research is being done in the area of

indoor localization. This has led to the development of several indoor positioning systems

using different signal technologies for both research and commercial purposes. These

solutions are built with different measurement methods e.g. fingerprinting, literation,

angulation, and Received Signal Strength. Therefore, when developing an indoor positioning

system choice has to be made with respect to signal technologies available and measurement

methods that can be used with these technologies. The following figure shows some of the

common signal technology that is used in making indoor positioning systems.

Figure 1 Common signal technologies used in Indoor Localization

To develop an indoor localization system and choose from which signal technology to

use, a lot of factors such as; cost, accuracy, robustness, scalability, resilience, and coverage

should be considered. It’s obvious that a solitary solution that works without limitation for

any scenario does not exist. Then, it is significant to consider the enactment factors of all

technologies and contest them with the user requirements, which have to be examined and

defined precisely for each application. Additionally, the standards of performance factors are

not unambiguously determined since they, in turn, depend on numerous factors and

conditions. Therefore, it is necessary to find the right method to be used taking in to account

performance parameters, user requirements, and environmental conditions in order to come

up with a good solution.

Indoor

Localization

system

Optical

system

Ultrasound

Based

System

Radio

frequency

based

Infrared

Based

System

Other

System

Survey On Indoor Localization: Evaluation Performance of Bluetooth Low Energy and Fingerprinting

Based Indoor Localization System


In indoor localization works done, there are several approaches in which some of them

focus their attention on one technology. In [1], the deliberated indoor localization approach is

based on the Radio Frequency Identification (RFID) technology, in [2] the Sample Size

Determination Algorithm for fingerprinting based indoor localization systems is explored. In

[3], fingerprinting indoor localization technique is explored where deep learning model,

called de-noising auto-encoder is used, to extract robust fingerprint patterns from noisy RSSI

measurements and make a BLE based indoor localization environment.

In [4] the authors present an Indoor Multi-Tag Cooperative Localization Algorithm Based

on NMDS (nonmetric multi-dimensional scaling) for RFID. They even used received signal

strength Euclidean distance based on finger printing method to get the rank order of the

distance between all pairs of tags, whereas in [5], the implementation of indoor localization

based on an experimental study of RSSI using a wireless sensor network is analyzed and

discussed. Finally, [6] provides an implementation of a mobile-based indoor positioning

system using mobile applications with the iBeacon solution based on the Bluetooth Low

Energy (BLE) technology.

This survey paper aims to give an updated overview of the most popular enabling

technologies and provide a review of the main technologies explored in the literature to solve

the indoor localization system issues. Moreover, some of the common used indoor

localization algorithms along with their measurement methods for position estimation in

indoor environments are presented and discussed.

2. INDOOR LOCALIZATION METHODS AND TECHNOLOGY.

Indoor localization systems enable users to find the location of assets, people and places in

specified environments like a shopping mall, Hospitals, Train Stations and Airway stations.

Meanwhile, GPS is imperfect inside buildings for the reason that visual contact with GPS

satellites is poor and the signals can’t penetrate through walls, an Indoor Localization

Systems need to use other positioning means. In most cases, this includes RFID, WLAN/Wi-

Fi, ZigBee or Bluetooth Low Energy Beacons in combination with the internal sensors of a

smartphone. The first and most important step in the implementation of indoor positioning

systems is the selection of the indoor positioning signal technology.

As mentioned earlier, indoor localization systems can be developed using different signal

technologies. Following are the most commonly used signal technologies.

1. Infrared (IR) based Localization Systems

2. Ultrasonic (US) based Localization Systems

3. Radio Frequency (RF) based Localization Systems

4. Optical-based Localization Systems

5. Other Localization Systems

2.1. Infrared (IR) Localization Systems

Infrared-based indoor localization technology is among the most commonly used systems that

work with the help of wireless technology and can be used in applications for detecting or

tracking objects or assets. They are readily available for various devices like mobile phones,

PDAs, and TV (both wired and wireless). The mechanism for IR-based systems is based on

using LOS communication between the two nodes, i.e. transmitter and receiver, provided

there is no interference from light/optical sources in the environment. They are advantageous

due to their small size, being lightweight and thus easily moveable. But also have issues like

security and privacy and require expensive hardware and maintenance cost. An example of a

localization system based on Infrared technology is Active Badge System [13]



IR based indoor location systems use Infrared light pulses (like a TV remote) to locate

signals inside of a building. IR readers are installed in every room, and when the IR tag

pulses, it is read by the IR reader device. It is a near-foolproof way of guaranteeing room

level accuracy. The drawback is that every room needs a wired IR reader to be installed in the

ceiling. It is commonly used in new hospital construction.

2.3. Ultrasonic Localization Systems

Ultrasonic based localization systems use ultrasonic waves to measure the distance between

the sound source and the mobile system (whose localization is required). Generally, such

systems have multiple ultrasonic receivers and synchronization between them is required

which is usually done with IR or RF waves. The systems use ToA (Time of Arrival)

information of the sound signal from the source to the receiver to estimate receivers’ distance

from the source. The systems based on ultrasonic technology enjoy very good accuracy. Also,

low cost, ease of implementation and high accuracy make such systems a good option for

indoor localization. The disadvantage is they are also affected by a multipath reception and

can have large-scale implementation complexity.

2.3. Radio Frequency Based (RF) Localization Systems

Localization systems based on radio frequency (RF) technologies are most commonly used

nowadays due to the property of radio waves to penetrate through obstacles like walls, human

bodies etc. These systems thus provide better coverage and can be deployed with less

hardware. Another useful aspect of RF-based localization systems is the further division of

RF into narrowband based technologies (RFID, Bluetooth, WLAN/WiFi, and FM) and

wideband based technologies (UWB). RF-based localization systems have attracted

researchers interests over the last ten years and a significant amount of work is done in this

regard. The technique used in RF-based localization is given below and will be subsequently

explained.

1. RFID

2. Bluetooth Low Energy

3. WLAN/WiFi

4. ZigBee

5. UWB and

6. Hybrid

RFID

RFID (radio-frequency identification), practices the use of radio waves to wirelessly

communicate the identity and other characteristics of an object, to an evolving positioning

technology and allows flexible tracking of objects or people. RFID is not suitable for area-

wide positioning as it offers a limited range of less than a meter, but rather for a selected

object identification. It’s cost-effective, easy for maintenance and provides both identification

and location. This makes localization via RFID mostly appropriate for tracking results in

manufacturing environments (e.g. asset management).

The categories of this expertise brands it the perfect contender for the tracking of

numerous products, like food or drugs [14], [15], [16] along the stock series, but it is also

used for several further purposes, comprising indoor localization. RFID technology based

localization systems are used in many applications such as locating people, in automobile

assembly industry, in warehouse management, in supply chain network etc. since the system

works without line of sight requirement.




A basic system would consist of a reader (also known as RFID scanner) with an antenna

which constantly scans for active transceivers or passive tags in its environment. Using radio

signals as one way wireless communication of data is done from RFID tags to the reader.

Following fig shows basic procedures of how RFID based localization works.

Figure 2 Representation of RFID technology working

Bluetooth (BLE beacons)

Bluetooth is a wireless standard for WPANs (Wireless Personal Area Networks) just like

ZigBee. It is a patented format handled by Bluetooth SIG (Special Interest Group). Bluetooth

operates in the 2.4 GHz ISM band. Compared to WLAN, the range is shorter (typically 10–15

m). With Bluetooth standard also used for information exchange, there is also another benefit

of this technology in form of provision of high security, low cost, low power and small size.

Bluetooth technology can be used in position detection and authorizing to reuse the devices

previously well-appointed with Bluetooth technology, so the addition of a fresh consumer to

such a system does not involve any extra hardware. Meanwhile, Bluetooth is a less in cost

and has low power consumption technology, it is effectual in order to project indoor

localization systems. Moreover, Bluetooth tags have small size transceivers. As any other

Bluetooth device, respective tags have an exclusive ID, which can be used to locate the

Bluetooth tags. On the other hand, Bluetooth is a “lighter” and pervasive typically because it

is implanted in most devices such as mobile phones, personal digital assistants (PDAs),

laptop, desktop, etc.

There has been researching done in exploring the best possible positioning principle for

Bluetooth based localization systems. In [6], a mobile-based indoor positioning system using

mobile applications with iBeacon solution based on the Bluetooth Low Energy (BLE)

technology are implemented. Whereas in [9] the technology is used for finding an accurate

and precise location of a tracked asset or place by using smartphone built-in inertial

measurement unit (IMU) sensors, WiFi received signal strength measurements and

opportunistic iBeacon corrections based on particle filter. BLE-based indoor positioning

systems usually use Proximity and fingerprinting localization approach. The following fig

shows how a common BLE based indoor positioning system would work by fingerprinting

approach.



Figure 3 Representation of BLE technology working

WLAN/WiFi

Using a WiFi-based indoor positioning system is common practice because of low

infrastructure cost and no need for line-of-sight (LOS). Any device with WiFi compatibility

can be easily localized without any additional hardware or software manipulation. They are

commercially available and are mostly based on received signal strength measurement

principle.

There are several advantages for designing a localization system using WLAN (WiFi)

technology. Some of them include the ready availability of access points in indoor

environments, no special hardware requirements, a 50-100 meters range making it more

attractive in comparison to Bluetooth or RFID.

ZigBee

ZigBee is wireless technology standard popular for short and medium range communication

applications. It can be regarded as a low rate Wireless Personal Area Network (WPAN). The

standard is designed for applications requiring low power consumption in mind and not

requiring large data throughput. For indoor environments, ZigBee signal range is typically

20-30m. RSSI is the usual principle used for distance estimation between two ZigBee nodes.

One drawback is that since ZigBee operates in the unlicensed ISM band, the designed

localization system would be vulnerable to interference from other signal types consequently

harming the radio communication. In [8] ZigBee communication technology is used to design

an energy efficient indoor localization system and to improve the localization accuracy.

Whereas [7] used ZigBee to perform an indoor localization application and locate a person in

a building with a reasonable position accuracy

UWB

Ultra-wide-band is a radio technology for short range, high bandwidth communication

holding the properties of strong multipath resistance. For localization systems with high

accuracy demands (20-30 cm), UWB is widely used as other conventional wireless

technologies such as RFID and WLAN/WiFi do not provide such high level of accuracy. A

basic UWB based localization setup would include stimulus radio wave generators and

receivers which can capture the propagated and scattered waves. UWB signals have property

to penetrate through walls, glass and other obstacles making it extremely good for indoor

localization because ranging is then free of LoS constraint and also inter-room ranging is




possible. The problem with UWB is that hardware is expensive thus making it unsuitable for

large-scale implementation.

Hybrid

Hybrid localization systems use multiple different localization technologies for locating a

mobile client. Localizing a mobile client is one of the most important services of a

localization system and since some location technologies are primarily designed for indoor

and GPS based positioning system is unsuitable for indoor, thus a hybrid system which works

both indoors and outdoors would be highly desirable. This is how the concept of hybrid

localization system came into being. Hybrid localization systems have been worked upon and

[12] implemented a prototype of the hybrid indoor positioning system to obtain better results

jointly using both iBeacon BLE and WiFi.

2.4. Optical Positioning Systems

Optical indoor positioning systems use the camera as the main sensor. There are also optical

positioning systems in combination with a distance or mechanical sensors. Optical indoor

localization systems using camera-based system architectures are exclusively built on the

Angle of Arrival (AoA) method. The advancement in CCD technologies, processing speed,

and image understanding have helped in developing camera-based indoor localization

systems.

2.5. Other Systems

There are other ways to do indoor localization as well. Some of the systems developed in this

regard are discussed now. They can be a specifically designed system with a certain

application in mind and would make use of different available options including external

(multiple sensors), different RF technologies etc. They are as:

• Inertial Navigation Systems (INS)

• Magnetic Localization

• Infrastructure-Based Localization Systems

3. INDOOR LOCALIZATION ALGORITHMS

The over-all algorithms which are commonly used for indoor localization are listed below:

• Trilateration Algorithm

• Triangulation Algorithm

• Fingerprinting Algorithm

• Proximity Algorithm

• Dead Reckoning Algorithm

These algorithms make use of different measurement methods for position estimation for

indoor positioning. A graph showcasing the above-mentioned algorithms with their

corresponding measurement methods is given below. Each algorithm is briefly explained

afterward.



Figure 4 Common Indoor Localization algorithms

Triangulation and Trilateration

The working principle of triangulation practices geometric assets of triangles to define the

target’s position, whereas travel time of the signal from the source to destination is used in

trilateration. They are of two derivations (basic measurement principles):

• Lateration and

• Angulation

Lateration (Trilateration)

In lateration, the position of an object is estimated by measuring its distance from multiple

reference points. In this approach time of arrival (ToA) or time difference of arrival (TDoA)

measurement method is used and distance is derived by computing attenuation of signal

strength or by simply using the relationship that signal velocity multiplied with time traveled

gives distance. The common lateration measurement techniques are:

• Time of Arrival (ToA) Method

• Time Difference of Arrival (TDoA) Method

• RSS (Received Signal Strength or Signal Attenuation) based Method

• RToF (Roundtrip Time of Flight) Method

• Received Signal Phase Method

Angulation (Triangulation)

In angulation measurement method, the position of an object is computed with help of

measured positions comparative to several location points. This method is typically

implemented with Angle of Arrival method.

Fingerprinting

Fingerprinting or Scene analysis is a type of algorithm used for indoor localization in which

the first step is to gather features of a scene and then assess the position of an entity by

corresponding current location’s dimensions with the neighboring apriori location

fingerprints. Position fingerprinting involves, matching of the fingerprint of a signal’s feature

which is location dependent. This technique comprises of two stages:

Indoor localization

Trilateration

ToA

TDoA

Triangulation AoA

Fingerprining RSSI

Proximity RSSI

Dead Reckoning




• Offline &

• Online stage.

The offline stage is about doing a site survey of the environment. This involves taking

signal strengths of various location points from the close-by base stations (reference units)

and noting them down. The online stage would then be using a positioning algorithm to

estimate the current location, based on the observed current signal strength and previously

collected information. The key challenge for the positioning algorithms based on location

fingerprinting is a general problem with signal strength i.e. it being affected by diffraction,

reflection, and scattering in its propagation in an indoor environment. There are multiple

fingerprinting-based localization algorithms using pattern recognition method, e.g. Euclidean

distance, Probabilistic methods, K-Nearest neighbors (kNN), Neural networks etc.

The standard signal technology used is RF (Received Signal Strength Indication, RSSI)

for fingerprinting but there are also fingerprinting localization systems with audio signals or

visual images.

Proximity

The proximity method for localization finds the position of a mobile device just by its

presence in a special area. Hence, proximity-based algorithms provide symbolic relative

location information. This method works by simply forwarding the location of an anchor

(base or reference) point from where the strongest signal is received. Proximity measurement

method has a simple implementation, but the accuracy of this method depends on how much

anchor points are deployed and signal range. Proximity-based localization systems are usually

based on signal technologies like Infrared Radiation (IR) and Radio Frequency Identification

(RFID). General examples of proximity-based localization systems are in sensing physical

interaction, automatic ID systems, and mobile wireless locating systems.

Dead Reckoning

In dead reckoning, the position is estimated by using knowledge of previously defined points

and recognized or assessed speeds over the intervened period. Usually, the main sensor type

used is an inertial navigation system. The one problem with this system’s usage is inaccuracy

is cumulative; hence, abnormality in the location fix raises with time. In the domain of indoor

applications, a term called Pedestrian Dead Reckoning (PDR) is used in literature to indicate

that external sensors like accelerometer are being attached to the user’s body.

Table ahead summarizes different algorithms and measurement methods used for indoor

localization with respect to some key performance parameters.

Table 1 Summary of different methods used in indoor localization systems.

Method Measurement

Type Accuracy Coverage

LoS/NLo

S Multipath affect Cost

Proximity RSS Low-high Good Both No Low

Direction AoA Medium Good LOS Yes High

Time ToA,TDoA High Good LOS Yes High

Fingerprinting RSS High Good Both No Medium

Dead

reckoning

Acceleration,

Velocity Low-medium Good NLOS Yes Low



4. MAKING OF BLE FINGERPRINTING ALGORITHM BASED

INDOOR LOCALIZATION

In making indoor localization system, fingerprinting is commonly used localization approach.

This is because this method requires no additional cost on infrastructure along with no prior

knowledge of the environment is required. This algorithm starts with a comprehensive survey

of the site (i.e. the indoor space which is to be localized) with respect to RSS readings that

can be recorded over multiple points (distance distribution) in the coverage area. This results

in a database of recorded signal strengths over numerous points (i.e. fingerprints of each

point). The localization (of a mobile device) problem is then reduced to co-relating

(matching) the currently measured RSS reading with those in the database to estimate

position. The system works on the assumption that each position in localization space can be

associated with a unique signal strength feature and by virtue of this current location can be

obtained relying on the difference of signal strength at different positions.

4.1. How it works

The implementation of fingerprinting involves conducting an offline & online phase. These

two phases are explained in detail ahead to develop a better understanding so that a BLE-

based indoor localization system can be developed using fingerprinting localization approach,

and its performance with respect to accuracy will be evaluated.

4.2. Offline phase

The offline phase starts with the division of the indoor environment area (where localization

of a mobile device is to be done) into a grid of cells. The Figure (3.7) helps explain this first

step of offline phase. Consider a generic indoor environment, presented as a blank square box

on left side in Figure (3.7). This indoor space is divided into small cells. Each cell enjoys a

unique identification within the localization space. In the second step of offline phase, signal

strength characteristic for each cell is recorded (usually at the center of each cell) and

associated with it. This way a database (or radio-map) is built where each cell will have its

own unique RSS characteristic from each reference node and hence the word fingerprint. The

radio-map (or database) can be created in two ways: mean value type radio-map and

probability density function type radio-map. Commonly mean value type radio-map

(database) is created in offline case. In mean value type radio map, mean RSSI values from

each reference node are gathered for each cell

Figure 5 The division of (desired) localization area into small cells acts as the first step of offline

phase. For each cell mean RSS value from each reference node is measured and uniquely associated

with that cell’s identity.

The pseudocode for offline phase (also called calibration phase) is provided here for

fingerprinting approach. The quality of radio-map would determine the precision and

accuracy of position estimation of a mobile device. Therefore more the number of points

where signal features are collected i.e. the richer the database, better would be the outcome of




localization results. Hence for good localization performance, an extensive site survey

(offline phase) should be conducted. This requires that fingerprints of numerous points (with

high resolution) in the localization space should be gathered.

4.3. Online Phase

In the online phase measurements taken at the current location (in the localization space) are

matched with the already-established database (or radio-map) from the offline phase. The

position estimation of a mobile device is done by matching the current position’s signal

feature with the fingerprint (signal features) of each cell in the database. The cell whose

signal feature is closest to mobile device’s current location’s signal feature is obtained and the

coordinates of the midpoint of that cell are estimated as the 2D position of the mobile device.

One problem with fingerprinting-based localization approach is that indoor environments

are dynamic and collection of signal features in offline phase may not account for the change

of indoor environment via indoor decoration, furniture, or walking of people which might

have happened at the time of online phase measurements. This can severely affect

localization performance.

The following figure of an architecture taking the BLE based indoor localization of

fingerprinting approach is assumed.

Figure 3 An architecture showcasing how fingerprinting based indoor localization would work

5. CONCLUSION

The attention in the direction of the indoor localization is quite large in the literature and

more and more efforts are made to explore further operative explanations which are able to

overcome the limitations of the technologies which could be applied. Selection of the

appropriate technology, or a combination of them, varies in circumstance and depends on

both the explicit application framework and user necessities in relation to precision, coverage

area, price, obligatory set-up, robustness, scalability, and so on. Actually, a solution that is

appropriate for a specific state, can indicate to failure for another. This paper is envisioned to

deliver an outline on latest technologies for tracking and positioning in an indoor

environment. Particular attention has been turned to systems for indoor positioning based on

fingerprinting approach of iBeacon technology and how they operate.



REFERENCES

[1] Rostamian, M., Wang, J., & Bolić, M. (2017). An Accurate Passive RFID Indoor

Localization System Based on Sense-a-Tag and Zoning Algorithm. In Ad Hoc Networks

(pp. 270-281). Springer International Publishing.

[2] Kanaris, L., Kokkinis, A., Fortino, G., Liotta, A., & Stavrou, S. (2016). Sample Size

Determination Algorithm for fingerprint-based indoor localization systems. Computer

Networks, 101, 169-177.

[3] Xiao, C., Yang, D., Chen, Z., & Tan, G. (2017). 3-D BLE Indoor Localization Based on

Denoising Autoencoder. IEEE Access, 5, 12751-12760.

[4] Gao, Z., Ma, Y., Liu, K., Miao, X., & Zhao, Y. (2017). An Indoor Multi-Tag Cooperative

Localization Algorithm Based on NMDS for RFID. IEEE Sensors Journal, 17(7), 2120-

2128.

[5] Chen, Z., Zhu, Q., & Soh, Y. C. (2016). Smartphone inertial sensor-based indoor

localization and tracking with iBeacon corrections. IEEE Transactions on Industrial

Informatics, 12(4), 1540-1549.

[6] El Amine, C. M., Mohamed, O., & Boualam, B. (2016). The implementation of indoor

localization based on an experimental study of RSSI using a wireless sensor network.

Peer-to-Peer Networking and Applications, 9(4), 795-808.

[7] Niu, J., Wang, B., Shu, L., Duong, T. Q., & Chen, Y. (2015). ZIL: An energy-efficient

indoor localization system using ZigBee radio to detect WiFi fingerprints. IEEE Journal

on Selected Areas in Communications, 33(7), 1431-1442.

[8] Lin, X. Y., Ho, T. W., Fang, C. C., Yen, Z. S., Yang, B. J., & Lai, F. (2015, August). A

mobile indoor positioning system based on iBeacon technology. In Engineering in

Medicine and Biology Society (EMBC), 2015 37th Annual International Conference of

the IEEE (pp. 4970-4973). IEEE.

[9] Zou, H., Chen, Z., Jiang, H., Xie, L., & Spanos, C. (2017, March). Accurate indoor

localization and tracking using mobile phone inertial sensors, WiFi and iBeacon. In

Inertial Sensors and Systems (INERTIAL), 2017 IEEE International Symposium on (pp.

1-4). IEEE.

[10] Ji, M., Kim, J., Jeon, J., & Cho, Y. (2015, July). Analysis of positioning accuracy

corresponding to the number of BLE beacons in indoor positioning system. In Advanced

Communication Technology (ICACT), 2015 17th International Conference on (pp. 92-

95). IEEE.

[11] Li, X., Xu, D., Wang, X., & Muhammad, R. (2016, July). Design and implementation of

indoor positioning system based on iBeacon. In Audio, Language and Image Processing

(ICALIP), 2016 International Conference on (pp. 126-130). IEEE.

[12] Chiu, C. C., Hsu, J. C., & Leu, J. S. (2016, October). Implementation and analysis of

Hybrid Wireless Indoor Positioning with iBeacon and Wi-Fi. In Ultra Modern

Telecommunications and Control Systems and Workshops (ICUMT), 2016 8th

International Congress on (pp. 80-84). IEEE.

[13] Want, R., Hopper, A., Falcao, V., & Gibbons, J. (1992). The Active Badge Location

System ACM Transactions on Information Systems, 10 (1): 91-102.

[14] Maffia, M., Mainetti, L., Patrono, L., & Urso, E. (2012). Evaluation of potential effects

of RFID-based item-level tracing systems on the integrity of biological pharmaceutical

products. International Journal of RF Technologies, 3(2), 101-118.

[15] Calcagnini, G., Censi, F., Maffia, M., Mainetti, L., Mattei, E., Patrono, L., & Urso, E.

(2012). Evaluation of thermal and nonthermal effects of UHF RFID exposure on

biological drugs. IEEE Transactions on Information Technology in Biomedicine, 16(6),

1051-1057.




[16] Catarinucci, L., Colella, R., De Blasi, M., Mighali, V., Patrono, L., & Tarricone, L.

(2010, September). High performance RFID tags for item-level tracing systems. In

Software, Telecommunications and Computer Networks (SoftCOM), 2010 International

Conference on (pp. 21-26). IEEE.

[17] Gansemer, S., Großmann, U., & Hakobyan, S. (2010, September). Rssi-based euclidean

distance algorithm for indoor positioning adapted for the use in dynamically changing

wlan environments and multi-level buildings. In Indoor Positioning and Indoor

Navigation (IPIN), 2010 International Conference on (pp. 1-6). IEEE.

[18] Sun, D., Leung, V. C., Qian, Z., Liu, Y., & Ge, B. (2016). Beacon deployment strategy

for guaranteed localization in wireless sensor networks. Wireless Networks, 22(6), 1947-

1959.

[19] Kapil K Shukla, Kaushik I Manavadariya and Deven J Patel, Comparative Study of

Bluetooth, 802.11 AND HIPERLAN, Volume 4, Issue 3, May-June (2013), pp. 455-463,

International Journal of Computer Engineering and Technology (IJCET).

[20] Nilima Bargal and Pratima Bhalerao, Effortless Sharing of Books On E-Library Through

Bluetooth, Volume 5, Issue 6, June (2014), pp. 61-66, International Journal of Electronics

and Communication Engineering & Technology (IJECET)

[21] Zheng, X., Bao, G., Fu, R., & Pahlavan, K. (2012, September). The performance of

simulated annealing algorithms for wi-fi localization using google indoor map. In

Vehicular Technology Conference (VTC Fall), 2012 IEEE (pp. 1-5). IEEE.

survey on indoor localization: evaluation … · indoor localization systems enable users to find...

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