SURVEY ON INDOOR LOCALIZATION: EVALUATION PERFORMANCE OF BLUETOOTH LOW ENERGY AND FINGERPRINTING BASED INDOOR LOCALIZATION SYSTEM

International Journal of Computer Engineering & Technology (IJCET) Volume 8, Issue 6, Nov-Dec 2017, pp. 23 35, Article ID: IJCET_08_06_003 Available online at http://www.iaeme.com/ijcet/issues.asp?jtype=ijcet&vtype=8&itype=6 Journal Impact Factor (2016): 9.3590(Calculated by GISI) www.jifactor.com ISSN Print: 0976-6367 and ISSN Online: 0976 6375 IAEME Publication 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 http://www.iaeme.com/ijcet/index.asp 23 editor@iaeme.com

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. Optical system Other System Indoor Localization system Ultrasound Based System Infrared Based System Radio frequency based 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. http://www.iaeme.com/ijcet/index.asp 24 editor@iaeme.com

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] http://www.iaeme.com/ijcet/index.asp 25 editor@iaeme.com

Gemechu Wako Samu and Prachi Kadam 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 areawide 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. http://www.iaeme.com/ijcet/index.asp 26 editor@iaeme.com

Survey On Indoor Localization: Evaluation Performance of Bluetooth Low Energy and Fingerprinting Based Indoor Localization System 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. http://www.iaeme.com/ijcet/index.asp 27 editor@iaeme.com

Gemechu Wako Samu and Prachi Kadam 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 http://www.iaeme.com/ijcet/index.asp 28 editor@iaeme.com

Survey On Indoor Localization: Evaluation Performance of Bluetooth Low Energy and Fingerprinting Based Indoor Localization System 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. http://www.iaeme.com/ijcet/index.asp 29 editor@iaeme.com

Gemechu Wako Samu and Prachi Kadam Trilateration ToA TDoA Indoor localization Triangulation Fingerprining AoA RSSI Proximity RSSI Dead Reckoning 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: http://www.iaeme.com/ijcet/index.asp 30 editor@iaeme.com

Survey On Indoor Localization: Evaluation Performance of Bluetooth Low Energy and Fingerprinting Based Indoor Localization System 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 LoS/NLo Accuracy Coverage Type 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 Acceleration, reckoning Velocity Low-medium Good NLOS Yes Low http://www.iaeme.com/ijcet/index.asp 31 editor@iaeme.com

Gemechu Wako Samu and Prachi Kadam 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 BLEbased 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 http://www.iaeme.com/ijcet/index.asp 32 editor@iaeme.com

Survey On Indoor Localization: Evaluation Performance of Bluetooth Low Energy and Fingerprinting Based Indoor Localization System 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. http://www.iaeme.com/ijcet/index.asp 33 editor@iaeme.com

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