Wifi Positioning System (WPS)

Executive Summary

The alternative of using a GPS for indoor navigation is application of WIFI positioning (Toloza, Acosta & Kornuta, 2013). This is based on the fact that GPS signal inside a building is quite exposed to different barriers since it cannot penetrate through concrete walls and other barriers.  The advantage for using WIFI positioning system for indoor navigation is that most of the buildings in the contemporary world of technology are installed with WIFI connection (Toloza, Acosta & Kornuta, 2013). Thus, this makes it effortless to locate WIFI access points with the building. Different factors are used to locate the position of the user within the building. These factors include access points, the strength of the WIFI signals within the building, and the media access control (MAC) (Agarwal, Singh & Sharma, 2014). However, in order for the user to access indoor positioning services, he or she must have a mobile app installed on the smartphone. The app will help in calculating the current position of the user using the data obtained from the WIFI connection (Agarwal, Singh & Sharma, 2014). The accuracy of using WIFI positioning system is determined by several factors which include the number of the networks available, shielding through walls, and reflections for instance in corridors among others. Typically, the accuracy of the system varies from 5 to 15 meters with regard to the preconditions of the building (Sapiezynski, Stopczynski, Gatej & Lehmann, 2015)). However, the accuracy can be improved using the sensor fusion where sensors are installed in the smartphone to enhance the process of locating the user. Generally, the major advantage of using WIFI positioning system is based on the fact that it is possible to identify the current floor level that the user is (Makki, Siddig, Saad & Bleakley, 2015). Therefore, this paper will access the functioning of the mobile app introduced in the project together with how it will contribute to indoor positioning t the target audience which will include students and people in commercial buildings.

Key Words; WIFI positioning system, access points, GPS, mobile app, accuracy      

Introduction

It is factual that with the influx of GPS, location-based mobile apps and services have increased significantly making it easy to trace where one is just in case one does not know the place. However, it appears that GPS fails once a person is inside the building (Guo, 2014). The reason why GPS tends to fail inside a building is based on the fact that signals are carried by waves at a frequency that cannot enable them to pass through solid objects. GPS appliances receive signals from a satellite where most of the times the frequency does not penetrate some solid barriers (Yuanfeng, Dongkai, Huilin & Chundi, 2016). Additionally, when the GPS signal is inside the building, it is exposed to a variety of barriers and interferences thus making it hard to locate the position of an individual. This is the reason why one ought to use an alternative method to site the location while inside the building. Therefore in this case, the alternative is WiFi positioning system (WPS). WiFi positioning system (WPS) is used in indoor positioning where specific data is transmitted to WiFi access point (Woo, Jeong, et al, 2011). Unlike GPA, WiFi is effective in that it sufficient, it provides back channel and in indoor positioning, GPS is not used.   ‘Received Signal Strength Indication’ and ‘Media Access Control’ are used together with fingerprinting method in helping the app to use a database and calculate the location (Mautz, 2008).  In order to get the accuracy in indoor positioning, factors such as available networks, walls shielding and more are considered.   In addition, accuracy is measured from 5-15 meters and sensor fusion can help to view the accuracy and unlike GPA, WiFi helps in detecting the floor level. Generally, this paper will focus on how the WiFi positioning system works together with the benefit of the WiFi signal connection.

It is perceptible that indoor navigation using WiFi is being significantly used in the contemporary number of projects (Yang & Shao, 2015). The reason is because there a wide variety of existing WiFi signals that can be used for indoor navigation. Furthermore, positioning requires the user only to enable WiFi connectivity in the phone without login requirements. Precisely, WiFi positioning system enables the phone to calculate the position of the user by combining the “fingerprint” of the available WiFi hotspot together with the data taken from accelerometers of the phone (Yang & Shao, 2015). Using the signal strength and the distinctive IDs of all the existing WiFi hotspot, the WiFi positioning system using the compass of the device is able to site the location of the user. These things are matched against data acquired from the area accessed through the internet or from data contained in the device. The estimation of the location can be effectuated if the device moves to some extent because WiFi positioning system’s algorithms is able to gather numerous fingerprints from the available WiFi hotspots (Van Haute, De Poorter et al, 2016). The accuracy is refined by the compass and accelerometer signals which capture the footsteps of the user as the device is moving around. However, it appears that the WiFi positioning system requires similar data to be gathered from the building ahead of providing location fixes of the same building. This means that another person with the same but special app should walk around the building several times (Van Haute, De Poorter et al, 2016). This helps the system redevelop the navigation area by capturing the patterns of WiFi fingerprints. As a result, the device is able to trace the map of the place so that the WiFi positioning system can obtain that data to spot the location of the user in the building (Petrenko, Sizo et al, 2014).

Under the basis of WiFi connection, it is factual that every WiFi access point starting from router, customer hotspot to internet- enabled point of sale system, transmits certain definite data (Petrenko, Sizo et al, 2014). Thus for WiFi positioning system, through Received Signal Strength Indication (RSSI) and Media Access Control (MAC) address, the mobile app is able to calculate the current position of the device user. This demands for a database with information regarding the specific location which can be compared with while spotting the position of the user.  This process is known as fingerprinting. Because of the technical restrictions of the operating systems of mobile devices, fingerprinting functions only with android operating system. Therefore, it is undeniable that WiFi positioning system cannot work with iPhone operating system (iOS). Under the Received Signal Strength Indication (RSSI), the device is able to obtain various values of signal strength within the building. RSSI is a scale that is used to calculate power levels of the signal received by the device from a wireless network. Different positions have different signal strengths which make up a map of the building (Pathak, Palaskar, Palkar & Tawai, 2014). In deriving the position of the user in the building, the map is used to compare the signal power values recorded by the portable device and by comparing with the values in the map, the position of the user is identified (Helhel & Kocakusak, 2016). Precisely, the positioning using fingerprinting is done using two ways which include using neighbors where distance between RSSI reading points and reference points fingerprint are used to determine the position of the device, and use of statistical data of the fingerprint to estimate the position (Helhel & Kocakusak, 2016).

Generally, use of strength of network signal to calculate the distance of the user is called WiFi trilateration technique (Bobescu & Marian, 2015). This technique is broken down to Spherical Trilateration Algorithm which utilizes different factors such as frequency of the signal, address and actual coordinates of the access points, and signal strength among others (Bobescu & Marian, 2015). As aforementioned, the signal strength as received by the device can be used to determine the distance between the device and source of the signal (Sapiezynski, Stopczynski, Gatej & Lehmann, 2015). This means there has to be several access points within the building which can be used to determine the location that the mobile device is receiving the signal. The strength of the signal at these points varies exponentially which depend on the distance between the device and transmitter (Sapiezynski, Stopczynski, Gatej & Lehmann, 2015). Thus, this dependence is taken as a function of distance. The estimated distance using the strength of the signal is presented with a circle which represents the access point. The intersection of three circles provides the location of the device user.

Literature Review       

With the widespread encroachment of mobile internet which has resulted to extensive use of internet services, the demand on indoor positioning has increased tremendously (Rui, Qiang, Changzhen, & Jingfeng, 2015). This has resulted to development of different kinds of apps that support navigation inside the building where GPS appears to be ineffective.  Citing from the fact that GPS does not work inside a building, researchers have established ways of using WiFi networks to determine the position of an individual within the building (Rui, Qiang, Changzhen, & Jingfeng, 2015). WiFi indoor positioning has been successful because most of the buildings in the contemporary technological world are installed with WiFi network connections. In this case, the ordinary WiFi indoor positioning algorithm that uses fingerprinting is more effective since it does not depend on access points to estimate the location of the device user (Rui, Qiang, Changzhen, & Jingfeng, 2015).

However, it is factual that fingerprinting approach appears to face two major challenges regarding its application. One of the drawbacks includes time consumption during offline acquisition process to create a map of the building. The other challenge is based on the inaccuracy associated with fingerprinting approach (Rui, Qiang, Changzhen, & Jingfeng, 2015). The accuracy of the approach can only be determined at 2-5 meters. Precisely, indoor positioning can be broken down into three groups which include proximity algorithm, scene analysis algorithm and triangulation algorithm (Rui, Qiang, Changzhen, & Jingfeng, 2015). For proximity algorithm, the estimation of device position is done determining the relationship between WiFi checkpoints and target position in the building. As the device receives the signal from different access points, the position of the access point that exhibit strongest signal is regarded as the position of the device. On the other hand, triangulation algorithm uses principle of triangle properties where when the device receives signals from different access points, time and angle of arrival together with signal strength are used to estimate the distance between the device and access points (Makki, Siddig, Saad & Bleakley, 2015)). Lastly, scene analysis algorithm involves collection of fingerprints in the building which are used to estimate the position of the device using online data/measurements and the location fingerprints. Generally, these are the principles that are applied in development of WiFi positioning system.

Benefits of the App

• The benefits of the app using WiFi positioning system for the indoor positioning include the following;
• It promotes indoor positioning without using GPS
• It detects the level of the floor
• It estimates the position of the user within a large range – up to 100 meters (Kennedy, Kingsbury, et al, 2016)
• It can use any existing WiFi network
• Clients will enjoy a back channel

 The app is applicable in building navigation where GPS is basically inapplicable. For instance, navigation in complex buildings such as shopping malls, railway stations, exhibition halls, airports museums, hospitals, and industry buildings are some of the most potential places that WiFi positioning system app can be used (Zheng, Chen, Sun & Chen, 2016). Generally, the indoor navigation app enhances user’s services and minimizes the complexity of navigating in an unfamiliar building (Van Haute, De Poorter et al, 2016). However, the user is required to have a mobile device with android operating system. Some of the cases that the app can be used included while analyzing the walking routes and flows of the visitors, tracking the movement of goods, animals, vehicles and machinery, and navigating in buildings (Kennedy, Kingsbury, et al, 2016). It is noteworthy that to tack the movement of objects, WiFi tags ought to be instilled in the objects in order to be able to follow them (Helhel & Kocakusak, 2016).     

Functionality of the App

It is factual that the major function of the app will be displaying symbolic identifiers but not the geographic coordinates of the building. These symbolic identifiers that will be used by the app include room number and name. Furthermore, the position of the user will be represented by a red dot that will be displayed on the screen of the phone displaying the current location of the user on the floor plan of the building. The rooms are numbered and named with regard to the floor that the user is currently in as indicated by figure 1 and 2.

The user will be required to add the photo of the floor plan of the building in order to be able to locate him or herself within the building. In this case, the mobile app will use the WIFI scan to determine the location of the user using fingerprinting approach. After the scan, the app will both indicate the position of the user on the floor plan and name the location at the top of the plan as shown by figure 3.

The advantages of the app are based on the use of WIFI positioning system that works without GPS which means that it will not be affected by barriers such as concrete walls and others. Therefore, it will be easy to use the existing WIFI infrastructure which is sufficient in modern world and it has a back channel to the user. The other benefit of the app will be that it will have a large range of up to 150 meters and will be able to detect the floor level. Therefore, for the users, it will be easy to navigate all over the building thus making reaching the destination faster and effectively. Citing from the fact that the app will be accessible using mobile devices (except iOS devices), users will only be required to connect to the immediate WIFI signal and navigate the building without complications (Xi, Mingxing, Zhongwen, Naijun, Zhongwei & Jing, 2016). For the students, the app will be effective in ensuring that no time is wasted especially when one is not sure about the floor level of the destination in the school building. It is noteworthy that the app will promote time effectiveness which is a very imperative value in a learning institution.

Conclusion  

Citing from the fact that GPS signals fail within a building because it is affected by solid barriers, the most effective alternative is use of WIFI indoor positioning system. Using WiFi connection, it is possible to locate the position of the user within the building regardless of different floor levels. Using the fingerprinting method, a mobile app is able to calculate the location of the user using the data regarding the place obtained and stored in a database. The effectiveness of WiFi positioning system is based on the fact that it depends on WiFi connectivity which is readily available in most of the places and buildings. Therefore, using the WiFi signal the app obtains any relevant data regarding the place which is used in the estimation of the location of the user. Using that data, the app assesses the access points and through the strength of the signal, it identifies the location of the user. Thus, based on that concept, the app will be beneficial to the users in different ways such as identifying the location of the user without using GPS, detecting the floor level, estimating the position of the user within a large range of more than 100 meters, and offering a back channel to the user. Lastly but not least, the user will be able to easily identify the current position since the location will be displayed through symbolic identifiers rather than geographic coordinates. It is noteworthy that the app will enhance time effectiveness as the user is able to identify the destination prior to navigating the building.

Reference

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Appendix

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