Software engineering model based smart indoor localization system using deep-learning

TELKOMNIKA Telecommunication, Computing, Electronics and Control
Vol. 18, No. 4, August 2020, pp. 1904~1916
ISSN: 1693-6930, accredited First Grade by Kemenristekdikti, Decree No: 21/E/KPT/2018
DOI: 10.12928/TELKOMNIKA.v18i4.14318  1904
Journal homepage: http://journal.uad.ac.id/index.php/TELKOMNIKA
Software engineering model based smart indoor
localization system using deep-learning
Zainab Mohammed Resan, Muayad Sadik Croock
Computer Engineering Department, University of Technology-Iraq, Baghdad, Iraq
Article Info ABSTRACT
Article history:
Received Oct 12, 2019
Revised Feb 12, 2020
Accepted Mar 18, 2020
During the last few years, the allocation of objects or persons inside a specific
building is highly required. It is well known that the global positioning system
(GPS) cannot be adopted in indoor environment due to the lack of signals.
Therefore, it is important to discover a new way that works inside.
The proposed system uses the deep learning techniques to classify places based
on capturing images. The proposed system contains two parts: software part
and hardware part. The software part is built based on software engineering
model to increase the reliability, flexibility, and scalability. In addition,
this part, the dataset is collected using the Raspberry Pi III camera as training
and validating data set. This dataset is used as an input to the proposed deep
learning model. In the hardware part, Raspberry Pi III is used for loading
the proposed model and producing prediction results and a camera that is used
to collect the images dataset. Two wheels’ car is adopted as an object
for introducing indoor localization project. The obtained training accuracy
is 99.6% for training dataset and 100% for validating dataset.
Keywords:
CNN
Deep learning
GPS
Indoor localization
Raspberry Pi
Robotic car
Software engineering This is an open access article under the CC BY-SA license.
Corresponding Author:
Muayad Sadik Croock,
Department of Computer Engineering,
University of Technology-Iraq,
Alsinaa Street, Baghdad, Iraq.
Email: Muayad.S.Croock@uotechnology.edu.iq
1. INTRODUCTION
Localization systems are recently appeared to provide the information of location of different
objects, such as people, animals and things. Location finding is normally acquired by using global
positioning systems (GPS) for outdoor environments. Since GPS has a lack of the sight line to satellite
and a lot of other obstacles, it is not working for indoor environments [1]. The localization is very important
for the paralyzed people, and their families as it eases the live for them and displaces their concerns.
In addition, it saves time of searching for the disabled persons if they were lost for any reason.
In [2], the authors studied finger printing-based indoor localization in commodity 5-GHz Wi-Fi
networks. They proposed a system called BiLoc, which uses bi-modality deep learning for localization
in the indoor environment using off-the-shelf Wi-Fi devices. The experimental results validated the superior
performance of BiLoc over several benchmark schemes. In [3], the authors designed an indoor localization
system based on crowed sourcing, which can construct the map of WiFi radio using its data, the map
of indoor location is turned to indicative map, the authors show results that their proposed network can
minimize the varity problems caused by changing environment. In [4], a convolutional neural network
(CNN) model for indoor localization in multi-floor buildings was produced using WiFi received signal
strength values. These values have been taken from the access points in wireless LAN. The results for this

TELKOMNIKA Telecommun Comput El Control 
Software engineering model based smart indoor localization system… (Muayad Sadik Croock)
1905
model showed accuracy for training model about 100%. In [5], the authors used pervasive Wi-Fi
and attractive field data for indoor limitation. Exploratory outcomes demonstrated that profound network
models consolidating attractive field and Wi-Fi finger printings improved indoor localization precision.
The training phase of deep positioning was computationally intensive, the testing phase was fast and suitable for
real time indoor localization. In [6], the authors designed WiFi deep, which was a Wi-Fi -based indoor
fingerprinting localization system that can achieve robust and high accuracy tracking in the presence of device
heterogeneity. The system employed a model regularization to enable the network to generalize and avoid
over-fitting, leading to a more robust and stable models. The results showed that the WI deep comes with
a localization accuracy better than the state-of-the-art systems by at least 53% and 29.8% in the large and small
environments respectively. In [7], they proposed a model that learns profound learning scene acknowledgment
technique. This depends on limitation improvement utilizing the technique for exchange learning on inception
V3 network. Model feature information was added to assist in scene recognition [8]. In [9], The authors utilized
an advanced mobile phone-based movement acknowledgment for indoor restriction utilizing a convolutional
neural system. These exercises can be utilized as the tourist spots for indoor localization. These exercises
could be utilized as the tourist spots for indoor confinement. Another convolutional neural system has been
intended to become familiar with the best possible features consequently.
In this article, an indoor object localization model is proposed based on deep learning [10]
and deep CNN [11]. The proposed model contains two levels. First level, or software level, is used
for collecting the images dataset, done using raspberry pi III camera. This camera is fixed on a simple robot
of two wheels’ car. The dataset is collected and prepared by gathering and pre-handling images information.
It uses as input for the proposed model. The model is trained until it reaches to a satisfying accuracy for both
training and validating dataset. Then the model is saved. In the second level, or can be called as hardware
level, is used for gathering and controlling the electric robot car that carries on the raspberry pi III and its
camera. This camera is not only used for preparing the dataset, it is also used to take a real time image when
a certain condition happened or when an authorized user is asking about an object's place. In this case,
the raspberry pi III captures the image and send it to the proposed inside the Raspberry Pi.
Then, the proposed system waits for the response. The prediction of the model with certain accuracy is
supposed to be the prediction of the place itself [5, 12] and [13].
2. PROPOSED SOFTWARE ENGINEERING MODEL
It is well known that the provided algorithms are required to be more reliable and have all the ability
of being expanding and flexible. In this work, a software engineering model is proposed as a base stricture
for the utilized algorithm. This is to ensure the reliability, scalability and flexibility of the proposed
algorithm. The mentioned algorithm is concepts of performing the suggested method of detecting the affected
areas in the brain. Figure 1 shows the block diagram of proposed model that explains stages of method as a
work flow.
Figure 1. Block diagram of the designed software engineering model

 ISSN: 1693-6930
TELKOMNIKA Telecommun Comput El Control, Vol. 18, No. 4, August 2020: 1904 - 1916
1906
It is shown from Figure 1 that the system requirements are collected by the first round for preparing
the structure of the designed algorithm. At the second round, the initial design of the suggested algorithm
is performed based on the system requirements. Different levels of developments have been done in the third round
depending on the initial design of the proposed algorithm in iteration way. The last version of the proposed
algorithm is tested in the last round for ensuring the reliability, expandability and flexibility of the it.
3. PROPOSED SYSTEM
As mentioned earlier, the proposed system clearly contains two different phases, designed based on
the proposed software engineering model. The first one, can be called as the offline phase, which contains all
the programming algorithms starting with creating the dataset and processing it, ending with saving a
well-trained model. The second phase, or the online phase, in which, a real time images are taken, security
where localization can significantly improve security conditions the world over. Client versatility examples
and communication can be utilized to distinguish conceivable dangers that may present security risks.
Similarly, in war zone, the military can follow its advantages through a restriction framework that can
improve the general task and increment the odds of fruitful activity. The final classification is then assigned
to its estimated location. As shown in Figure 2 [2].
Figure 2. Proposed system block diagram [2]
3.1. Proposed methodology
In this section, the dataset is collected and prepared. It also introduces the CNN model algorithm
and training procedure. Moreover, this section provides the website design that ia based on the proposed
algorithm. At the other side, the object localization system is introduced with the related proposed algorithms
and flowcharts. The proposed overall system is started with the software part and followed by the hardware
part as shown in Figure 3. The following steps describe the method used to build the model briefly:
− Starting the collection of a dataset for training the model. This dataset must cover all the desired areas that
is wanted to be added to the classification model.
− Preparing this dataset by adding the necessary operations, such as cropping the images to be all of the same
size. Moreover, adding a filliped copy of the same images for increasing their number in each class.
− Designing the CNN layers of the model with Keras library. Then, the system trains the model until
reaching the desired accuracy and save them in the Raspberry Pi for later use.
− The software part ends with creating a simple website that contains the controlling buttons on
the hardware parts. This website is built by using the HTML, PHP, CSS and python scripts.
− The first step in the hardware part contains gathering and building a simple prototype of a robotic two
wheels’ car with its battery and motor driver.
− Combining the Raspberry Pi and related accessories that include camera, LEDs, breadboard, voltage step
regulator, two ultrasonic sensors. All these components are connected together and placed in a black box
for arrangement.

1907
− The Raspberry Pi is the core of controlling the hardware part. It is connected by WiFi to the website.
When the search button is clicked in the web, a certain procedure is followed by moving the car
and capturing real time images. This procedure is introduced in details in the next sections.
− The last step is finding an appropriate classification accuracy and assigning it to its class that is related to
its corresponding classified indoor location.
Figure 3. The proposed system methodology flowchart
3.1.1. Dataset collecting
The dataset is chosen very carefully, with notifying the angle of capturing the images and
the number of images that covers the included areas that are needed to be classified. In these images,
different situations and different positions are taken in consideration, as shown in Table 1. In this part,
the data set collection and choosing are discussed. Moreover, as well as the preprocessing operations, such as
cutting, zooming, and shearing are also introduced. The camera is located about 20 cm from ground on the
robotic car with vertical angle of 15 degrees. After all datasets are collected, each class is assigned to one
room. This is to reduce the complexity as it is not sensory to include all walls in the dataset because some
images in the collected dataset are taken in a way that covers more than one corner inside the room.
Table 1. Overview of the collected dataset
Location (Wall)
No.
Corresponding
Room Name
Number of
Training dataset
Number of
Validating dataset
Size Of Images
(width x height)
Location 1 Meeting Room 264 60 480 x 270
Location 2 Meeting Room 268 61 480 x 270
Location 3 Eating Room 255 59 480 x 270
Location 5 Admin Room 208 54 480 x 270
3.1.2. Dataset preparing
The next step is to prepare these datasets to be ready for use in the training model. The data
augmentation is used as a tool that is available in any deep learning toolbox. It is done with Keras library

 ISSN: 1693-6930
1908
using a class called image data generator [14]. This class applies a random invariance, such as images
cropping, scaling, shearing, and flipping as shown in Table 2.
Table 2. Overview of the used data augmentation techniques
Name Value
Scaling (1. /255)
Zooming 20%
Flipping Horizontal
Crops 224*224
3.1.3. Model training
The model is trained from scratch, because there is no pre-trained model used for initialization.
The main source of data is the images available with Raspberry Pi camera. The proposed CNN with deep
learning is designed to train a number of images and test others [15] and [16]. Each entered image
to the CNN model passes through many levels of convolution layers. Some of them contains a filters
and pooling, while the others include fully connected layers. Finally, such an image passes through a soft
max filter [17]. The following steps illustrate the building of the CNN model:
− In the first convolutional layer, 20 filters of size 5x5 pixels are applied to the input image of size
224x224, followed by a rectified linear unit (ReLU). A max pooling layer is used for taking the maximal
value of 2x2 regions with strides of 2x2.
− The output of the previous layer is then processed by the second convolutional layer. It contains 50 filters
of a size 5x5 pixels. Again, that is followed by a ReLU and a max pooling layer.
− This convolutional layer's output is passed to a fully connected layer and contains 500 neurons, followed
by a ReLU.
− At last, the yield of the last completely associated layer is nourished to a soft max layer that appoints a
likelihood for each class of 8 classes.
Then it assigns the class number to its corresponding location in one of the three rooms mentioned earlier,
with a probability goes from 0 to 100%. As shown in Figure 4.
Training the CNN model is the next step, see Table 3, it takes a time ranges from 88 to 94 second
for each epoch. This is because the deep learning model training requires a high ability GPU. The proposed
model is trained online in Google Colab, which gives a free GPU on a free cloud service for developing
applications of deep learning [18] and [19]. This is done by loading the dataset to the Google drive for
opening the Google Colab and making the model. Training tries are done with different number of classes
from 4 to 8, in both laptop and online on Google Colab, but there is no need to mention the results of these
tries. The important thing to know is that, it has been reached to a point in which only small number
of epochs is enough to train this model with satisfying results. Therefore, only 5 epochs are taken each
of which produces different accuracy and loss metrics for both training and validating dataset.
Figure 4. CNN architecture of the proposed model
Table 3. Model training results corresponding to each epoch
Epochs
Time in
seconds
Training Validation
Accuracy Loss Accuracy Loss
1 94s 0.7923 0.7252 0.9146 0.2176
2 89s 0.9836 0.0785 1.0000 0.0301
3 90s 0.9960 0.0276 1.0000 0.0180
4 88s 0.9965 0.0170 1.0000 0.0096
5 90s 0.9965 0.0152 1.0000 0.0145

1909
3.1.4. Website design
After the model had been trained and reached to a satisfying accuracy results, which was 99.6% for
training dataset and 100% for validation dataset. This model is saved to the RPi III memory to be used in
the hardware level. The saved model cannot be used directly for predictions, because it is saved as a python
script. So, an interfacing mechanism must be added for the user to be able to contact with the model in some
way. Figure 5 shows a flowchart for the designed website, which contains the steps that must be followed to
operate this system.
Figure 5. Website flowcharts
The following steps are described in the next lines:
− A simple web page is created inside the RPi. For security reasons, this web page contains a login button at
the starting home page.
− Without login, the normal user cannot see more than simple description on the project and the copy rights
and the year of modification.
− When the user fills up the login form, and he/she is authorized to enter to this web. The web then moves
the user to the indoor localizer page.
− The indoor localization page contains number of persons listed, and followed by two buttons; the search
and showing buttons.
− The user should select one object at a time to search for its place. In this paper, only one object is
available but for the future work, this project can cover large number of them.
− After choosing the person's name, the user should click on search location button, which contains
the hardware controlling and model prediction results.
− The other button, location button, displays the captured images that had been interred to the model
for testing reasons.
3.2. Hardware integration level
This section clarifies how the equipment parts are gathered, associated and utilized together as
a total working framework for indoor item restriction robot. It contains two levels, the initial segment
is connected for structure the vehicle segments, while the subsequent level is in charge of the controlling
system for the vehicle and the rest segments.

 ISSN: 1693-6930
1910
3.2.1. Car parts gathering and arrangement
The principle segments utilized for setting up the mechanical vehicle are appeared in Figure 6.
These parts are Raspberry Pi III model B, the L298N motor driver, the ultrasonic sensor, the voltage steps
down regulator, the Li-Po battery, and the RPi camera [20-25].
Figure 6. The hardware requirement for car module
3.2.2 Robotic car movement and obstacles detection
Two ultrasonic sensors, fixed at the front and back of the car, are used to detect the obstacles in
the car's passage way. The detection is accomplished by measuring the distance between the car and the faced
obstacles. It is important to note that thel software algorithms are designed based on the proposed software
engineering model. The proposed algorithm of car movement and obstacles detection is shown as a flowchart
in Figure 7 and the next lines describe it in details:
− For forward movement, the RPi fully stops the car, if the measured distance is smaller than or equal to
(100 cm). The car is kept waiting for 5 seconds (secs).
− After 5 secs, again it checks if the measured distance is increased. This means that the obstacles are
moved, then the Raspberry Pi orders the car to drive again, until the measured distance is smaller than or
equal to (100 cm).
− In case of the 5 seconds are passed and the distance still the same, this means that this obstacle may be
a wall or any stable object.
− Now, the car is stopped, and it starts the procedure of capturing and classifying the images.
− If the measured distance is smaller than (80 cm), this means that the robotic car is too close to the wall,
is to take an image in a clear way the car must drive back at least for 20 cm, which can be equipped by
moving the car backward for (1 sec).
− For backward movement, The Raspberry Pi fully stops the car, if the desired time for backward
movement is passed, or if the measured distance is below than or equal to (30 cm).
3.2.3. Classifying images procedure
After the robotic car has been reached to the wall successfully, the Raspberry Pi starts in capturing
images. This procedure is shown in Figure 8 as a flowchart that explains the proposed algorithm. The image
classification algorithm is proposed based on the software engineering model. The following lines are
presented to describe this algorithm in details:
− The first step in this procedure is to load the previously saved model in the RPi as a python script, by a
special PHP formula in the website. After the user clicked on the search location button, and the robotic
car found a wall (or as it thinks, it is a wall).
− The RPi orders the camera to take the first image and save it in gallery file inside it.
− Then it turns the car to the left with a 15 degree for changing the image angle, and again the camera takes
another image and save it with the first one.
− Again, the RPi turns the car back to its original location by turning it to the right with the same
degree (15).
− Then it moves the car to the right with the same mentioned degree, for taking the last image in another
different corner.
− At this point the system has three different images for distinct corners. These images enter to the loaded
model as real time test images.

1911
− Each image is passed through the loaded model and reach to a certain classification accuracy.
The obtained three accuracies form three images are compared with each other. The maximum accuracy
of these accuracies is chosen as the actual result of the classification.
− In the proposed algorithm, a desired accuracy is considered to be 70%.
− If the resulting image classification accuracy is equal or higher than this desired accuracy,
then the classification is accepted and the image is an actual wall. Then this image should be assigned
to the corresponding room and takes it as the final result. Otherwise, the RPi ordered the car
− to turn to another wall. This is done by moving it to the left with 90 degrees. Then the procedure is started
from the beginning, starting from robotic car movement and obstacles detection.
Figure 7. Robotic car movement algorithm

 ISSN: 1693-6930
1912
Figure 8. Capturing and classifying images algorithm
4. THE EXPERMENTAL RESULTS
To determine the efficiency of the proposed system, many experiments are introduced on different
case studies. As previously mentioned, the system contains a website, in which, the user interfaces with
the proposed algorithms. In this section, different case studies of different situations and different locations
are discussed in details. The first step in all these case studies is to login to the system through the login page,
for authority confirming, so this step is introduced first followed by the case studies.
4.1. Authorization checking
The user should be authorized to be able to enter to the page of localization in the website.
Otherwise, the user cannot do anything with website except seeing the information that are shown in
the home page. Therefore, and as a beginning in the localization procedure, any experiment or case study in
the proposed system passes through the following lines:
− In the starting of the website, a login button is appeared. The user should click on it to view the login form.
It has been noticed that there is no sign in button. For security and privacy purposes, no one can sign in to
the system without authorization.

1913
− After filling the user name and password with an authorized account, and then clicking on the login button.
If the account is authorized, the user should see the localization page, else the website will return to
the home page with login error message.
− In this step, the user must select one person from the introduced list, in the proposed system only one person
is active. But in the future, other persons will be active also. It should be noticed that only one person should
be selected at any time.
− After choosing the name of the person to search for, the user should click on the search location button.
Then the procedure of controlling the robotic car by the Raspberry Pi, checking for obstacles, and capturing
the images that passes through different algorithms too. The evaluation of them will be introduced in
the following sections with different case studies.
− The final results are shown as a text in the website.
− If the user clicked on the show location button, the last three taken images are shown in the gallery webpage.
4.2. Case study one
The first experiment, which can be named as the normal case study with distance less than or equal
to 100 cm is introduced. Here, after the user selects the required person to search for, the following steps
are followed:
a. The Raspberry Pi orders the robotic car to move forward. The two blue LEDs are on, indicate the forward
moving, as shown in Figure 9 (a).
b. The car moves forward and checks the distance, when it became less than or equal to 100 cm and more
than 80 cm, as the algorithm says, the Raspberry Pi orders the car to stop and wait for 5 sec. Then, again
the distance is measured, if it changes, the car continues to move forward and wait the distance to become
less than or equal to 100cm as shown in Figure 9 (b). Otherwise and as shown in Figure 10,
the Raspberry Pi starts capturing three test images in three different angles, with 15 degrees between each
corner. Then the car returns to its original location.
c. These images are sent to the website to be used inside the program of classification. The classification
procedure takes about 3-5 sec. The results are shown in the web page as a text according to Figure 11.
(a) (b)
Figure 9. (a) The car is moving forward, (b) The nearest wall is on 90 cm distance
Figure 10. The procedure of capturing images

 ISSN: 1693-6930
1914
Figure 11. The final localization results of the first case study
4.3. Case study two
The second experiment, which can be named as the closest distance case study with distance less
than 80 cm, is introduced. After the user selects the required person to search for, the following steps are
followed:
a. The Raspberry Pi orders the car (prototype) to move backward for one second, to increase the distance a
little bit for not being too close to the wall. The two rear Red LEDs are on, indicating for start moving
backward.
b. The Raspberry Pi orders the car to stop and start capturing three test images in three different angles.
c. These images are sent to the website to be used inside the program of classification. The classification
procedure takes 3-5 sec of time. The results are shown in the web as a text.
4.4. Case study three
The third experiment can be named as the forward and backward obstacles detection. First,
the forward obstacles will be discussed. As mentioned in the first experiment, (For forward obstacles detection)
if the distance is less than or equal to 100 cm, the car waits for 5 sec, then the following steps are occurred:
a. Check the distance, if it is increased, which means that the obstacle is moved, the car continues until it
reaches again to a distance less than or equal to 100.
b. Otherwise, if the distance is still less than 100cm, which means that the obstacles are not moving, the car
stops and completes the capturing and classifications procedures. This is because the obstacle maybe the
wall itself.
c. The same procedure is done for the backward moving. When the car is moving backward and the distance
is less than 30 cm, it will wait for 5 secs, then the following steps are occurred:
− The RPi checks the distance, if it is increased, which means that the obstacle is moved, the car
continues until it finishes the desired backward moving time.
− Else, the distance is still less than or equal 30cm, which means that the obstacles are not moving,
the car stops and completes the capturing and classifications procedures.
4.5. Case study four
This experiment can be called the wrong classification results handling experiment. This case
study is the same as the normal experiment of the case study one, except that the probability
of the classification is under the desired probability, which is 70%, as mentioned in chapter three. In addition,
this case study considers an image distortion, that caused by unmoved obstacles, which indeed gives a wrong
classification probability. In this case, the Raspberry Pi orders the car to turn to the left with a 90 degree as
shown in Figure 12 (a). This makes the car moves to the next wall and then the total procedure of case study
one is repeated, moving, checking for obstacles, as mentioned early. Then the car is capturing three images as
shown in Figure 12, and classifying them until the probability is improved. The results of this experiment
have shown that the classified images are taken in the Meeting room with probability of 99%. It has been
noticed that only the final results are displayed on the website as shown in Figure 12 (b).

1915
(a) (b)
Figure 12. (a) Moving the car to the next wall, (b) The final classification results of the case study four
4.6. Case study five
This case takes the probability of putting new objects, which were not trained in the trained
dataset. This experiment shows that the designed system can classify the walls to its corresponding room
even if new objects are added like new chair, table, or new curtains. The same procedure of section B
is executed here, with the addition of some obstacles as mentioned in section D. Moreover, a new curtain
is added to the wall as shown in Figure 13 (a). This experiment is introduced in the admin room,
and the classification results have shown that the probability of the images to be in the admin room was 82%.
This proves that the proposed system has a great ability to classify any building as shown in Figure 13 (b).
The results of implementing the proposed system for different room classifications are listed in
Table 4. It has been shown that many predictions are right. Many experiments had been introduced for this
project, covering the eight walls, each experiment gave a different probability result. The results have shown
that from every ten tested locations only one location is false. This means that the project false predictions
are less than or equal to 10% which is acceptable loss percentage for a system starts with dataset
from scratch.
(a) (b)
Figure 13. (a) Adding a new object, (b) The final classification results
Table 4. The final probability results for different experiments
Experiments Origin location Estimated location Probability
1 Admin Room Admin Room 99%
2 Admin Room Admin Room 82%
3 Eating Room Eating Room 88%
4 Eating Room Eating Room 91%
5 Meeting Room Meeting Room 99%
6 Meeting Room Meeting Room 86%

 ISSN: 1693-6930
1916
5. CONCLUSION
An affordable indoor localization system based on deep learning was introduced and implemented to
classify locations inside a building. The proposed system consisted of two parts: the classification algorithms
and website building part (software part as well as the mini-robotic car (object) part (hardware part).
The proposed algorithms were designed in deep learning technology with Keras library and CNN. A training
model was created and trained, then the model was saved inside the Raspberry Pi SD card as a python script.
The website was designed with HTML and CSS with the help of PHP for inserting the python scripts easily.
Different experiments were introduced in order to test the proposed system. The results of these experiments
have shown that it is easier to control the car by using only the Raspberry Pi control signals with high
performance in term of accuracy and precision.
REFERENCES
[1] Maryland NRCS, ''Introduction to Global Positioning Systems (GPS),'' USDA, August 2007.
[2] Xuyu Wang, Lingjun Gao and Shiwen Mao, “BiLoc: Bi-Modal Deep Learning for Indoor Localization with Commodity
5GHz WiFi,” IEEE Access Special Section on Cooperative and Intelligent Sensing, vol. 5, pp. 4209–4220, 2017.
[3] Baoding Zhou, Qingquan Li, Qingzhou Mao, and Wei Tu, “A Robust Crowd Sourcing-Based Indoor Localization
System,” Sensors, vol. 17, no. 4, pp. 1-16, 2017
[4] Mai Ibrahim, Marwan Torki and Mustafa ElNainay, “CNN based Indoor Localization using RSS Time-Series,” IEEE
Symposium on Computers and Communications (ISCC), pp. 1044-1049, 2018.
[5] Wei Zhang, Rahul Sengupta, John Fodero and Xiaolin Li, “Deep Positioning: Intelligent Fusion of Pervasive
Magnetic Field and WiFi Fingerprinting for Smartphone Indoor Localization via Deep Learning,” 16th IEEE
International Conference on Machine Learning and Applications, pp. 1-5, 2017.
[6] Baoding Zhou, Jun Yang and Qingquan Li, “Smartphone-Based Activity Recognition for Indoor Localization Using a
Convolutional Neural Network,” Sensors, vol. 19, no. 3, pp. 1-15, 2019.
[7] Wei Guo, Ran Wu, Yanhua Chen and Xinyan Zhu, “Deep Learning Scene Recognition Method Based on Localization
Enhancement,” Sensors, Vol. 18, no. 10, pp. 1-20, 2018.
[8] Tang P., Wang H., Kwong S., “G-MS2F: GoogLeNet Based Multi-Stage Feature Fusion of Deep CNN for Scene
Recognition,” Neurocomputing, vol 225, pp. 188–197, 2017.
[9] Moustafa Abbas, Moustafa Elhamshary, Hamada Rizk, Moustafa Youssef and Marwan Torki, “WiDeep: WiFi-Based
Accurate and Robust Indoor Localization System using Deep Learning,” 2019 IEEE International Conference on
Pervasive Computing and Communications (Percom), pp. 1-10, 2019.
[10] B. Javidi, “Image Recognition and Classification: Algorithms, Systems, and Applications,” CRC press, 2002.
[11] Yuan Y., Mou L., Lu X., “Scene Recognition by Manifold Regularized Deep Learning Architecture,” IEEE
Transaction on Neural Network and Learning Systems, vol. 26, no. 10, pp. 2222–2233, 2015.
[12] J. Xiong, K. Sundaresan, and K. Jamieson, “ToneTrack: Leveraging Frequency-Agile Radios for Time-Based Indoor
Wireless Localization,” 21st
Annual International Conference on Mobile Computing and Networking, pp. 1-13, 2015.
[13] J. Haverinen and A. Kemppainen, “Global Indoor Self-Localization Based on the Ambient Magnetic Field,” Robotic
Autonomious System, vol. 57, no. 10, pp. 1028-1035, 2009.
[14] The PiHut, “Raspberry pi Camera,” 2019. [Online]. Available: https:// www.thepihut.com /products/raspberry-pi-
camera-module. Accessed: 19 May 2019.
[15] Nguyen L. D., Lin D., Lin Z., Cao J., “Deep CNNs for Microscopic Image Classification by Exploiting Transfer
Learning and Feature Concatenation,” IEEE International Symposium on Circuitsand Systems (ISCAS), pp. 1–5, 2018.
[16] Zhou B., Lapedriza A., Xiao J., Torralba A., Oliva A., “Learning Deep Features for Scene Recognition Using Places
Database,” 27th
International Conference on Neural Information Processing Systems, pp. 487–495, 2014.
[17] François Chollet, ''Deep Learning with Python,'' Manning, 2018.
[18] Jordi Mas and Carles Matue, "Introduction to Web Application Development,” Eureca Media, 2015.
[19] Martin Thoma, “Analysis and Optimization of Convolutional Neural Network Architectures”, arXiv:1707.09725, 2017.
[20] Tan and Clinton, "Embedded system application development for the raspberry pi", Nanyang Technological
University, 2016.
[21] Yunus Çelik, Mahmut Altun, and Mahit Güneş, "Color Based Moving Object Tracking with an Active Camera Using
Motion Information," IEEE Artificial Intelligence and Data Processing Symposium (IDAP), pp. 1-4, 2017.
[22] Liuliu Yin, Fang Wang, Sen Han, Yuchen Li, Hao Sun, Qingjie Lu, Cheng Yang, and Quanzhao Wang, "Application
of Drive Circuit Based on L298N in Direct Current Motor Speed Control System," Proceedings Volume 10153,
Advanced Laser Manufacturing Technology, 2016. doi: https://doi.org/10.1117/12.2246555.
[23] Mohit Rane and Kalpan Mehta, "Potential Applications of Ultrasonic Sensor," International Journal of Engineering
and Computer Science, vol. 6, no. 3, pp. 20583-20586, 2017.
[24] Jang, Youngcheol, and Eunok Kwak, "Lithium Polymer Battery" U.S. Patent No. 9,065,083, 2015.
[25] Muayad Sadik Croock, Salih Al-Qaraawi and Rawan Ali Taban, '' Gaze Direction based Mobile Application for
Quadriplegia Wheelchair Control System,'' International Journal of Advanced Computer Science and Applications,
vol. 9, no. 5, pp. 415-426, 2018.

Software engineering model based smart indoor localization system using deep-learning

More Related Content

What's hot (20)

Similar to Software engineering model based smart indoor localization system using deep-learning (20)

More from TELKOMNIKA JOURNAL (20)

Recently uploaded (20)

Software engineering model based smart indoor localization system using deep-learning