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1. http://www.ierek.com/press
Print ISSN: 2357-0849
International Journal on: Proceedings of Science and Technolgy
pg. 1
Performance Analysis of Enhanced Delta Sampling Algorithm for
BLE Indoor Localization
Mohd Faiz Bin Mat Daud1
, Idawaty Ahmad 1
1
Faculty of Computer Science and Information Technology,
University Putra Malaysia,
Selangor, Malaysia
David Chieng2
, Alvin Ting2
, SehChun Ng2
2
Wireless Innovation,
Mimos Berhad,Technology Park Malaysia,
Kuala Lumpur, Malaysia
Keywords
RSSI; BLE; Smoothing
Algorithm; Delta Sampling;
Indoor Localization
Abstract
Localization technologies are becoming increasingly important in support of Smart
Building applications such as in-building navigation, asset and visitor or staff
tracking applications. These Smart Building applications in turn improve user
experience, operation efficiency, security and safety of building occupants. In case
of active localization or position tracking technologies, Bluetooth Low Energy (BLE)
has gained popularity due to its accuracy and lower entry cost. BLE indoor
localization technology typically utilizes the Receive Signal Strength Indicator
(RSSI) as a basis for wireless positioning algorithms such as fingerprinting or
trilateration to compute its location. However, the accuracy of location computation
is often impacted by unstable RSSI reading especially inside the buildings. To
investigate this problem, we first evaluate the Delta Sampling algorithm proposed by
Huh et. al. (2016) on its capability to stabilize the RSSI reading using an actual
testbed. We then analyze its strength and weaknesses and propose an enhanced
algorithm. The results show that the original Delta Sampling Algorithm is capable of
stabilizing the RSSI readings but it posses some drawbacks. Further results show that
our proposed enhanced algorithm is able to mitigate some of these problems.
1. Introduction
Localization technologies are becoming increasingly important in support of Smart Building applications such as in-
building navigation, asset and visitor or staff tracking applications. These applications in turn improve user experience,
operation efficiency, security and safety of building occupants. In case of active localization or position tracking,
Bluetooth 4.0 or Bluetooth Low Energy (BLE) technology has gained popularity due to its accuracy and lower entry
cost. BLE indoor localization technology typically uses Receive Signal Strength Indicator (RSSI) as a basis for
wireless positioning algorithms such as fingerprinting or trilateration to compute its location. However, the accuracy
of this system is often impacted by unstable RSSI values at the receiver’s end due to well-known challenges such as
RF interference, multipath fading, shadowing, etc, which impact the accuracy of location estimation. As shown by
many studies, the further the receiver is away from the transmitter, the more unstable it becomes. Hence, some forms
of preprocessing is required such as applying time-series filters or smoothing algorithms to stabilize the signal before
it is can be used by localization algorithm.
The work by Huh et al. (2016) introduces a Delta Sampling algorithm which removes some of the flier points or
“noises” in the raw RSSI reading. It is believed that the algorithm can be further improved by addressing the undecided

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range of sample size and also identifying the number of invalid or false data. This algorithm can be combined with
other smoothing algorithms to further improve the result. Figure 1 shows the application of the algorithm used in
indoor localization which includes its usage in Wi-Fi and Cellular systems.
Figure 1 Flow chart for problem area
This paper proposes several improvements over the original Delta Sampling algorithm (Huh et al., 2016). A number
of additional features are proposed to counter the weakness of the Delta Sampling. Since highly fluctuating RSSI will
result in inaccurate location estimation, this study therefore only focuses on solving this problem.
2. Related work
The underlying technologies for indoor localization ranges from Wi-Fi, BLE, visible light, sound/audio, magnetic,
cellular system to mobile cameras (Dmitry, 2015). These technologies can either be implemented independently or in
combination of two or more technologies. For example, a smart phone can fuse the info from WI-FI, BLE and its
built-in sensors (Accelerometer, Magnetometer, Stepmeter) in order to achieve a better localization experience.
However in practice, Wi-Fi and BLE-based systems are more popular due to their lower entry cost and widely
accessibility. In the case of Wi-Fi, there is almost no need to build the infrastructure as Wi-Fi is available virtually
everywhere. The common techniques used to compute the position are triangulation, trilateration and fingerprinting.
Except for triangulation, most of these localization algorithms use RSSI as the base parameter for positioning
computation, thus an error in RSSI measurement will severely impact the accuracy. Unfortunately in practise, RSSI
reading of BLE is very unstable as proven in Neburka et al.(2016), Faragher et al. (2014) and Georgia et al. (2014).
As shown in Georgia (2014) and Zhou et al. (2017), to get a stable RSSI value, smoothing and/or Kalman filters are
often applied to the raw RSSI readings before the data can be used for localization.
In this paper, with aim to further stabilize the received RSSI values, the Delta Sampling algorithm proposed in Huh
et al. (2016) is implemented using an actual testbed and its strength and weaknesses are analyzed. The original is later
enhanced to address some of the weaknesses found.
3. Methodology
This experimental study consists of data collection and data analysis phases as shown in Figure 2 by adopting the
methodology similar to Huh et al. (2016). During data collection phase, the raw data was gathered and stored while
data analysis phase, the proposed Delta Sampling algorithm was executed and analyzed. The data collected phase was
done using an android application. In the data analysis phase, the proposed algorithm was implemented and analyzed
using a Python script.

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Figure 2 Experiment design
3.1. Data collection phase
In order to perform this task, we developed an Android application with BLE receiver which offered the wireless
scanner functionality. An integrated database within the app was also developed so that the scanned BLE data could
be stored. The report generator functionality was also implemented in order to convert the data into the simple form
which is easier to be analyzed. The filter algorithms were also implemented for this experiment.
A BLE beaconing hardware was used as the transmitter according to iBeacon configuration. During the experiment,
RSSI samples were collected every 100ms while moving the distance of the receiver (smartphone) from the transmitter
(beacon) from 1m to 3m. The RSSI samples were collected at each distance for 70 seconds. The data obtained from
the experiment (using the Android app) were raw RSSI which is subsequently exported to CSV format so that it can
be easily processed.
3.2. Data analysis phase
The data (RSSI samples) collected in CSV format needs to be imported into a computer so that data processing and
analysis could be performed via a Python script. The script needs to use the raw RSSI data as input. After executing
the script, the results were output in CSV form to enable graph plotting as well as computation of statistical data such
as minimum, maximum and standard deviation. The results gather were than compared with the results obtained from
Huh et al. (2016).
3.3. Experiment environment and setup
Figure 3 shows on how the data collection phase was setup. The beacon was preconfigured configured with iBeacon
standard. The configuration of this experiment is shown in Table 1 which is similar to those used in Huh et al. (2016).
Figure 3 Experiment setup for data collection phase

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Table 1. Experiment configuration.
3.4. Delta sampling algorithm
Figure 4 shows the detail mechanism in Delta Sampling Algorithm implemented in this experiment. As stated in Huh
et al. (2016), this algorithm is good on its own or can be used in combination with other algorithms. They have also
presented the result of combination with average filter. However, they the recommended values for Range and
Threshold in the said algorithm are not addressed. The subsequent experiments aim to look into this.

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Figure 4 Delta Sampling algorithm. Huh et al. (2016)
From the result shown in Figure 5, it can be seen that the overall trends are comparable to the benchmark paper (Huh
et al., 2016). However, the raw RSSI in Huh et al. (2016) is found to be much more unstable as compared to this
experiment as shown by its high standard deviation. The differences are likely owed to device and environment
heterogeneity.
Figure 5 Delta sampling algorithm. Huh et al. (2016)
From Figure 6, it is clearly shown that the Delta sampling result is much more stable than the raw data. In this
experiment, Delta sampling algorithm proven itself to be effective to solve the problem of RSSI fluctuation by
detecting the flier points and remove them from the result.
Figure 6 Raw vs delta sample
This paper further analyzes the effect of Threshold and Range parameters in Delta Sampling algorithm. As shown in
in Figure 4, the Threshold parameter is the main deciding factor in the detection of flier points while Range controls
the data sets that are to be included in the calculation. In order to further understand the impacts of Threshold and
Range parameters, further experiments were performed as shown in Figure 7 and Figure 8.
The result in Figure 7 is obtained by using different Threshold values. When a low Threshold value is used, almost all
of the Raw Data values are being considered as flier points or invalid data. As the Threshold value increases on the
other hand, the graph will become closer to its original raw data which mean it is become less sensitive in outlier
detection.
Figure 7 Comparing threshold value

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Meanwhile, further analysis on Range value as in Figure 8 reveals that smaller range means only a few past data is
taken into consideration in order to find the trend which means it only considers very recent data trend. This is quite
useful option to we only want to consider the latest data.
Figure 8 Comparing range value
3.5. Enhanced delta sampling algorithm
The original Delta Sampling algorithm is not only good in detecting sudden change in data but can also be easily
combined with other algorithms without much difficulty as stated in Huh et al. (2016). However, the weaknesses of
Delta Sampling algorithm, especially in terms of identification of suitable Range and Threshold values in real-time
must be addressed in order for it to be useful for BLE localization.
This section proposes that the Range value must be in real-time application in order to be useful. Since the beacons
are normally set at the beaconing rate or interval between 500ms to 1s only, therefore the data is much lesser compared
to this experiment where the beaconing rate is at 100ms. Moreover, data that are older than 3s are normally unwanted
as in real-time application as the user might have already moved more than 5m after 3s. The proposed solution uses
aging or decay method in finding the right Range value. In short, tagging for time is added when the Raw data is
collected by receiver. Figure 9 depicts the Enhanced Delta Sampling algorithm.
For the Threshold, the best value is equal to 1.0 or below. This is because the lower the value of mean, the more
sensitive it is to the outlier. In order to enable this, the Delta Sampling algorithm needs to be modified so that it is able
to reset itself. This is provided if it detects more than or equal to 3 flier points continuously. Figure 10 shows the result
of the enhanced algorithm with lower Threshold value and self-resetting function. The result shows that further
improvement can be attained with the enhancements. In order to get a smoother result, this algorithm can be combined
with other filtering or smoothing algorithms such as Kalman or Gaussian filter. Figure 11 shows the result of combined
enhanced Delta Sampling with Moving Average (MA) filter. It is shown that the RSSI values can be stabilized
significantly.
4. Conclusion and future work
Through this experiment, it can be concluded that the Delta Sampling algorithm can stabilize the raw RSSI by
removing the flier points in the samples. However, the results are very much depending on the choice of Threshold
and Range values. To address this uncertainty, an enhancement in the algorithm is proposed. The results show that
some further improvements can be achieved especially when combined with additional filter algorithm such as
Moving Average.

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Figure 9 Enhanced Delta Sampling algorithm
Figure 10 Raw vs Enhanced Delta Sampling algorithm

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Figure 11 Raw vs enhanced delta sampling + moving average
5. Acknowledgement
The author would like to acknowledge the support from MIMOS Wireless Innovation Lab and University Putra
Malaysia. This paper is also sponsored by Fundamental Research Grant Scheme (FRGS), University Putra Malaysia
(UPM). Project code: 08-01-15-1722FR.
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