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Evaluation of the reliability of RSSI for indoor localization

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In wireless sensor networks, nodes can be static or mobile, depending on the application requirements. Dealing with mobility can pose some formidable challenges in protocol design, particularly, at the link and network layers. These difficulties require mobility adaption algorithms to efficiently localize mobile nodes and predict the quality of link that can be established with these nodes. An off the shelf development platform that uses Radio Signal Strength Indication (RSSI) is mostly selected as the sensor localization method, especially in the indoor environment. Despite this, not much research work has been done to practically demonstrate the reliability of RSSI for indoor localization. Therefore, in this paper, we aim to calibrate and map RSSI to distance by doing a series of experiments. The result shows that the RSSI technology gives an unacceptable high error and thus is not reliable for the indoor sensor localization.
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Evaluation of the reliability of RSSI for Indoor Localization
Qian Dong and Waltenegus Dargie
Chair of Computer Networks, Faculty of Computer Science, Technical University of Dresden, Germany, 01062
Email: qian.dong, waltenegus.dargie@tu-dresden.de
Abstract—In wireless sensor networks, nodes can be static
or mobile, depending on the application requirements. Dealing
with mobility can pose some formidable challenges in protocol
design, particularly, at the link and network layers. These
difficulties require mobility adaption algorithms to efficiently
localize mobile nodes and predict the quality of link that can
be established with these nodes. An off the shelf development
platform that uses Radio Signal Strength Indication (RSSI) is
mostly selected as the sensor localization method, especially in
the indoor environment. Despite this, not much research work
has been done to practically demonstrate the reliability of RSSI
for the indoor localization. Therefore, in this paper, we tired
to calibrate and map RSSI to distance by doing a series of
experiments. The result shows that the RSSI technology gives
an unacceptable high error and thus is not reliable for the
indoor sensor localization.
Keywords-distance; localization; RSSI; wireless sensor net-
works;
I. INTRODUCTION
Several applications in wireless sensor networks require
sensor localization technologies. Some of these applications
use location information to infer the activity of mobile ob-
jects, animals, or human beings [7]. For example, biomedical
sensor nodes can be attached to the bodies of patients [6]
and nurses [5] to monitor their activities; workers in disaster
recovery scenes [18] and oil extraction and refinery areas
[4] can carry sensing devices to avoid dangerous situations;
mobile sensor nodes can also be employed to report or
debrief soldiers the events encountered during a mission
[16].
Another reason why location information is useful is to
assist mobile nodes to remain connected with a network.
Mobility of sensor nodes can lead to the deterioration of
the quality of an established link. This in turn may make
data transmission prone to failure, increasing the cost of
packet retransmission. Mobility can also cause frequent
route changes and thus produces a considerable packet
delivery delay, since a mobile node cannot immediately
begin transmitting data upon joining a network. Instead, it
has to wait for a certain amount of time before it can be
fully integrated [9].
In order to reduce the end to end latency of a data
transmission caused by the movement of nodes, several
mobility-aware MAC protocols require location information
[8]. The usefulness of the protocols highly depends on how
accurately they determine the location of mobile nodes. Most
of the protocols employ RSSI for real-time localization,
especially in indoor environments. Nevertheless, not much
research work has been done to practically demonstrate the
reliability of RSSI for indoor localization. Therefore, in
this paper, we aim to calibrate and map RSSI to distance
by carrying out a series of experiments. Based on the
observations, the conclusion that whether RSSI is reliable
and feasible for the indoor localization can be drawn.
The remaining part of this paper is organized as follows:
in Section II, related work is summarized. In Section III,
a brief introduction to RSSI technology is described. In
Section IV, the experiment settings are presented. In Sec-
tion V, the reliability of RSSI for the indoor localization
is investigated and observations are discussed. Finally, in
Section VI, concluding remarks are given.
II. RE LATE D WORK
Determination of location can be done in a number
of ways. Here, only some of the approaches are briefly
discussed.
1) Global positioning system (GPS): GPS gives the ab-
solute coordinates of a mobile node, but it is expensive and
energy consuming [20]. It also suffers from frequent satellite
disconnections in indoor environments [24].
2) Pedometers: A pedometer is a portable and elec-
tronic/electromechanical device that counts each step a per-
son takes by detecting the motion of the person’s hips.
Algorithms for navigating a mobile node by using the hop-
count based metric is simple and scalable [17]. This method,
however, is highly dependent on the network density and
path length, and thus is coarse-grained and error-prone [23].
3) Robotics: A robot can localize itself in both mapped
and unmapped terrains by employing the method which
represents the posterior distribution of possible locations
via a set of weighted samples. New measurements such as
observations of new landmarks are incorporated to filter the
previous mobility prediction and update the data of location
[12]. However, such estimation suffers from rotational and
translational errors [26], even if a map of the environment
and sensory information is given.
4) Radio frequency identification (RFID): RFID is a
technology that employs radio frequency signals to exchange
data between a reader and an electronic tag attached to an
object for the purpose of identification and tracking. RFID
readers are located strategically in the field [22]. One of
its drawbacks is the relative short communication range
(12m) and the inhibition to future extensions.
5) Anchor node: Anchors are a set of static nodes with
globally known or unknown positions. In the literature, they
are also referred to as reference nodes or seeds [12]. Anchor
nodes periodically broadcast beacon messages. By receiving
beacons from enough sources, mobile nodes can localize
themselves. In some cases, robots equipped with GPS are
deployed into a wireless sensor network to act as reference
nodes, so that sensors can localize themselves with the
information given by the robots [27]. The accuracy of the
localization depends on the distance between the mobile and
the reference points as well as the number of the anchor
nodes [25]. If the distance is too long or the anchor nodes
are too less, the location estimation errors can be high.
Moreover, the loss or malfunctioning of anchor nodes can
affect the estimation mechanism [28].
6) Time of arrival (TOA): TOA finds the distance be-
tween a transmitter and a receiver via a one way propagation
time by exploiting the relationship between the light speed
and the carrier frequency of a signal [2]. However, all the
nodes, with no information when messages will come, have
to keep awake all the time.
7) Angle of arrival (AOA): AOA is usually employed
as prior-knowledge for the triangulation localization method
[13]. The information of the arriving angle can be obtained
by using either goniometers, gyroscopes or compass.
8) Signal-to-Noise ratio (SNR): Deriving connectivity
information from position information is not straightforward,
since it requires a one-to-one mapping between distance and
signal quality. SNR, that is utilized as a measure of a node’s
link state, is easy to be monitored and does not require any
special hardware [11].
9) Ultrasound: A mobile node with an ultrasonic sensor
measures the distance to a node by exploiting the ultrasonic
signal propagation time. However, the transmission range of
an ultrasound signal is small as it cannot propagate further
than radio frequency wave [17]. It also adds size, cost,
and energy supply to each device. Therefore even though
ultrasound based localization approach can achieve high
accuracy, it is not suitable for wireless sensor networks.
10) Accelerometers: Accelerations are generated due to
both translational and rotational movements of an object. An
accelerometer-based mechanism is shown to be an accurate,
robust and practical method for objectively monitoring the
free movement of objects and persons. The mechanism
responds to both frequency and intensity of movement [28].
However, these devices increase the cost and size of a node
and may not always be available or deployable. Moreover,
accelerometer readings are sensitive of the node placement
[21].
11) Triangulation and trilateration: The localization of
mobile nodes can also be accomplished through triangulation
in a one-hop neighborhood [3]. Once a local estimation is
made for each node, a global localization can be established
by calculating differences in terms of the distance and
direction between each node and a particular central node,
or a dense group of nodes [3]. However, this mechanism
requires the use of isotropic antennas, which is expensive
and less practical.
A trilateration requires priori-knowledge of the location
of at least three nodes. The distance between nodes can be
determined only within a certain degree of certainty [13].
III. RSSI DESCRIPTION
Unlike all the localization approaches discussed above,
RSSI [10] represents the relationship between a transmission
and a received powers. It can be employed to compute the
distance of separation between a transmitter and a receiver
when a good portion of the electromagnetic wave propagates
in a line-of-sight (LOS) link. This approach has been used
in a number of mobility-aware MAC protocols.
If there is a direct path between two nodes placed in the
environment in which no signal interference and attenuation
occur, the received signal power, Pr, is related to the relative
distance, d, between the transmitter and the receiver nodes
in the inverse square low [15].
Prd2(1)
However, Equation 1 expresses the ideal relationship be-
tween RSSI and the relative distance. In the real world, many
factors influence the value of the received signal strength,
such as reflection, refraction, diffraction, and scattering of
waves caused by the nearby objects. It has been found
empirically that a wall can reduce the signal power by
approximately 3dBm on average [14]. Due to multi-path
fading and non-uniform propagation of the radio signal, the
received power may decay at a faster rate. This transfers the
relationship between Prand dto:
Prdn(2)
Here ndenotes the loss exponent. Another factor that
affects the received power and thus affects localization
is antenna polarization. In order to obtain the maximum
received power, the antenna of the receiving node should
be adjusted to the same orientation as the transmitting node
[14]. The loss due to a misaligned antenna polarization [19],
L, can be expressed as:
L= 20log(cosθ)(3)
IV. EXP ER IM EN T SETTINGS
The aim of our experiments is to prove whether RSSI is
reliable and, hence, feasible to be used for indoor localiza-
tion. The sensor platform we employed is SunSPOT motes
from Sun Microsystems. These nodes integrate 802.15.4
radio (CC2420) with a built-in 2.4GHz antenna. Each RSSI
value is obtained by averaging over 8symbol periods
(128µs) in the register [1]. The distance estimation model
proposed by Texas Instruments for the Chipcon CC2420
radio is given as:
RSSI =(10 ×n)log10 (d)A(4)
In Equation 4, RSSI is the radio signal strength indicator
in dBm,nis the signal propagation constant or exponent,
dis the relative distance between the communicating nodes,
and Ais a reference received signal strength in dBm (the
RSSI value measured when the separation distance between
the receiver and the transmitter is 1 m).
The experiment was carried out in a long corridor made
up of a glass wall on one side and a concrete wall on the
other side. One node was used as a base station directly
connected to a laptop via a USB cable. The other node is
mounted on the ankle of a user. Both nodes operated with a
full battery. There were no additional obstacles standing in
the communication path between the two nodes during the
experiment. As a result, a good portion of the signal was
propagated in a line-of-sight.
V. EX PE RI ME NT
In order to verify whether the RSSI values can be used to
reliably determine the distance between two communicating
nodes, a reference curve giving a one-to-one mapping be-
tween RSSI and the distance should be made first. This curve
is regarded as the standard showing the accurate relationship
between RSSI and the distance. To start with, the user
moves way from the base station to test the maximum radio
transmission range of the node, which was 27.5m.
RSSI is measured as an integer value and can be converted
into its corresponding dBm value by subtracting a constant
(the default value is 45 [1]). Since an RSSI value cannot be
a decimal or a fractions, it cannot offer enough resolution
to distinguish fine-grained changes in distances. Instead, it
can only provide resolution to distinguish between distances
that are large enough to cause at least a unit change in dBm
of the signal power at the receiving node. Therefore, it is
unnecessary to test RSSI values by using small increments in
distances. In our experiment, the RSSI value is tested every
1.6meters, each test lasting for 10 seconds. By averaging
all the values obtained during this time, the valid RSSI at
each testing location can be calculated. All these data sets
are displayed as red stars in Figure 1.
A. Reference Curve Establishment
There are two approaches for setting up a reference curve
using the collected discrete data sets. The first approach
starts with the evaluation of the parameter Ain Equation 4.
The evaluation is made by testing the RSSI value of the base
station which is one meter away from the transmitting node.
Ais tested to be -60.3754 dBm. By inserting this value
along with each pair of dand RSSI values obtained from
the data sets into Equation 4, a suite of values of ncan be
acquired and the average value of nis computed as 4.2119.
The values of nand Aenable us to establish a reference
curve, which is shown as the black dotted line in Figure 1.
Figure 1. The establishment of the reference curve
The second approach is curve fitting. By assuming that
x= (10)log10(d), a linear relationship between RSSI and
xcan be established (RSSI =nx A). Then, using a
polynomial curve fitting, both the values of nand Acan
be calculated. For our case, this is equal to 1.6838 and -
59.0668 dBm, respectively. The reference curve made by
this approach is illustrated by the blue solid line in Figure 1.
As can be observed from Figure 1, there is a deviation be-
tween the two reference curves. The distinction demonstrates
– from one perspective – the accurate RSSI values cannot
be obtained as long as the two communicating nodes are
very close to each other. After noticing this phenomenon, we
conducted another experiment by placing the mobile node
on a waist, knee, and ankle, respectively. The result shows
that the RSSI values become quite different for each node
placement (body position). In other words, RSSI becomes
more and more sensitive as the relative distance decreases.
Therefore, the reference curve established from the second
approach is considered more precise and thus is preferable
to be used for the verification of the reliability of RSSI.
B. Verification of the Reliability of RSSI
So far, we examined the RSSI values in a quasi-static
condition, though the distance between the transmitter and
the receiver was changed slowly. Now we consider the case
in which the RSSI values were read as one of the nodes
moves away from the base station. In the experimenter, the
mobile node was attached to the ankle of a person who was
moving on average 1.4 m/s. The reason why the movement
began from the maximum transmission range was to align
the distance traveled during all the experiments (27.5m).
Since the movement of human beings is slow, the walking
speed can be regarded uniform. As a result, for each pair
of the data sets, the time can be transformed to the relative
distance. The transformation can be expressed as:
d(i) = R
tmax tmin
t(i)i[1, n](5)
Here, nis the total number of data sets, Ris the maxi-
mum radio transmission range, and tmax and tmin are the
beginning and the end time of the experiment, respectively.
In this way, the relationship between RSSI and the distance
can be established. Before verifying the reliability of RSSI
for localization, a few mathematical methods have to be
applied to process the data obtained from the experiment.
If the processed result is close to the established reference
curve, the RSSI received during the movement can determine
the relative distance between the communication nodes with
a certain degree of accuracy.
1) Raw Data: The first and, obviously, the simplest
method to test the reliability of RSSI for node localization
is to directly use the raw data in the experiment. As
described in Figure 2, the signal fluctuation was considerably
high during mobility. Moreover, for a given RSSI values,
there were multiple corresponding distances. Still worse, the
difference between these distances were large. For example,
the RSSI value -90 dBm indicated at once a distance of
7mand 26m. Therefore, the raw data of RSSI is absolutely
weak in determining the distance of a mobile node an indoor
environment.
Figure 2. Utilization of the raw data for localization
2) Moving Average Method: In order to reduce the fluc-
tuation of the signal, the moving average method is applied.
Instead of directly using the RSSI values, the RSSI value at
each time point is calculated by averaging all the previously
received one hundred RSSI values. The time consumed by
walking from the edge of the radio transmission range to
the base station in the experiment is 21.86s. This makes the
average moving speed of the node to be 1.375m/s. Since the
data sets are generated every 10 millisecond, one hundred
RSSI samples will take 1 second to produce. This indicates
that the RSSI value at each location can be represented by all
the RSSI values in its nearby positions (1.37m). The moving
average method enables a comparatively smooth RSSI curve,
as displayed in Figure 3.
Figure 3. Utilization of the moving average method for localization
3) Weighted Average Method: Theoretically speaking,
the change of the RSSI value should be a gradual but
steady process. For a unique RSSI sample, its value should
approach the data that were collected next to it. As a result,
instead of giving the same weight for the previous data sets,
different weight should be applied to the collected samples
to enable a more accurate RSSI value for each location.
The weighted average method assigns a higher weight to
the sample that is closer to the target data whose RSSI
value is aimed to be evaluated. However, due to the strong
fluctuation of the signal, the result is quite similar with
the one obtained from the moving average method. This
is illustrated in Figure 4. One optimization of this approach
is that the trajectory of the processed RSSI values better
fits the reference curve. This indicates that the RSSI values
are able to realistically determine the distance on the whole.
Nevertheless, focusing on each RSSI sample, the distance it
gives can still lead to a big difference in terms of the actual
location.
4) Curve fitting method: Curve fitting uses all the samples
in the experiment to predict the value of the data in the
next time instant. The more samples provided, the more
precise the prediction will be. Therefore, the curve fitting
method will generate more accurate values of RSSI as time
goes by. The curve obtained by this approach is shown in
Figure 5. As can be observed, the processed RSSI value
is far away from the value that is given by the reference
curve when the relative distance is less than 6m. However,
with the increment of the distance, the difference between
the values obtained from the curve fitting method and the
Figure 4. Utilization of the weighted average method for localization
Figure 5. Utilization of the curve fitting method for localization
values provided by the reference curve decreases. Even
though the result exhibits convergence to a certain extent,
the localization errors cannot be disregarded.
VI. CONCLUSION
In this paper, we investigated the reliability of RSSI for
an indoor localization. First, we statically measured a series
of samples, based on which a reference curve that gives
the accurate one-to-one mapping between the RSSI values
and distance was established. Then, we made use of four
estimation techniques to establish a relationship between
distance and the RSSI values obtained from a mobile node.
The RSSI values obtained in the mobile scenario fluctuate
considerably. Hence, the inaccuracies we observed rendered
RSSI as the only input to determine the location of a mobile
node in an indoor environment unsuitable, regardless of the
estimation techniques applied.
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... The figure shows the location of the data container storing data on the RSSI of the measured radio probe transmitting modules and their identification numbers necessary for data parsing by the probe receiving module. The key operations related to acquiring and overwriting information contained in the created communication frame (extracting the RSSI value from the structure and comparing the identifier) are presented in the white rectangle [59][60][61]. ...
... The figure shows the location of the data container storing data on the RSSI of the measured radio probe transmitting modules and their identification numbers necessary for data parsing by the probe receiving module. The key operations related to acquiring and overwriting information contained in the created communication frame (extracting the RSSI value from the structure and comparing the identifier) are presented in the white rectangle [59][60][61]. The transmitter is configured to cyclically broadcast a signal containing data on its power and the powers of other transmitters as measured by its receiving portion. ...
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Recently, various novel scenarios have been studied for indoor localization. The trilateration is known as a classic theoretical model of geometric-based indoor localization, with uniform RSSI data that can be transferred directly into distance ranges. Then, a trilateration solution can be algebraically acquired from theses ranges, in order to fix user’s actual location. However, the collected RSSI or other measurement data should be further processed and classified to lower the localization error rate, instead of using the raw data influenced by multi-path effect, multiple nonlinear interference and noises. In this survey, a large number of existing techniques are presented for different indoor network structures and channel conditions, divided as LOS (light-of-sight) and NLOS (non light-of-sight). Besides, the input measurement data such as RSSI (received signal strength indication), TDOA (time difference of arrival), DOA (distance of arrival), and RTT (round trip time) are studied towards different application scenarios. The key localization techniques like RSSI-based fingerprinting technique are presented using supervised machine learning methods, namely SVM (support vector machine), KNN (K nearest neighbors) and NN (neural network) methods, especially in an offline training phase. Other unsupervised methods as isolation forest, k-means, and expectation maximization methods are utilized to further improve the localization accuracy in online testing phase. For Bayesian filtering methods, apart from the basic linear Kalman filter (LKF) methods, nonlinear stochastic filters such as extended KF, cubature KF, unscented KF and particle filters are introduced. These nonlinear methods are more suitable for dynamic localization models. In addition to the localization accuracy, the other important performance features and evaluation aspects are presented in our paper: scalability, stability, reliability, and the complexity of proposed algorithms is compared in this survey. Our paper provides a comprehensive perspective to compare the existing techniques and related practical localization models, with the aim of improving localization accuracy and reducing the complexity of the system.
... For instance, diffraction, refraction, reflection, and scattering of waves are a result of nearby objects and obstacles between receivers and transmitters. It has been discovered through experimentation that walls can lower the signal strength by up to 3 dBm (decibel-milliwatts) on average [71]. In other words, the received power of signal P r declines more gradually because of shadow fading, nonuniform propagation and multipath propagation. ...
... Proposed TDoF-based verification. a relationship to the distance, D RSS , between the receiving and transmitting devices in the law of inverse square[71]. ...
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... Whenever target localization is based on the RSSI information, the reliability of RSSI signals becomes an important issue. As pointed out in [5] and [6], RSSI can easily be disturbed and, therefore, cannot be used directly for calculation of target location in an indoor environment. This fact motivates the use of filtering techniques to enhance positioning accuracy. ...
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... In all the experiments, the robots start at a random location in the workspace and move with a random walk trajectory. Several studies have shown a range of 40 m is optimum to receive a good RSSI signal with high link quality [37], [38]. Therefore, we estimate that in our wireless sensor networks testbed, the nodes can be spaced with a diagonal range of up to 40 m for optimum performance in ideal conditions. ...
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... where d is the wireless distance between the sender and the receiver node, and f is the carrier frequency. The value of d can be calculated as, d = 10 (T X P −T RSSI ) /20 [24], where T RSS I is the Received Signal Strength Indicator (RSSI). It depends on the loss, sensitivity, fading effect, and carrier frequency band. ...
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... The value of d (in Eq. 1) can be calculated as, d = 10 (Txp−Trss) /20 [17], where T rss is the Received Signal Strength Indicator (RSSI), it depends on gains, loss, sensitivity, fading effect, and carrier frequency band. T xp is the transmission power of an 802.11ah node. ...
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