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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,
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-
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
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.
Determination of location can be done in a number
of ways. Here, only some of the approaches are briefly
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
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].
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].
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:
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)
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.
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
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
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.
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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This article presents research results on a smart building prediction, navigation and asset management system. The main goal of this work was to combine all comfort subsystems, such as lighting, heating or air conditioning control, into one coherent management system supported by navigation using radio tomographic imaging techniques and computational intelligence in order to improve the building’s ability to track users and then maximize the energy efficiency of the building by analyzing their behavior. In addition, the data obtained in this way were used to increase the quality of navigation services, improve the safety and ergonomics of using the room access control system and create a centralized control panel enriched with records of the working time of individual people. The quality of the building’s user habit learning is ensured by a network of sensors collecting environmental data and thus the setting values of the comfort modules. The advantage of such a complex solution is an increase in the accuracy of navigation services provided, an improvement in the energy balance, an improvement in the level of safety and faster facility diagnostics. The solution uses proprietary small device assemblies with implementation of popular wireless transmission standards such as Bluetooth, Wi-Fi, ZigBee or Z-Wave. These PANs (personal area networks) are used to update and transmit environmental and navigation data (Bluetooth), to maintain the connection of other PANs to the master server (Wi-Fi) and to communicate with specific end devices (ZigBee and Z-Wave).
... A classic solution [52,53] is to analyze RSSIs from the geometric relations. A conventional definition of RSSI to the distance d in the indoor localization problem is given as [60]: ...
... • Reliability is basically a performance feature towards real-time localization modeling, which requires the system to realize precise positioning [60] with acceptable roaming delay [59]. To reach the reliability requirement under real-time, the minimum sampling number for tri-partition-based RSSI filter [25] is set as 30 with the shortest RSSI collection time at less than 1 s. ...
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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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The Internet of Things (IoT) and localization are two important technology enablers. An important IoT technology is the Narrow-Band Internet of Things (NB-IoT), which is a 3GPP standards compliant connectivity solution. Quantum computing, another developing technological paradigm, promises novel computational opportunities, but it has also been used to compromise many cybersecurity procedures. Therefore, improved methods to mitigate such jeopardies are needed. In this research, we propose a cryptographic system that guarantees post-quantum IoT security. The ultimate value of a location-driven cryptosystem is to use geographic location as a player’s identity and credential. Position-driven cryptography using lattices is efficient and lightweight, and it can be used to protect sensitive and confidential data in many critical situations that rely heavily on exchanging confidential data. Based on our best knowledge, this research starts the study of unconditional-quantum-resistant-location-driven cryptography by using the Lattice problem for the Internet of Moving Things (IoMT) in pre-and post-quantum world. Unlike existing schemes, the proposed cryptosystem is the first secure and unrestricted position-based protocol that guards against any number of collusion attackers and against quantum attacks. It has a guaranteed authentication process, solves the problems of distributing public keys by cancelling public key infrastructure (PKI), offers secure NB-IoT without SIM cards, and resists location spoofing attacks. Furthermore, it can be generalized for any network – not just NB-IoT.
... Generally, received signal strength indications (RSSI) of these sensors are used for localization. How- ever, RSSI is prone to disturbances in indoor environments and may not be used for positioning directly [5], [6]. Hence, extra tools are needed to enhance positioning accuracy. ...
... 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. ...
In this paper, federated Kalman filter (FKF) is applied for indoor positioning. Position information that is multi-laterated from the distance information obtained using the received signal strengths collected from several access points are processed in a FKF to estimate the position of the target. Two approaches are presented to adjust the information-sharing coefficients of FKF using online measurements. The data collected on a test bed composed of four access points are used to assess and compare the performances of the proposed algorithms. It is shown that the estimation error can be improved considerably by adjusting the information-sharing coefficients online.
... 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. ...
A mobile robot's precise location information is critical for navigation and task processing, especially for a multi-robot system (MRS) to collaborate and collect valuable data from the field. However, a robot in situations where it does not have access to GPS signals, such as in an environmentally controlled, indoor, or underground environment, finds it difficult to locate using its sensor alone. As a result, robots sharing their local information to improve their localization estimates benefit the entire MRS team. There have been several attempts to model-based multi-robot localization using Radio Signal Strength Indicator (RSSI) as a source to calculate bearing information. We also utilize the RSSI for wireless networks generated through the communication of multiple robots in a system and aim to localize agents with high accuracy and efficiency in a dynamic environment for shared information fusion to refine the localization estimation. This estimator structure reduces one source of measurement correlation while appropriately incorporating others. This paper proposes a decentralized Multi-robot Synergistic Localization System (MRSL) for a dense and dynamic environment. Robots update their position estimation whenever new information receives from their neighbors. When the system senses the presence of other robots in the region, it exchanges position estimates and merges the received data to improve its localization accuracy. Our approach uses Bayesian rule-based integration, which has shown to be computationally efficient and applicable to asynchronous robotics communication. We have performed extensive simulation experiments with a varying number of robots to analyze the algorithm. MRSL's localization accuracy with RSSI outperformed other algorithms from the literature, showing a significant promise for future development.
... 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 emerging IEEE 802.11ah is a promising communication standard for large-scale networks particularly the Internet of Things (IoT). The single-channel-based centralized channel access mechanism employed in 802.11ah does not scale well in such networks and leads to poor data reception quality. In this paper, we propose an Enhanced and Scalable Medium Access Control (ES-MAC) protocol, which employs multi-band sectorization and dynamic load balancing. These features facilitate multi-hop communication more efficiently and enhance network capacity. Traffic congestion issues prevailing around the access point node due to the large volume of uplink traffic is mitigated by allowing simultaneous transmission using multiple orthogonal channels and sectors. Simulation and analytical results establish the essence of the novel protocol by showing significant improvements in terms of throughput and average packet delay over the existing schemes. The proposed network architecture improves throughput and delay performance up to 150% and 100% respectively compared to the relevant schemes.
... 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. ...
Obtaining fine-grained spatial information is of practical importance in RFID-based systems for enabling multi-object identification. However, as high-precision positioning remains impractical in COTS RFID systems, researchers propose to combine CV with RFID and turn the positioning problem into a matching problem. Promising though it seems, current methods fuse CV and RFID through converting traces of tagged objects extracted from videos by CV into phase sequences for matching, which is a dimension-reduced procedure causing loss of spatial resolution. Consequently, they fail in harsh conditions like small tag intervals and low reading rates. To address the limitation, we propose TagFocus to achieve fine-grained multi-object identification with visual aids in RFID systems. The key observation is that traces generated through different methods shall be compatible if they are of one identical object. Accordingly, a Transformer-based seq2seq model is trained to generate a simulated trace for each candidate tag-object pair. And the trace of the right pair shall best match the observed trace directly extracted by CV. A prototype of TagFocus is implemented and extensively assessed in lab environments. Experimental results show that our system maintains a matching accuracy of over 91% in harsh conditions, outperforming state-of-the-art schemes by 27%.
In recent years, Smart Grid have become the center of interest for IT companies and construction companies and various types of Smart Grids have been made currently available on the market. Yet, equipment is costly and it is not easy to convert existing equipment for Smart Grid application as they may require additional resources which could also inflict much costs. The extra costs involving the remodeling of existing housing structure and installment of new equipment can be avoided by using advanced wireless technologies. As an example, this book proposed an indoor localization system that adopts Bluetooth technology and uses RSSI (Received Signal Strength Indication) values for localization. Researchers have configured a system where the central control device will recognize all other devices or equipment in the system, communicate with each other, and respond to the commands or the information provided. However, despite the efforts of many researchers, existing RSSI-based indoor localization systems do not show a satisfactory level of accuracy such that we have devised a system that traces the trend in the RSSI samples.
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Recent advancements in Internet of Things (IoTs) have brought about a surge of interest in indoor positioning for the purpose of providing reliable, accurate, and energy-efficient indoor navigation/localization systems. Ultra Wide Band (UWB) technology has been emerged as a potential candidate to satisfy the aforementioned requirements. Although UWB technology can enhance the accuracy of indoor positioning due to the use of a wide-frequency spectrum, there are key challenges ahead for its efficient implementation. On the one hand, achieving high precision in positioning relies on the identification/mitigation Non Line of Sight (NLoS) links, leading to a significant increase in the complexity of the localization framework. On the other hand, UWB beacons have a limited battery life, which is especially problematic in practical circumstances with certain beacons located in strategic positions. To address these challenges, we introduce an efficient node selection framework to enhance the location accuracy without using complex NLoS mitigation methods, while maintaining a balance between the remaining battery life of UWB beacons. Referred to as the Deep Q-Learning Energy-optimized LoS/NLoS (DQLEL) UWB node selection framework, the mobile user is autonomously trained to determine the optimal pair of UWB beacons to be localized based on the 2-D Time Difference of Arrival (TDoA) framework. The effectiveness of the proposed DQLEL framework is evaluated in terms of the link condition, the deviation of the remaining battery life of UWB beacons, location error, and cumulative rewards. Based on the simulation results, the proposed DQLEL framework significantly outperformed its counterparts across the aforementioned aspects.
Conference Paper
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This paper investigates the latency of packet transmission during mobility with and without the support of a handover mechanism. The Receiver-Initiated MAC protocol (RI-MAC) is used for the analysis. When the handover mechanism is applied, it enables a node to establish a new connection before the existing link breaks. A mathematical model is set up to examine the performance of RI-MAC when it uses the handover mechanism and when it does not. The analytical result shows that the handover latency is much less than the latency introduced when a node waits until an existing link breaks and then establishes a new link.
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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 layer. These difficulties require mobility adaptation algorithms to localize mobile nodes and predict the quality of link that can be established with them. This paper surveys the current state-of-art in handling mobility. It first describes existing mobility models and patterns; and analyzes the challenges caused by mobility at the link layer. It then provides a comparative study of several mobility-aware MAC protocols.
Conference Paper
Wireless sensor networks have the potential to become the pervasive sensing (and actuating) technology of the future. For many applications, a large number of inexpensive sensors is preferable to a few expensive ones. The large number of sensors in a sensor network and most application scenarios preclude hand placement of the sensors. Determining the physical location of the sensors after they have been deployed is known as the problem of localization. We present a localization technique based on a single mobile beacon aware of its position (e.g. by being equipped with a GPS receiver). Sensor nodes receiving beacon packets infer proximity constraints to the mobile beacon and use them to construct and maintain position estimates. The proposed scheme is radio-frequency based, and thus no extra hardware is necessary. The accuracy (on the order of a few meters in most cases) is sufficient for most applications. An implementation is used to evaluate the performance of the proposed approach.
The objective of this paper was to investigate the effect of limited grazing time on urination and defecation frequency, spatial distribution of excrement in the paddock, and the resulting nitrogen balance at animal and field level. During a 6-week period in early summer, 60 Holstein Frisian dairy cows (31.0 ± 5.4 kg ECM) were randomly allocated to three different treatments, with grazing at clover-grass pasture during daytime for 4, 6.5 or 9 h daily. Indoor feeding, with a mixture of roughage and concentrates (13% crude protein), was restricted for treatment 4 and 6.5 h to the amount the 9-h treatment could eat. Cows allowed grazing at pasture for 4 h moved more rapidly during pasture, moved longer distance per active hour and used a higher proportion of the time eating, both at pasture and indoor, than the cows allowed longer time at pasture. Limiting the grazing time had no influence on the urination (mean = 0.26) and defecation (mean = 0.37) frequency per cow per hour during pasture. Even though the proportion of time active (eating, drinking, standing or walking), and the actual time active during pasture was different for the treatments, the frequency of urination and defecation per active hour was also unaffected by the treatments. Urine and faeces were distributed in the pasture, without specific hot-spots. The estimated daily N-balance at animal level showed increased N excretion with time at pasture. Assuming that excretion follows the active periods during the day and 7000 kg DM foliage is available on yearly basis, this would result in total excretion at field level of 58, 86 and 108 kg N per ha respectively for treatment 4, 6.5 and 9 h. The results of this experiment show that it is possible to reduce the nitrogen excretion in a grazing system by restricting the grazing time of dairy cows together with restricted indoor feeding while maintaining high foliage intake.
In this paper, we present a novel approach to location system under indoor environment. The key idea of our work is accurate distance estimation with cricket-based location system using A* algorithm. We also use magnetic sensor for detecting obstacles in indoor environment. Finally, we suggest how this system can be used in various applications such as asset tracking and monitoring. Keywords—Cricket, Indoor Location Tracking, Mobile Robot, Localization.
In the changing nursing environment, nurses frequently encounter interruptions while performing nursing care. This phenomenon creates prolonging nurses' work hours and particularly makes an impact on novice nurses. In our previous study, a dynamic scheduling method was proposed to support inpatient nursing on the execution order of nursing activities. Furthermore, it is evaluated to be effective through several simulated problems. In actual, the inpatient nursing is complex for the random occurrence of undetermined events like patient calls and variation of processing times for nursing. It limits the validation of practical applicability of the proposed method. The purpose of this study is to verify the dynamic scheduling system for nurses' care working in acute. In this study, through a set of laboratory experiments, we evaluate the applicability of the dynamic scheduling system by comparing its instructed cares with those based on nurses' own action rules. As a result, the proposed dynamic scheduling system provided higher quality care, and thus its applicability to practical nurses' work environments is confirmed.
A method to facilitate large-scale deployment of location-aware sensor networks was studied. Anchor-free algorithms use local distance information to attempt to determine node coordinates when no nodes have pre-configured positions. The anchor-free localization (AFL) algorithm was simulated on a 250-node graph and 50 simulations were carried out to obtain each point on the graph. It is observed that AFL shows the superior performance under ranging errors, since the maximum distance error between any two points is small most of time.
In this paper, we consider the problem of node positioning in ad-hoc networks. We propose a distributed, infrastructure-free positioning algorithm that does not rely on Global Positioning system (GPS). The algorithm uses the distance between the nodes to build a relative coordinate system in which the node positions are computed in two dimensions. The main contribution of this work is to define and compute relative positions of the nodes in an ad-hoc network without using GPS. We further explain how the proposed approach can be applied to wide area networks.
Real-time position localization of moving objects in an indoor environment is an encouraging technology for realizing the vision of creating numerous novel location-aware services and applications in various market segments. An off the shelf development platform that uses Radio Signal Strength Indication (RSSI) based location tracking technique is studied. In this paper we investigate the affects of polarization on the accuracy of an indoor location tracking system. We present an approach to increase system accuracy based on this investigation. We established a model for determining range from RSSI and showed that the model fits our own experimental data. The model includes parameters used to account of environmental effects and we use the least squares method of determining the parameter values. Antenna polarization angle will affect RSSI and thus range accuracy. We empirically show that the model is still valid for polarization mismatch but with different environmental parameter values. A method based on semi-automated trail and error is proposed as a better method for selecting the environmental parameters. Using experimental data we show that if we adjust the model parameters to account for polarization angle then we can increase location accuracy. A practical solution for determining the polarization angle is with an accelerometer. The addition of an accelerometer could also be used to increase the battery life of the node.