=Paper= {{Paper |id=Vol-2525/paper14 |storemode=property |title=Position control for mobile nodes in wireless sensor network based on the IEEE 802.15.4 protocol by link quality estimation |pdfUrl=https://ceur-ws.org/Vol-2525/ITTCS-19_paper_29.pdf |volume=Vol-2525 |authors=Alexander Ermakov,Alexander Titaev,Nikita Gorshkov |dblpUrl=https://dblp.org/rec/conf/ittcs/ErmakovTG19 }} ==Position control for mobile nodes in wireless sensor network based on the IEEE 802.15.4 protocol by link quality estimation== https://ceur-ws.org/Vol-2525/ITTCS-19_paper_29.pdf

Position control for mobile nodes in wireless sensor network based on
the IEEE 802.15.4 protocol by link quality estimation*

Alexander A. Titaev Alexander A. Ermakov Nikita I. Gorshkov
PhD, Associate professor Ural Federal University Ural Federal University
Ural Federal University Ekaterinburg, Russia Ekaterinburg, Russia
Ekaterinburg, Russia alexerm99@mail.ru ngisport110@gmail.com
alexander.titaev@gmail.com

Abstract
The paper describes a method for position control for mobile nodes in wireless
sensor network, based LQI indicator provided by the IEEE 802.15.4 standard.
Experimental measurements were carried out. The optimal performance
parameters of the regulator have been obtained.

1 Introduction
Wireless Sensor Networks (WSN) based on the IEEE 802.15.4 standard [1] - low-speed personal distributed networks
are currently the subject of many researches. This is due to their use in many relevant technical areas, such as monitoring
of disturbed parameters (fire sensors, seismic monitoring, radioactivity, etc.), rapid deployment of the network in order to
gather the information in the area (for military or rescue purposes). In such cases WSNs of aggregation network type are
use: collecting data from disturbed sensors using a single root device (sink), forming a tree-like structure of wireless routes
with a root in the sink. In this case, the transmission range of each node can be limited by several neighbors that act as
repeaters from this node to the sink. This allows you to reduce the power of transmitter and increase the lifetime of nodes
for safe battery. At high levels of the OSI model one of protocols for PAN networks is used: 6LowPAN - a protocol which
uses IPv6 addresses for PAN networks [1], ZigBee - a protocol for devices with simple automation purposes (IoT, Smart
Home, etc.) [2].
Wireless communication allows you to realize the ability of sensors to move. It’s can increase the efficiency of the
system by increasing the coverage of the area an optimal way. The initial location of nodes may be a priori unknown (if
nodes were placed in its initial positions randomly, for example, by dropping from an airplane, or they begin to move from
one common place). One of the most common optimization problems is the problem of optimal coverage of the field with
sensors. Each point of the field should be in the coverage area of sensors of at least one node, and the total number of nodes
in WSN should be minimal. Such mobile sensor networks are used both for tasks of dynamically coverage of certain area
with a sensor network, and for more complex algorithms accompanied with movement (perimeter protection by patrolling,
sweep coverage, etc.). A review of possible applications and the most typical algorithms was made in [3]. Several
approaches are applied to this problem: methods based on the virtual force algorithm [4], graph-oriented methods [5],
algorithms with the possibility of gap closing [6].
The problem described above is based on methods for determination and control of relative position of nodes by
distance estimation between nodes. From location and distances to its neighbors the node can calculate its necessary
movement dynamically. Currently, there are many approaches to determine the position of nodes: from expensive and
accurate (GPS) to cheap, but with low accuracy. It is obviously that small WSN nodes require accurate, but cheaper methods
for distance estimation. Several approaches have been developed for this task: by received signal strength (RSSI), by time
of arrival (TOA), by time difference of arrival (TDOA), and by angle of arrival (AOA).

*Copyright © 2019 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0
International (CC BY 4.0).
API though the problem of distance estimation has been observed in many papers, the close task of position control
based on this data is much worse highlighted. However, since any values used by the regulator of object (in this case, the
motor subsystem of sensor node) should be predictable and reliable, it is necessary to focus on practical aspects of control
system implementation based on the feedback distance estimation signal.
According to this information, remainder of the article contains the following sections: section 2 shows an overview
of existing methods for distance estimation from LQI value, in section 3 we discuss the probabilistic nature of the measured
LQI values and propose a method for distance estimation, in section 4 gives a technique for position control of node using
the measured LQI value has been proposed, experimental results of performance measurements has been shown in section
5, section 6 contains analysis of the results and a conclusion.

2 First Level Heading Existing methods for distance estimation based on signal strength and
link quality
The paper of [7] provides an overview of existing methods for localization of nodes throw characteristics of radio
signal. There are two main groups of algorithms: algorithms with distance estimation (ranged based) and with no distance
estimation (ranged free). A key aspect of distance estimation algorithms is to obtain the distance from the current node to
several nearest nodes and to find its position using the trilateration method. In ranged-free algorithms, node position can
be estimated from the fact that the nearest beacon node with known coordinates is placed near the one. We assume that our
node has approximately the same coordinates as the reference one. If there are several beacon nodes nearby, a more accurate
estimation of node position can be performed by combining known coordinates of beacon nodes.
Ranged-based algorithms use one of following parameters of the received radio signal for this task:
- RSSI
- Time of Arrival
- Difference Time of Arrival
- Angle of Arrival
The most universal method is the first one. It is caused by complexity of due to the complexity of small time intervals
measuring in ToA and DToA methods, and design of the antenna configuration for measuring in AoA method.
During measuring distance in RSSI based method the power of transmitted signal, the power of received signal and
attenuation model must be taken into account. The formula is
𝑃𝑅 (𝑡) = 𝑃𝑇 − 10𝜂 log(𝑑) + 𝑋(𝑡) (1)
where 𝑃𝑇 and 𝑃𝑅 (𝑡) –powers of transmitted and received signals; 𝜂 – an empirically selected parameter; d – distance
between transmitting and receiving nodes; 𝑋(𝑡) – random component that depends on interference, attenuation in medium
properties.
This equation can be solved with respect to variable d. Then the distance can be calculated by known values of the
remaining parameters.
However, this technique has several disadvantages:
- the logarithmic law the attenuation is not always performed in real cases. This may be caused by both signal effects
and anisotropy of signal propagation in different directions. The paper of [8] provides an estimation of error in calculated
distance of ± 50% even for the stationary case.
- the presence of obstacles in the line of sight causes uncontrolled changes in the RSSI signal due to reflections from
obstacles
- the determination of coefficient 𝜂 and distribution law of random variable 𝑋(𝑡) requires calibration of the system
before taking measurements, which is not always possible with dynamic deployment of network.
These shortcomings have been investigated in many papers where methods for overcoming them by hardware or
software signal processing have been proposed. Part of these papers in this area is devoted to refinement and modification
of model (1) [9, 10]. In research of [11] it is proposed to use an adaptive algorithm that dynamically determines actual
current value of RSSI as a weighted sum of instantaneous measured values. Recent works focus on non-classical RSSI
signal calculation: for example, the paper of [12] proposes distance estimation with the help of fuzzy logic. One of the
hardware improvements is a combination of radio and ultrasonic transceivers [13]. However, this will require the
installation of additional equipment on the mobile platform. In the case when sensor field can be prepared in advance, it is
possible to orient nodes in space according to pre-arranged tags or anchor nodes, with known location [14].
However, it must be noted that the disadvantages cannot be completely by proposed methods. In networks based on
IEEE 802.15.4-2003 standard, the quality of received signal can be estimated using the link quality indicator (LQI). The
standard proposes to calculate LQI value with the help of the received signal, or signal-to-noise ratio, or a combination of
both values. The LQI value should be between 0 and 255, where 0 is the worst received signal quality and 255 is the best.
The dependence of LQI on the distance between the nodes is covered in the literature in only few articles [15] (indoors and
outdoors), [16] (indoors). The difficulty of such task is that LQI calculation algorithm is selected individually by each chip
manufacturer. Thus, the published data on this topic mainly concerns the Chipcon CC2420 chip and cannot be extrapolated
on chips from other manufacturers. However, based on the information available in papers [15, 17], it can be assumed that
the dependence of distance 𝑑𝑖𝑗 between nodes i and j on LQI indicator, which shows the link quality between them, can be
interpolated by simple piecewise linear function:
𝑑𝑖𝑗 = 𝑓(𝐿𝑄𝐼𝑖𝑗 ) = 𝐴 ∙ 𝐿𝑄𝐼𝑖𝑗 + 𝐵 (2)
where coefficients A and B depend on specific chip manufacturer.
A comparison of RSSI and LQI indicators versus the distance between receiver and the transmitter in [18] shows that
the relationship between LQI and distance is more monotonic and less susceptible to interference, especially for outdoor
cases. This is consistent with the authors' data obtained during experiments with chips from another manufacturer [17].

3 Determination probability distribution parameters of LQI measurements values
Because measured value of LQI indicator is based on RSSI value, we can assume that distribution laws of random
LQI values and RSSI values are the same as was shown in [11] the measured RSSI is the Gaussian random variable.
To determine the standard deviation, a number of measurements was carried out for various LQI values. The
experimental setup consists of a pair of modules (transmitter and receiver), the distance between which can vary from 0 to
85m. The tests were carried out outdoors, with no people around and in conditions of direct visibility between modules. As
a payload, a packet with length of 100 bytes was used (the maximum packet length in ZigBee protocol). The total number
of measurements for each LQI value is 100.
Experimental results are presented in Fig. 1 and 2. It is seen that the average LQI values in the absence of obstacles
on the line between receiver and transmitters decrease monotonously with increasing distance. Unlike LQI, the Packet
Delivery Ratio (PDR) decreases non-monotonously, which can be caused by the fact that LQI is calculated by the first
eight received bytes of the frame, while the PDR depends on bytes of whole frame, which can be dropped because errors
in frame checksum.

Fig.1. Influence of the distance between nodes on measured average LQI and PDR for outdoor
120

100

Standard deviation
80

0
0 50 100 150 200 250 300
Average LQI

Fig. 2. Standard deviations of LQI values.
From Fig. 1 we can interpolate the relationship between distance d and measured LQI value: Small distances (<20m)
do not cause a systematic change in LQI values. After reaching d = 20m, LQI value begins to decrease linearly. The
dependence d (LQI) which we are interested in, can be obtained for distances greater than 20 m and has the form:
𝑑 = 𝑓(𝐿𝑄𝐼) = −1,75 ∙ 𝐿𝑄𝐼𝑖𝑗 + 420,25 (3)

From Fig. 2, we can conclude that for LQI range [200; 255] the RMS value does not exceed 6.7 (except for two
measurement misses in region of 11-12). For LQI values less than 200, the standard deviation is stepwise and significantly
increase. This limits the operating range for control the moving node: LQI = [200; 255]. Taking a lower value of LQI as
the operating point will make control process of the node position difficult due to the noisy feedback signal for the regulator.
The probability of measurement miss can be estimated from the value of the standard deviation and known distribution
law. According to [11], we can assume the distribution law of the obtained LQI values is the Gaussian law.
Suppose that measured LQI is a random variable with the mean value 𝐿𝑄𝐼𝑠𝑒𝑡 . In this case, for a normally distributed
variable, we estimate the probability 𝑝∗ of obtaining value beyond a given range (𝐿𝑄𝐼𝑠𝑒𝑡 − ∆𝐿𝑄𝐼𝑠𝑒𝑡 ; 𝐿𝑄𝐼𝑠𝑒𝑡 + ∆𝐿𝑄𝐼𝑠𝑒𝑡 ),
which will be used in deadband for the regulator:

𝑝(𝐿𝑄𝐼𝑠𝑒𝑡 − ∆𝐿𝑄𝐼𝑠𝑒𝑡 ≤ 𝐿𝑄𝐼 ≤ 𝐿𝑄𝐼𝑠𝑒𝑡 + ∆𝐿𝑄𝐼𝑠𝑒𝑡 ) = 𝐹(𝐿𝑄𝐼𝑠𝑒𝑡 + ∆𝐿𝑄𝐼𝑠𝑒𝑡 ) − 𝐹(𝐿𝑄𝐼𝑠𝑒𝑡 − ∆𝐿𝑄𝐼𝑠𝑒𝑡 ) (4)

Where
(𝜆−𝐿𝑄𝐼𝑠𝑒𝑡 )2
1 𝑥
𝐹(𝑥) = ∫ 𝑒 2𝜎2 𝑑𝜆 (5)
𝜎√2𝜋 −∞
is probability distribution function.
The probability 𝑝∗ can be calculated:
𝑝∗ = 1 − 𝑝(𝐿𝑄𝐼𝑠𝑒𝑡 − ∆𝐿𝑄𝐼𝑠𝑒𝑡 ≤ 𝐿𝑄𝐼 ≤ 𝐿𝑄𝐼𝑠𝑒𝑡 + ∆𝐿𝑄𝐼𝑠𝑒𝑡 ) (6)

Having upper limit of 6.7 as the standard deviation (from fig.2), we get a graph for determining the probability p*
and regulator deadband ∆𝐿𝑄𝐼𝑠𝑒𝑡 .
1,0

The probability of a significant miss
0,9
0,8
0,7
0,6

change 0,5
0,4
0,3
0,2
0,1
0,0
0 5 10 15 20 25
Deadband

Fig. 3 The relationship between probability of miss and regulator deadband range.

4 Position control of mobile node
The research task is to automatic the distance between the fixed sink node and mobile autonomous WSN node. The
equipment of model is shown in Fig. 4.

Fig. 4. The experimental setup.

Problem statement: it is required to automatically control the distance 𝑑𝑠𝑒𝑡 from mobile unit to the base station by
controlling the motors of the wheel platform of the unit.
As a mobile node WSN, a model based on AVR ATmega328 microcontroller was constructed. The mobile part
consists of a four-wheeled platform with DC motors mounted on each wheel. The digital engine control circuit can select
the direct and reverse direction of the wheels rotation at a constant speed. The experimentally established speed of the
wheel rotation is 10.5 rad/s, while the linear velocity of the platform is 0.34 m / s. As a transceiver for wireless
communication we use XBee S2C module with ZigBee protocol. The core of the module is Ember EM357 chip.
Such characteristics of the equipment led us to consider the position control system as a three-position digital
regulator. This regulator has input parameters: 1) Set point distance between the mobile and fixed nodes 𝑑𝑠𝑒𝑡 ; 2) value of
deadband of the controller ∆𝑑𝑠𝑒𝑡 ; 3) feedback signal - measured random value of LQI and the value of the distance
𝑑(𝐿𝑄𝐼)obtained according to formula (3); and output parameter 4) a signal to DC motors of the wheels, which can take
three states - back, stop, forward.
Node speed 𝑣 is determined by the expression:
+𝑉𝑛 , 𝑑(𝐿𝑄𝐼) < 𝑑𝑠𝑒𝑡 − ∆𝑑𝑠𝑒𝑡
𝑣 = {0, 𝑑𝑠𝑒𝑡 − ∆𝑑𝑠𝑒𝑡 ≤ 𝑑(𝐿𝑄𝐼) ≤ 𝑑𝑠𝑒𝑡 + ∆𝑑𝑠𝑒𝑡 (7)
−𝑉𝑛 , 𝑑(𝐿𝑄𝐼) > 𝑑𝑠𝑒𝑡 + ∆𝑑𝑠𝑒𝑡

Because the distances 𝑑(𝐿𝑄𝐼), 𝑑𝑠𝑒𝑡 , ∆𝑑𝑠𝑒𝑡 are determined by values of LQI, 𝐿𝑄𝐼, 𝐿𝑄𝐼𝑠𝑒𝑡 , ∆𝐿𝑄𝐼𝑠𝑒𝑡 , expression (7) can
be reformulated as follows:

+𝑉𝑛 , 𝐿𝑄𝐼 > 𝐿𝑄𝐼𝑠𝑒𝑡 + ∆𝐿𝑄𝐼𝑠𝑒𝑡
𝑣 = {0, 𝐿𝑄𝐼𝑠𝑒𝑡 − ∆𝐿𝑄𝐼𝑠𝑒𝑡 ≤ 𝐿𝑄𝐼 ≤ 𝐿𝑄𝐼𝑠𝑒𝑡 + ∆𝐿𝑄𝐼𝑠𝑒𝑡 (8)
−𝑉𝑛 , 𝐿𝑄𝐼 < 𝐿𝑄𝐼𝑠𝑒𝑡 − ∆𝐿𝑄𝐼𝑠𝑒𝑡

The experiment equipment includes: 1) stationary base node; 2) mobile node (see Fig. 4). By determining the distance
to sink, mobile node controls switch direct-stop-reverse directions of platform wheels. For operational measurement of
LQI values, the node must receive ZigBee packets addressed for it or broadcasts. Based on performance of the MC, the
frequency of broadcast packets of 1 Hz was selected on the stationary node. The time-lag of the control object in this case
will be determined by the frequency of updating LQI value, and, therefore, the frequency of receiving frames from the base
station. Parameters of three-position controller used in the experiment: 1) setpoint value 𝐿𝑄𝐼𝑠𝑒𝑡 , which can be converted
into a distance according to an empirically obtained formula; 2) the value of deadband of the regulator ∆𝐿𝑄𝐼𝑠𝑒𝑡 , inside
which the position of node is recognized as satisfactory and does not require its movement to another place.
The choice of 𝐿𝑄𝐼𝑠𝑒𝑡 was made basing on the obtained in Figs. 1 and 2 relationships. It can be seen in Fig. 1 that LQI
values from the interval [222; 255] are of practical interest. At lower values, distance estimation is not possible due to the
increasing influence of interference. Another criterion for choosing a setpoint is the standard deviation of a random variable
LQI shown in Fig. 2. This parameter limits the range of setpoint values to the range [215,255]. The number of 𝐿𝑄𝐼𝑠𝑒𝑡 values
was chosen: {230, 240, 250}.
Deadband value was chosen on basis of probability 𝑝∗ in Fig. 3. In addition, it must be noted that chosen value
∆𝐿𝑄𝐼𝑠𝑒𝑡 = 20 is the upper limit of this parameter based on the restrictions discussed in the previous paragraph. When
choosing, not only the probability of missing LQI value is important, but also the probability of such value, which forces
node to move, 𝑝𝑜𝑢𝑡 . This probability depends not only on 𝑝∗ , but also on driver subsystem of the platform’s (its speed and
updating period of the LQI values). The probability of a node moves out the deadband is defined as the probability of
obtaining N consecutively measured LQI values that exceed ∆𝐿𝑄𝐼𝑠𝑒𝑡 limit. Here N depends on the node speed 𝑣𝑛𝑜𝑑𝑒 and
the measurement period 𝜏𝑛𝑜𝑑𝑒 according to the formula:

𝑝∗ 𝑁 𝑝∗ 𝑑(∆𝐿𝑄𝐼𝑠𝑒𝑡 )⁄(𝑣𝑛𝑜𝑑𝑒 ∙𝜏𝑛𝑜𝑑𝑒 )
𝑝𝑜𝑢𝑡 = ( ) = ( ) (9)
2 2

These consideration sets a number of deadband ∆𝐿𝑄𝐼𝑠𝑒𝑡 = {2,4,5,10,15}.
The experiment was conducted under conditions of line of sight between nodes outdoor, which minimized reflections,
interference and attenuation from obstacles.

5 Measurement results
Performance measurement parameters are:
- Setting 𝑡𝑠𝑒𝑡 ; This value depends on the speed of node, which takes a place for gather sensor information.
- Average value of distance between nodes in the steady state 𝑑𝑎𝑣𝑔 ; It allows to evaluate the offset error of the position.
- Standard deviation σ of distance d from the set point 𝑑𝑠𝑒𝑡 . This parameter shows the accuracy of position control for
node over time. The start point of parameter measuring corresponds to the time when the node reaches the point with 𝑑 =
𝑑𝑠𝑒𝑡
- Total time 𝜏𝑚𝑜𝑣𝑒 of motors turning on. This parameter helps to calculate the degree of use of motor subsystem in
transient process and after. Movements a node around setpoint cause fast depletion of node battery.
Measured results for chosen cases of initial parameters are collected in table 1.

№ 𝑑𝑠𝑒𝑡 , m ∆𝑑𝑠𝑒𝑡 , m 𝐿𝑄𝐼𝑠𝑒𝑡 ∆𝐿𝑄𝐼𝑠𝑒𝑡 𝑡𝑡𝑜𝑡 , 𝑚 𝑡𝑠𝑒𝑡 , s 𝑑𝑎𝑣𝑔 , m 𝜎, m 𝜏𝑚𝑜𝑣𝑒 , s
1 45 3,5 250 2 500 442 45,9 0,8 344
2 45 6 250 4 220 166 - - 104
3 60 8,8 240 5 500 212 64,9 4,81 368
4 60 17,5 240 10 500 160 63,7 3,63 248
5 75 17,5 230 10 500 224 77,6 3,1 338
6 75 26,3 230 15 500 198 81,2 3,08 294

Typical dependences of the distance on time for experiments 1, 4, 6 are shown in Fig. 5-6.
60

0
0 50 100 150 200 250 300 350 400 450 500

Fig. 5. Time dependence of the distance between mobile node and the sink at 𝑑𝑠𝑒𝑡 = 45 𝑚 (𝐿𝑄𝐼𝑠𝑒𝑡 = 250), ∆𝑑𝑠𝑒𝑡 = 6 𝑚
90
80
Distance from start point, m

70
60
50
40
30
20
10
0
0 100 200 300 400 500
Time from the beginning of the experiment, s

Fig. 6. Time dependence of the distance between mobile node and the sink at 𝑑𝑠𝑒𝑡 = 60 𝑚 (𝐿𝑄𝐼𝑠𝑒𝑡 = 240),
∆𝑑𝑠𝑒𝑡 = 17,5 𝑚.
The corresponding time dependences of measured LQI parameter are shown in Figs. 7-8.
260

250

LQI 240

230

220

210

200
0 100 200 300 400 500
Time, s

Fig. 7. Time dependence of the distance between mobile node and the sink at 𝑑𝑠𝑒𝑡 = 45 𝑚 (𝐿𝑄𝐼𝑠𝑒𝑡 = 250),
∆𝐿𝑄𝐼𝑠𝑒𝑡 = 2.

260

250

240
LQI

230

220

210

200
0 100 200 300 400 500
Time, s

Fig. 8. Time dependence of the distance between mobile node and the sink at 𝑑𝑠𝑒𝑡 = 60 𝑚 (𝐿𝑄𝐼𝑠𝑒𝑡 = 240),
∆𝐿𝑄𝐼𝑠𝑒𝑡 = 10.

5.1 Setting time, required for node takes place
According to the table 1 the fastest way to the set point was shown in cases 2 and 4. This is can be explained by
reasons: a small target distance (𝑑𝑠𝑒𝑡 = 45 m in experiment 2) and a wide deadband (∆𝑑𝑠𝑒𝑡 = 17.5 m in the experiment 4).
The small target distance allows node to reach the operating point with high LQI values in short time, but at the same time,
by reducing average distance between nodes, the total network coverage area is reduced. On the other hand, considering
the random values LQI in feedback and small values of the deadband, the probability of a measurement miss is high, that
leds to often pauses in the platform engine moving and increases the time to reach the error bound (as, for example, in
experiment 1). Thus, considering the setting time, the optimal combination is: average distance between nodes (60-70m)
and average deadband (10-20m).
5.2 The accuracy of node B position control
In experiments 1,3,5 the setpoint distance was increased from 45 to 75 m. The accuracy of position of the motor
subsystem of the mobile node decreases with increasing required distance. This is due to the fact that with increasing
distance, the probability of LQI misses increases, which makes the control process more complicated. It is seen that in
experiment 6, small changes in the position of the node lead to significant changes in LQI value, which reduces the control
quality, but can be compensated by an increase of deadband (up to 26.3 m). The magnitude of standard deviation for the
distance does not increase significantly. This fact proves data shown in Fig. 2. So, we can conclude that for considered
combinations of setpoint distance and deadband, all experiments show required control quality.

5.3 Efficiency of motor subsystem using
Mobile nodes in wireless sensor networks have autonomous power supply for both the control and drive parts.
Therefore, an optimal movement strategy is required both for moving to set point and for maintaining the position. In this
paper, this parameter can be indirectly estimated as the total time, when the platform moves. The shortest moving time was
demonstrated in experiment 4 (𝑑𝑠𝑒𝑡 = 60 𝑚, 𝐿𝑄𝐼𝑠𝑒𝑡 = 240 и ∆𝑑𝑠𝑒𝑡 = 17,5𝑚).
Thus, summarizing the analysis of measured results we can describe each of experiments in terms of one of
performance indicators: experiment 1 demonstrated the poor quality of regulation due to small deadband. This drawback
does not allow the node to reach setpoint in the shortest time (time was spent on stopping due to missed measurements of
LQI value); experiment 2 with small setpoint distance shows a short settled time. During experiment 3 we set deadband
and the platform also reaches the setpoint point in a long time. Experiment 4 shows the optimal combination of
parameters (𝑑𝑠𝑒𝑡 = 60 𝑚, 𝐿𝑄𝐼𝑠𝑒𝑡 = 240 и ∆𝑑𝑠𝑒𝑡 = 17,5𝑚), in which the node holds on the distance, while spending a
small amount of energy on moving. Experiments 5 and 6 test the system for low LQI values, when probability of
measurement misses increases. This disadvantage can be compensated by wide deadband (Experiment 6). However, this
set of parameters is not efficient in term of battery usage.

6 Conclusions
In this paper, we research practical aspects of mobile node position control in wireless sensor network using the
measured LQI signal values. A number of experiments was conducted to obtain an optimal combination of control
parameters. Existing methods for distance estimation do not take into account properties of position regulator (type of
regulation, control parameters, random values of feedback and so on).
We propose to estimate distance from measured LQI values in wireless communication. The dependence of LQI value
on the distance, and standard deviation for LQI values has been measured. Based on the normal distribution law of a random
variable, an estimation of the measurement error probability, which can be led to harmful position changes of node. These
researches allows to obtain a number of control parameters for three-position regulator, which controls the motor subsystem
of node: setpoint (𝑑𝑠𝑒𝑡 = 40. .100m) and deadband (∆𝑑𝑠𝑒𝑡 = 3..25m).
The experimental model was assembled on a wheeled platform with DC motors controlled by the ATmega328 MC.
As a communication module, the ZigBee XBee S2C module was used, which provided the physical standard IEEE
802.15.4.
Measured results show the optimal combination of control parameters 𝑑𝑠𝑒𝑡 = 60𝑚, 𝐿𝑄𝐼𝑠𝑒𝑡 = 240 и ∆𝑑𝑠𝑒𝑡 = 17,5𝑚
for chosen conditions. When distance setpoint exceeds the optimum, link quality decreases, and accuracy of node position
control worsens. Reducing the deadband causes the platform to make unnecessary movements, because misses of
measurement LQI quickly move the platform away from optimal place. Increasing the deadband ∆𝑑𝑠𝑒𝑡 > 20 m reduces the
accuracy of position control near the operating point.
Future research will be aimed at increasing the number of mobile nodes to obtain a coverage area by sensors
network. The question of optimal control law also remains open, and PI or PID regulator may be interesting.
References
1. IEEE 802.15.4-2006, “Wireless Medium Access Control (MAC) and Physical Layer (PHY) Specifications for Low-Rate
Wireless Personal Area Networks (LR-WPANs),” IEEE Computer Society, September 2006.
2. ZigBee specification, version r17, ZigBee Alliance, January 2008.
3. Zhu, Ch.,Zheng, Ch., Shu, L., Han., G, (2012) A survey on coverage and connectivity issues in wireless sensor networks,
Journal of Network and Computer Applications, 35, pp/ 619–632.
4. Zou Y, Chakrabarty K. Sensor deployment and target localization based on virtual forces. In: INFOCOM 2003. Twenty-
second annual joint conference of the IEEE computer and communications. IEEE societies, vol. 2; 2003. p. 1293-303.
5. Wang G, Cao G, La Porta T. Movement-assisted sensor deployment. In: INFOCOM 2004. Twenty-third annual joint
conference of the IEEE computer and communications. IEEE societies, vol. 4; 2004. p. 2469-79.
5. Getting, I. (1993) The global positioning system. IEEE Spectrum 30, 12, 36–38.
6. Tamboli N, Younis M. Coverage-aware connectivity restoration in mobile sensor networks. In: IEEE international
conference on communications, 2009, ICC’09; 2009. p. 1-5.
7. Azmi N.A., Samsul S., Yamaba Y. Yakub M., Ismail M., Dziyauddin R. A Survey of Localization using RSSI and TDoA
Techniques in Wireless Sensor Network: System Architecture. 2018 2 nd International Conference on Telematics and Future
Generation Networks (TAFGEN). P. 131-136.
8. Langendoen. K. & Reijers. N, (2003. November). Distributed Localization in Wireless Sensor Networks: A Quantitative
Comparison. The International Journal of Computer and Telecommunications Networking. Vol. 43. Issue 4. North-
Holland. Elsevier.
9. Jiugiang Xu. Wei Liu. Fenggao Lang. Yuanyuan Zhang and Chenglong Wang. “Distance
Measurement Model Based on RSST in WSN” Wireless Sensor Network (SciRes). 2010, Vol. 2. PP.
606-611.
10. K. Vadivukkarasi and R. Kumar, “A New Approach for Error Reduction in Localization for
Wireless Sensor Networks”. Int. J. on Recent Trends in Engineering and Technology, Vol. 8. No. 2.
Jan 2013.
11. Luomala J. Hakala I., Analysis and Evaluation of Adaptive RSSI-based Ranging in Outdoor
Wireless Sensor Networks. Ad Hoc Networks, 2019, Volume 87. pp. 100-112.
12. Pita, R., Utrilla, R., Rodriguez-Zurrunero, R., Araujo, A., Experimental Evaluation of an RSSI-
Based Localization Algorithm on IoT End-Devices (Basel, Switzerland), 2019 Volume 19(18) pp. 1-24.
13. A. Harter, A. Hopper. P. Steggles, A. Ward, and P. Webster. 1999. The anatomy of a context-aware application.
Proceedings of the 5th Annual ACM/IEEE Intemational Conference on Mobile
Computing and Networking (MobiCom’99), 59-68.
14. B.-S. Choi, J.-W. Lee, J.-J. Lee. and K.-T. Park. 2011. A hierarchical algorithm tor indoor mobile
robot localization using RFID sensor fusion. IEEE Trans. Industr. Electron. 58, 6, 2226-2235.
15. Horvat. G., Sostari. D., Zagar. D. (2012) Power Consumption and RF Propagation Analysis on
ZigBee XBee Modules for ATPC, Proceedings of 35th International Conference or Telecommunications and Signal
Processing. TSP, Article Ne 6256286, 222-226.
16. Chehri, A., Jeon. G., Choi B. (2013) Link-Quality Measurement and Reporting in Wireless
Networks, Sensors. 13. 3066-3076.
17. Titaev A., Ermakov A., Gorshkov N., Location Management of Mobile Nodes in Low-Power Wireless Sensor Network
Using Link Quality Metric. Information technologies, telecommunications and control systems 2018. CEUR Workshop
Proceedings, Vol-2298, p. 7.
18. D. El Houssaini, S. Khriji, K. Besbes, O. Kanoun, Performance Analysis of Received Signal Strength and Link Quality
in Wireless Sensor Networks. 15th International Multi-Conference on Systems, Signals & Devices (SSD), 2018.