=Paper=
{{Paper
|id=Vol-2298/paper7
|storemode=property
|title=Location Management of Mobile Nodes in Low-Power Wireless Sensor Network Using Link Quality Metric
|pdfUrl=https://ceur-ws.org/Vol-2298/paper7.pdf
|volume=Vol-2298
|authors=Alexander Ermakov,Nikita Gorshkov,Alexander Titaev
}}
==Location Management of Mobile Nodes in Low-Power Wireless Sensor Network Using Link Quality Metric==
https://ceur-ws.org/Vol-2298/paper7.pdf
Location Management of Mobile Nodes in Low-Power Wireless Sensor
Network Using Link Quality Metric
Ermakov, A. Gorshkov N.
Ural Federal University Ural Federal University
Ekaterinburg, Russia Ekaterinburg, Russia
alexerm99@mail.ru ngisport110@gmail.com
Research head
Titaev A., PhD., Associate professor,
Ural Federal University
Ekaterinburg, Russia
alexander.titaev@gmail.com
Abstract
In the article a new method for relative location estimation of mobile nodes
in wireless sensor networks based on IEEE 802.15.4 standard has been proposed.
This approach uses link quality indicator (LQI) provided by transceiver chip.
Proving experimental measurements shows applicability of proposed method. The
result is the interpolation formula for the measured relationship between LQI value
and the distance between nodes.
1 Introduction
Wireless sensor networks (WSN) based on the IEEE 802.15.4 standard [1] (low-speed personal area networks)
are widely used to measure and transmit parameter values in a number of technical areas (natural habitat monitoring,
human environment, military tasks, etc.). This standard provides physical and link layer protocols for common industrial
bands (2.4 GHz). At recent time, Recently, WSNs are uses for data aggregation in a field where sensor nodes are
deployed in random manner. Such network forms a route tree structure with one gathering node (sink) and a number of
sensor nodes (end nodes). One of high level protocols in this case is ZigBee specification that provides network
deployment for devices of simplified functionality of automation (IoT, smart home, etc.) [2]. The ability of nodes to move
autonomously in a field significantly extends the functionality of WSN. Mobile sensor networks are used for tasks of
static and dynamic coverage, or for more complex, coupled with the movement, algorithms (perimeter patrol, sweeping
coverage, etc.). A review of possible applications and the most typical algorithms has been shown in the work [3]. The
initial location of nodes may be unknown (if nodes were deployed in random manner, for example, by dropping from an
airplane). Thereafter, the most common task for mobile WSN performance analysis is the problem of optimal nodes
deployment for a given field. Each point in a field must be covered at least one sensor, and the total number of involved
nodes must be minimal [3]. Two examples of different deployments are shown in the Figure.1 (a – non-optimal
deployment; b – optimal).
Effective location management allows to increase network performance parameters: node lifetime, robustness of
the coverage, energy of nodes. For proper control, node coordinates must be estimated or calculated using one of the
positioning technic. Currently, there are many approaches to determining the node coordinates: from quite expensive and
accurate (GPS) to cheap, but low accurate that based on RSSI signal. Also, the RFID-based solutions of localization are
uses for indoor conditions. However, it should be noted, that most of the works prove proposed approaches by network
simulations only. On the other hand, the experimental deployment of such networks are described significantly rarely.
Fig.1. Examples of sensor nodes deployment in closed field (a – non-optimal deployment, b – optimal
deployment)
2 Existing approaches for the calculation of WSN node coordinates
The paper [4] gives a survey of the existing methods for calculation coordinates of a node. There are absolute and
relative location management methods. As an example of absolute positioning scheme, one of the satellite navigation
systems (GPS, Galileo, GLONASS) [5] can be discussed. However, the use of this localization method is applicable only
for outdoor environment in places where the signal from satellites can be received. In addition, receiving equipment
increases the price of a node and its power consumption. Methods for determining the relative position in field include the
use of radio communication parameters (RF) for this purpose [6]. As a rule, these approaches are based on the evaluation
of the received RSSI (received signal strength indicator). However, as shown in [7], the use of this method allows to
estimate the distance between nodes with low accuracy due to the fact that the value of RSSI is influenced by many factors
(external noise, the presence of obstacles, the antenna orientation). Thus, it can be noted that the using of RSSI requires to
supplement by another measuring scheme to improve an accuracy in distance measurement. To overcome these
shortcomings, a combination of radio and ultrasonic rangefinder evaluation was proposed [8]. Some authors propose the
installation of an additional ultrasonic or infrared transmitter and receiver on the mobile platform. In the case that operating
field is closed and can be equipped the special active labels with known coordinates it is possible to orient nodes in the
field using information from this pre-deployed “anchors”. RFID-based solution are widely used in these purposes [9]. In
this work, we propose the approach for estimate relative distance between nodes using existed transceiver chip for ZigBee
network communications. This method uses LQI (Link Quality Indicator) that is described in IEEE 802.15.4 standard and
estimates quality of communication link between the nodes.
3 Proposed method of relative distance calculation
According to ZigBee specification [2], the routing algorithm for transmission a message from end nodes to the sink
calculates route metric, which depends on quality of communication link between nodes. The cost of the route is calculated
as the sum of the metric values Cij :
ǡ
ʠ ൌ ቐ ͳ ሺͳሻ
݉݅݊ ൭ǡ ݀݊ݑݎ൬ ସ ൰൱
where in second option is an integral estimation of link quality between nodes i and j. This value takes into account the
quality of communication indirectly, and equals the probability of successful transmission throw the link. According to [2],
the must be estimated for each incoming packet. The simplest method of calculation is based on the value of the link
quality indicator (LQI), which is proposed by IEEE 802.15.4-2003 standard at physical and channel layer. The standard
proposes to estimate LQI with the help of the energy of received signal or signal/noise ratio, or as a combination of both
methods. The LQI value should be between 0 and 255, where 0 is the value for the worst signal quality and 255 is for the
best one. The relationship between LQI and the distance between nodes is considered only in a few works: [10] (outdoor
and indoor), [11] (indoor). The main drawback in previous provided results is that the algorithm for calculation LQI
depends on a chip manufacturer. Thus, the data published on this topic mainly concern Chipcon CC2420 chip and cannot
be extrapolated to other chip models. However, according to [10,11], we can assume that the relationship can be
interpolated by a simple piecewise linear function:
ܵ ൌ ݂൫ܫܳܮ ൯ ൌ ܣή ܫܳܮ ܤሺʹሻ
where A and B – coefficients that depends on specific hardware platform. To prove this assumption, a number of
experiments to measure the relationship between the distance and LQI has been provided. As hardware platform we chose
communication module XBee S2C (Digi Inc.) with Ember's EM357 chip. This module was connected to Arduino-based
platform for simulation of sensor node functionality.
4 Experimental setup
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 indoor and outdoor in the absence of people at a line of sight between the
modules. Packet payload consists of 100 bytes (maximum length of payload that is defined in ZigBee specification). The
transmission rate is 1 packet per second. Fig.2 shows general view of the setup.
Fig.2. Experimental setup for LQI measurement
The total number of transmissions for each experiment is 1000. Measured parameters:
- average LQI, which is provided by the IEEE 802.15.4 standard and implemented in EM357 chip as the number in
the range from 0 (the worst quality) to 255 (the best) (indoor and outdoor)
- average RSSI, which is measured by chip software for each incoming packet (indoor)
- packet delivery ratio (outdoor)
The LQI values are changed by changing the distance between nodes. Also for indoor case, a number of
measurements was carried out for different transmission powers. This parameter in XBee modules can be changed in two
ways: a) set the transmit power level directly as the number in range 0 - 4, where 0 is the lowest power (-5 dBm), 4 is the
highest (+5 dBm). b) enable boost power mode, which increases the transmission power by 2 dBm. Three different power
modes were selected for the simulation: mode 1 - power level (PL) set to 0 with disabled boost mode (BM = 0); mode 2 –
PL = 4 with disabled boost mode (BM = 0); mode 3 – PL = 4 with enabled boost mode (BM = 1).
Outdoor measurements were carried out only for mode 3 because this is the most powerful mode, and this case must
test the module in extreme long distances. For outdoor case the packet delivery ratio (PDR) was measured. This parameter
must be taken into account for pointing the moment when modules lost connection and can not resume it in all observed
time interval.
5 Results of the measurements
Experimental results for indoor measurments are shown at Fig. 3,4
Fig.3. Influence of the distance between nodes on measured average RSSI value for indoor
Fig.4. Influence of the distance between nodes on measured average LQI value for indoor
Results show that small distances (up to 50m) do not affect on LQI, and quality of communication does not depend
on the distance in this interval. At the same time, the RSSI changes significantly and unpredictably in this interval. This
can be explained by the fact that RSSI value in the receiver depends on not only the signal from the transmitting module,
but also the third-party transmitting devices, which usually present indoor area and work at the same frequency range (Wi-
Fi access points, mobile user devices, etc.). LQI is calculated as the packet delivery ratio, and have good correlation with
the distance. Small fluctuations of its values are possible due to interference from walls, floors and roof. After the distance
was increased to 40m, the LQI begins to decrease linearly (see Fig.4).
For the maximum transmission power, a piecewise linear interpolation function LQI(S) was provided:
ʹͷͷǡ ˒˓ˋܵ ൏ ͷͲˏ
ܫܳܮሺܵሻ ൌ ൜ ሺ͵ሻ
െͲǡͷ ή ܵ ʹͺͶǡ˒˓ˋܵ ͷͲˏ
The inverse relationship S(LQI) can be expressed from (3) for distances that exceed 50m:
ܵ ൌ ݂൫ܫܳܮ ൯ ൌ െͳǡͷ ή ܫܳܮ ͶͻǡʹͷሺͶሻ
This experimental results good agree with previous published researches. Obtained piecewise linear function (4) can
be used to estimate distance between ZigBee modules. It must be noted that this relationship is true for large distances
(more than 50m). Smaller distances saturates LQI values that close to maximum 255.
The outdoor case result is shown at Fig.5
Fig.5. Influence of the distance between nodes on measured average LQI and PDR for outdoor
These results (see Fig.5) is similar the ones for indoor case: LQI decreases linearly, when the distance increase. Packet
delivery ratio also decreases with the distance but the curve “jumps” more unpredictably. Therefore, PDR is less applicable
for distance estimation, then LQI.
6 Conclusions
In this paper the problem of distance estimation between nodes of mobile ZigBee WSN has been considered. For this
transmission stack, the distance between devices can be measured with the help of link quality indicator, which is
introduced in IEEE 802.15.4 standard for LowPAN networks. LQI calculation is provided by transceiver chip for each
received packet. The proposed method do not uses any additional equipment (ultrasonic or infrared transceivers, GPS-
receivers, etc.). To prove the technic, a number of experiments were carried out to measure the relationship between
transmission distance and RSSI signal, and between distance and LQI. The results of the experiments show that the use of
LQI for distance estimation is more preferable in comparison with the RSSI and PRD value. Relationship S(LQI) can be
interpolated with the help of piecewise linear function.
Further efforts will focus on construction of a mobile platform that allows an autonomous location management that
will use proposed approach. Practical issues of interaction between several such platforms in the deployment of coverage
network also remain open for research.
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. Tas, B., Altiparmak, N., Tosun, A. (2014) Low-Cost Indoor Location Management for Robots Using IR Leds and an
IR Camera, ACM Transactions on Sensor Networks, 10(4), Article № 63.
5. Getting, I. (1993) The global positioning system. IEEE Spectrum 30, 12, 36–38.
6. P. Bahl and V. Padmanabhan. 2000. Radar: An in-building RF-based user location and tracking system. Proceedings of
the 19th Annual joint Conference of the IEEE Computer and Communications Societies (INFOCOM’00). 775–784.
7. T. Stoyanova, F. Kerasiotis, A. Prayati, and G. Papadopoulos. 2007. Evaluation of impact factors on RSS accuracy for
localization and tracking applications. In Proceedings of the 5th ACMInternationalWorkshop on Mobility Management
and Wireless Access (MobiWac’07). 9–16.
8. 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 International Conference on Mobile Computing and Networking
(MobiCom’99). 59–68.
9. B.-S. Choi, J.-W. Lee, J.-J. Lee, and K.-T. Park. 2011. A hierarchical algorithm for indoor mobile robot localization
using RFID sensor fusion. IEEE Trans. Industr. Electron. 58, 6, 2226–2235.
10. Horvat, G., Sostari, D., Zagar, D. (2012) Power Consumption and RF Propagation Analysis on ZigBee XBee
Modules for ATPC, Proceedings of 35th International Conference on Telecommunications and Signal Processing, TSP,
Article № 6256286, 222-226.
11. Chehri, A., Jeon, G., Choi B. (2013) Link-Quality Measurement and Reporting in Wireless Sensor Networks,
Sensors, 13, 3066-3076.