Design of a Guideline for Range-based Localization Algorithms

Evaluation using Multiple Linear Regressions

Dhouha El Houssaini

1,2

, Zaid Abdullah

, Sabrine Kheriji

2,3

, Kamel Besbes

and Olfa Kanoun

Chair for Measurement and Sensor Technology, Technische Universit

at, 09111 Chemnitz, Germany

Microelectronics and Instrumentation Laboratory, Faculty of Sciences of Monastir, 1002, Tunisia

Centre for Research on Microelectronics and Nanotechnology, Technopark of Sousse, 4000, Tunisia

Keywords:

WSNs, Localization, Range-based, Multiple Linear Regression, Guideline, IoT.

Abstract:

Localization is an essential feature in numerous Wireless Sensor Network (WSN) applications, including

tracking, health monitoring, and military supervision. Analytical modeling and analysis of the localization

system remain challenging and infeasible since it offers oversimpliﬁed results with limited reliability to the

evaluated cases. Likewise, disseminating test-beds involves a lot of effort, making the simulation phase indis-

pensable to study the WSN localization. The deﬁned localization model needs to ensure solid and pragmatic

network assumptions during the simulation. However, most network simulators don’t meet speciﬁc criteria re-

lated to network deﬁnition, such as scalability and heterogeneity. As part of this endeavor, a guideline for eval-

uating and analyzing technical methods of range-based localization is developed. Multiple linear regression is

used to generate the different localization instances, which enables to support different and non-dependent pa-

rameters. The developed guideline for range-based localization is tested and validated for existing localization

solutions.

1 INTRODUCTION

In Wireless Sensor Networks (WSNs), sensor nodes

are installed in the ﬁeld of interest to monitor and

provide certain physical and environmental informa-

tion, such as the temperature, humidity, and activity

of monitored quantities (Kanoun et al., 2021). The

Localization of these installed nodes is critical and

required for different applications, like object track-

ing, process supervision, and monitoring (El Hous-

saini et al., 2020; Naguib et al., ). Besides, following

the urgent trend to include the Internet of Things (IoT)

concept, the location information remains critical and

necessary for remote control and supervision activi-

ties (Ahmed Mansoor and Irtaza, 2019). The knowl-

edge of the position of installed nodes serves during

the network activities such as the routing, data trans-

mission, and network topology (Khriji et al., 2018;

Paul and Sato, 2017; Egea-Lopez et al., 2005; Ab-

delhabib and Brahim, 2008). Indeed, the node’s lo-

cation is critical to ensure better network function-

ality and enhance the lifetime of the node itself and

the complete network. It is essential to effectively

decide upon the localization technique for the net-

work assumptions and characteristics, such as the net-

work size, environment (indoor/outdoor, the existence

of obstacles, mobile system, etc.), hardware speciﬁ-

cation (sensing and communication range and power

module) and energy constraints. Thus, the realization

of a speciﬁc test-bed is extremely expensive and chal-

lenging. Furthermore, the implementation of real ex-

periments always takes more time than the simulation.

Therefore, simulation phase is crucial for the develop-

ment of WSNs.

The use of WSNs simulators allows users to sep-

arate numerous factors to evaluate and test their ap-

proaches, like ﬂexible network size, different sensing

and communication ranges, and predeﬁned commu-

nication modules. Various free and open-source sim-

ulators are commercialized in the market, with some

advantages. NS2 is a discrete event-driven simula-

tor, mainly used for academic research in the areas

of computer networks, MANETs, and WSNs (Hogie

et al., 2006). It is built based on C++ and supports

a wide range of protocols. It also provides complete

support of communication protocol. The main lim-

itation of NS2 is its graphical interface, as it only

provides a simple reﬂection of the network. It does

not allow the scalability and extension of the net-

work. Furthermore, NS3 was developed based on the

256

El Houssaini, D., Abdullah, Z., Kheriji, S., Besbes, K. and Kanoun, O.

Design of a Guideline for Range-based Localization Algorithms Evaluation using Multiple Linear Regressions.

DOI: 10.5220/0011013100003118

In Proceedings of the 11th International Conference on Sensor Networks (SENSORNETS 2022), pages 256-262

ISBN: 978-989-758-551-7; ISSN: 2184-4380

main concepts of NS2 (Nsam, ). It integrates Ne-

tAnim module for the graphical simulation of a net-

work model. Additionally, different simulators in-

tegrate GUI interfaces. One example is OMNET++

(Omnet, ), which is an example of a modular discrete

event simulator written in C++ and provides a power-

ful GUI library for animation and tracing and debug-

ging support. However, their libraries remain limited

compared to other simulators, as they don’t support

enough protocols for communication and transmis-

sion. For instance, J-sim (Java Simulator) (JSim, )

is a compositional simulation environment. It is built

according to the component-based software paradigm

called autonomous component architecture (ACA). J-

Sim has the advantage of supporting many protocols,

including a highly detailed simulation of WSNs plus

Localization features. However, the JSim develop-

ment is closed, and its scale is medium.

This paper aims to develop a generalized guide-

line, which identiﬁes the appropriate localization

technique for the test network. It considers the def-

inition of the deployment environment (Network size

and path loss exponent) and the system assumptions,

such as the sending power and the communication

range. The guideline implements a multiple regres-

sion module to build the decision upon the choice of

the localization technique. The comparison is based

on the localization accuracy and measurement error.

The remainder of the paper is organized as fol-

lows: Section 2 illustrates the related works pre-

senting an overview of range-based localization tech-

niques. In section 3, the proposed evaluation guide-

line is provided. Section 4 presents some test and

evaluation results. Finally, a conclusion is provided

in Section 5.

2 RELATED WORKS

The localization techniques can be classiﬁed into two

main categories: Range-based and range-free. The

range-based technique uses the distance or range in-

formation to determine the position of a node, such as

Global Positioning System (GPS), Angle of Arrival

(AoA), Time of Arrival (ToA), Time Difference of Ar-

rival (TDoA), and Received Signal Strength Indicator

(RSSI) (Alsheikh et al., 2014; Bekcibasi and Tenruh,

2014). As for the range-free techniques, the posi-

tion estimation relies on connectivity information be-

tween two nodes (Singh and Sharma, 2015). They use

radio connectivity to communicate among the nodes

to estimate their locations, so no coordinates system

is used. The position of the target node can be ob-

tained by applying geometrics relations. Most range-

free techniques are based on the number of surround-

ing neighbors known as DV-Hop, Approximate Point-

In-Triangulation test (APIT), or centroid system. DV

hop (Hu and Li, 2013; Liu et al., 2016) estimates the

range between nodes using the hop count. Indeed, by

comparing and evaluating the performance and lim-

itations of these methods, it is necessary to ﬁnd a

trade-off between centralized and distributed topolo-

gies combining low energy consumption and local-

ization accuracy. Considering the application require-

ments and constraints, such as low energy consump-

tion, secure data transmission, and efﬁciency, the lo-

calization technique can differ from one application

to another. For this purpose, it is necessary to ef-

ﬁciently simulate and evaluate the localization tech-

nique before the realization and implementation to en-

sure better network performance. Various research

efforts were performed to qualitatively classify and

review the range-based localization techniques. Us-

ing the Web of Science database, research for English

articles including the words ”localization WSN* sur-

vey” or ”Localization WSN* review” or ”localisation

WSN* survey” or ”Localisation WSN* review” (* to

include the plural) in their title, abstracts or keywords.

The ﬁrst-round results in 53 review papers related to

localization techniques in WSN. After reading their

abstracts, 42 papers are excluded because they are un-

related to reviews on Localization in WSN. The 11

remaining papers are analyzed here.

Authors in (Saad et al., 2018) classiﬁed localiza-

tion techniques into ﬁve categories, but they focus

only on the ranging technique, which is divided into

range-based and range-free methods. An overview of

the AoA, TDoA, and RSSI in the range-based meth-

ods is provided. Four range-free techniques are il-

lustrated: Centroid, APIT, DV-Hop, and Amorphous.

To compare the performance of the described tech-

niques, some metrics are considered, including ac-

curacy, scalability, cost, and power consumption. In

(Sneha and Nagarajan, 2020), the localization tech-

niques are classiﬁed as proximity-based, range-based,

and range-free Localization. The factors inﬂuenc-

ing the localization measurement are studied. A gen-

eral comparative analysis of only the range-based and

range-free methods is discussed in terms of accuracy,

cost, power consumption, and additional hardware re-

quirements. Then, a comparison between RSSI, AoA,

TDoA/ToA is performed by describing their advan-

tages and disadvantages. In (Kheliﬁ et al., 2015),

the advantages and drawbacks of the range-based and

range-free localization technique are described. Au-

thors proved that it is hard to ﬁnd the best algorithm,

which dominates by all criteria. Authors in (Ismail

et al., 2021) discussed the localization techniques us-

Design of a Guideline for Range-based Localization Algorithms Evaluation using Multiple Linear Regressions

257

ing GPS, range-based methods including RSSI, ToA,

AoA, and TDoA and range-free methods such as cen-

troid, DV-Hop, Amorphous. The discussed reviews

are summarized in Table 1.

Table 1: List of survey papers studying range-based local-

ization technique.

Ref. Comparison parameters

(Kheliﬁ et al., 2015)

Energy, accuracy, complexity, hardware

requirement, communication trafﬁc, coverage

(Saad et al., 2018) Accuracy, scalability, cost, power consumption

(Sneha and Nagarajan, 2020)

Accuracy, cost, power consumption,

requirement for additional hardware

(Ismail et al., 2021)

Principal of operation, special hardware,

attenuation problem, cost

Existing reviews perform the comparison with

certain conﬁguration parameter, such as the network

size, number of nodes, deployment strategy, energetic

model. Thereby, obtained results remain valid only

for some instances, and there is no generalized eval-

uation combining all performance metrics. Indeed,

there is no quantitative comparison aiming to improve

the existing techniques. Thus, which algorithm is the

best for a speciﬁc scenario remains open. These short-

comings motivate us to develop a methodology for a

generalized comparison. The paper aims to develop a

guideline that englobes the most relevant range-based

localization techniques.

3 DESIGN OF THE EVALUATION

GUIDELINE

In the localization method, the estimation of the

coordinates of nodes varies from one technique to

the other, where the network deﬁnition and char-

acteristics describe the input parameters of the sys-

tem. Sine, the performance evaluation of these tech-

niques, presents a multidimensionality problem, ma-

chine learning-based model offers a promising solu-

tion to support the use of various non-dependent in-

put parameters. The localization evaluation guideline

offers a generic comparison module of the basic local-

ization techniques. The system is based on a multiple

linear regression module, which enables an overview

of the complete localization scenario, where a predic-

tive module is attributed. In this guideline, the path

loss exponent, the communication range of nodes, and

the sending power all contribute to determining accu-

racy and position error. The choice of a suitable local-

ization technique depends on the network speciﬁca-

tion and the application requirement and is highly in-

ﬂuenced by these deﬁnitions and initializations. Ob-

tained results are identiﬁed for the four main range-

based localization techniques. It offers an under-

standing of suitable localization techniques in terms

of localization accuracy. The system includes ﬁve

main blocks: Network initialization, localization in-

stance, data preparation, multiple regression module,

and evaluation (see Figure 1). In the network initial-

ization, parameters related to the network deﬁnition

are initialized.

Figure 1: Block diagram of the evaluation guideline for

range-based localization algorithms.

It contains information about the network size ex-

pressed as Width and Length, the path loss expo-

nent to highlight the environment characterization (in-

door/outdoor, free space/ interferences), the commu-

nication range of nodes, and the sending power. The

input parameters are as follows:

• Transmission power of the sensor node;

• Network size of the area of interest, Width ×

Length;

• Environmental coefﬁcient: Path loss exponent η;

• Communication range of sensor nodes R

;

Considering these inputs, the localization instance

is created for each localization technique concerning

its deﬁned equation. Later, the calculated data of each

localization instance are prepared as input to the re-

gression module to estimate the position of nodes.

After that, a prediction module and accuracy evalu-

ation are carried out. Once all instances are created

EWSN-IoT 2022 - Special Session on Energy-Aware Wireless Sensor Networks for IoT

258

and evaluated, the most suitable localization tech-

nique corresponding to the network initialization is

proposed in terms of localization accuracy and posi-

tion estimation error.

3.1 Localization Module

The ﬁrst phase of the guideline revolves around four

basic range-based wireless sensor localization algo-

rithms. The algorithms are implemented individually

using the algorithms themselves. Four different func-

tions are responsible for the Localization, which run

the following sequence.

• f un

ssi takes input and runs RSSI algorithm;

• f un

oa takes input and runs ToA algorithm;

• f un

doa takes input and runs TDoA algorithm;

• f un

oa takes input and runs AoA algorithm.

All these algorithms take the same set of inputs,

enabling respective functions to create the ﬁles and

save the data for several iterations as a .csv ﬁle. This

.csv is then later read by machine learning.

3.2 Regression Module

The performance and selection of machine learning

algorithms are solely based on the available datasets.

Although no algorithm has superiority over another

algorithm, there are some traits and properties of each

algorithm, which can be considered by tuning the

hyper-parameters. The regression module enables the

analysis of multi-factor data, which helps decide over

the most suitable localization techniques based on the

input parameters. The regression module permits to

translate the localization choice into a mathematical

model describing the relationship between the depen-

dent and independent variables. More precisely, the

regression module allows mapping the results based

on numeric inputs. The general mathematical module

of multiple linear regression is illustrated in Equation

= β

+ β

+ ... + β

+ ε (1)

where, for i = n observations, x

, y

are the depen-

dent and explanatory variables, respectively. β

is the

y-intercept (constant term), β

is the slope coefﬁcient

for each explanatory variable and is the residual value.

The input parameters of the regression module are

the localization instances of each localization tech-

nique. Distances between nodes are estimated based

on each range-based technique. Once the distances

are estimated, a simple trilateration method is carried

out to estimate the coordinates of nodes. 10000 sam-

ples for each localization technique are created based

on the experiment. 80 % of the total number of sam-

ples is deﬁned to train the regression module to iden-

tify each localization technique. The remaining 20 %

of samples are used later to evaluate and test the re-

gression module.

4 TEST AND EVALUATION

The design of the proposed guideline is based on in-

built, reusable modules, which use the Python pro-

gramming language. Python provides a powerful and

easy-to-use programming tool, which supports exten-

sive machine learning computations. Also, it allows

access to complete library support that integrates ma-

chine learning and mathematical models. A user-

friendly, easy-to-use, and ﬂexible guideline is pro-

posed in this work. It allows the user to deﬁne its

network parameters, like the network dimensions, the

path loss exponent, and sensor nodes characteristics.

Figure 2 illustrates the designed interface. The inter-

face allows the user to deﬁne all network assumptions

that characterize the test ﬁeld. As output and based

on the localization aspect, the decision of the suitable

localization technique is made based on the accuracy.

Figure 2: Illustration of the developed guideline; data inputs

and network deﬁnition.

In the background, the input variables are col-

lected to deﬁne the localization instance for each

range-based technique. The most suitable localization

technique is deﬁned according to the used nodes, lo-

calization error, and inaccuracy. Indeed, the proposed

system offers different graphical results to understand

the comparison results better. The ﬁrst graphical com-

parison is a histogram modeling, as presented in Fig-

ure 3. The illustrated results are for the localiza-

tion error over various possible nodes collections. It

presents a comparative analysis of the nodes deployed

in the system. This is an accuracy graph, which shows

the cumulative accuracy of the system. The x-axis

shows the average Localization by plotting the error

of each node, and the y-axis shows the total number of

nodes. It gives an impression of the density of nodes

and their respective localization errors.

Design of a Guideline for Range-based Localization Algorithms Evaluation using Multiple Linear Regressions

259

0 0.5 1 1.5 2 2.5 3

100

200

300

RSSI

Location error in m

Number of nodes

(a)

0 0.5 1 1.5 2 2.5 3

100

200

300

ToA

Location error in m

Number of nodes

(b)

0 0.5 1 1.5 2 2.5 3

100

200

300

TDoA

Location error in m

Number of nodes

(c)

0 0.5 1 1.5 2 2.5 3

100

200

300

AoA

Location error in m

Number of nodes

(d)

Figure 3: Error distribution of range-based localization

technique for a network size of 15 × 20 m

, communica-

tion range R

=, transmission power of −70 dBm and path

loss exponent of η = 4.

In this work, the inﬂuence of the network deﬁni-

tion is studied. In fact, during the localization phase,

nodes are placed in a speciﬁc network with speciﬁc

hardware characterization. Each parameter may di-

rectly impact the choice of the localization technique

itself. In the following, the guideline is tested for dif-

ferent scenarios: (1) inﬂuence of network size, (2)

inﬂuence of path loss exponent, and (3) inﬂuence of

communication range of the installed nodes. Figure

4 summarize the obtained results for the three test

scenarios. It proves that the choice of the suitable

localization technique is strongly dependent on the

network deﬁnition. For example, for large-scale net-

works, the outperforms other localization techniques

(Figure 4a ), whereas the works better for small-scale

networks. The path exponent presents the effect of the

network’s scale impact, as it deﬁnes the environmen-

tal characteristic. It helps to identify if there exists

some interference and shadowing in the network and

if the signal is propagating in the line of sight. Figure

4b illustrates the effects of the path loss exponent on

the choice of the Localization.

It is clear that for the free space environment

(η = 2) (Figure 4c ), the RSSI outperforms other tech-

niques, whereas, in the case of industrial environ-

ments and buildings (η = 2), the AoA technique gives

better results. In the last scenario, the communica-

tion range of the instated nodes is studied. The com-

munication range enables deciding if the network is

covered or not, which helps reduce energy consump-

tion and maintain communication between installed

nodes. If the communication range increases, the lo-

calization capabilities increase, which helps reduce

the measurement error.

(a)

(b)

(c)

Figure 4: Performance evaluation of range-based localiza-

tion techniques: (a) Inﬂuence of network size, (b) inﬂuence

of path loss exponent and (c) inﬂuence of communication

range.

Finally, the proposed guideline is validated using

different types of localization algorithms, and each

one is implemented using different methods. Many

EWSN-IoT 2022 - Special Session on Energy-Aware Wireless Sensor Networks for IoT

260

Table 2: Comparison of the developed guideline to the result of some localization algorithms.

Ref. Test ﬁeld (m

) Accuracy (%) Localization error (m) Computation time (s)

(Weingartner et al., 2009) 20

4 A + 25 NN

98 0.5 2.01

(Weingartner et al., 2009) * 98.32 0.256 4.07

(Rahman et al., 2012) 100

15 A + 35 NN

81 4 10.54

(Rahman et al., 2012) * 89.23 5.25 3.2

(Ahmadi and Bouallegue, 2015) 20

3 A + 15 NN

95 0.409 3.2

(Ahmadi and Bouallegue, 2015) * 97.49 0.34 4.79

(Jin et al., 2010) 100

30 A + 100 NN

65 9 5

(Jin et al., 2010) * 93 4.32 5.84

(Wang et al., 2009)

3 A + 1 NN

75 5 3.33

(Wang et al., 2009) * 93.77 0.95 5.7

scenarios validate our guideline to study the effects

of different parameters on the design. As seen in Ta-

ble 2, obtained results are compatible with the orig-

inal results reported by the selected localization al-

gorithms in their papers. Different localization al-

gorithms have been tested using the proposed guide-

line. The selected localization algorithm are chosen

based on their achieved localization accuracy. Be-

sides, the selected works implements some artiﬁcial

intelligence technique for the determination of the

nodes’ location. We selected different works based

on RSSI techniques only since its strongly dependent

of the deployment environment and sending power of

the nodes. As illustrated in Table 2, obtained results

remain compatible with those reported by selected lo-

calization algorithms in their papers. In (Weingartner

et al., 2009) authors propose an RSSI-based localiza-

tion system. Their simulation achieved 98 % accu-

racy, compared to our guideline, which shows 98.32

% with a reduced error. In (Rahman et al., 2012), au-

thors propose an RSSI-based system, which uses the

regression Tree by comparing its performance with

Least Squares Support Vector Regression and Multi

Layers Perceptron Neural Network. The evaluation

considers the localization error and the complexity of

the algorithm. Using the regression tree, they reached

81 % of localization accuracy. Considering the same

network deﬁnition, the proposed work in (Rahman

et al., 2012) is tested using the developed guideline,

where the localization accuracy reached 89 %. Simi-

larly, authors in (Ahmadi and Bouallegue, 2015) pro-

pose a ﬁngerprint-based localization scheme that con-

siders the channel impulse response to computing the

location of nodes. The distance and position esti-

mation is carried out using non-parametric kernel re-

gression. The localization accuracy reached 95 %,

whereas, in the developed guideline, the total local-

ization accuracy is around 97 %, with a minimal lo-

calization error of 0.34 m. In (Jin et al., 2010), the

RSS-based lateration method is used to compute the

location of nodes. In their work, the authors provided

two approaches, regression-based and correlation-

based. The regression-based approach uses linear re-

gression to discover a better ﬁt of the signal propaga-

tion model between RSS and the distance. In contrast,

the correlation-based approach utilizes RSS correla-

tion in the local area to obtain more accurate signal

propagation. As a result, they obtained 65 % for local-

ization accuracy. In paper (Wang et al., 2009), RSSI

information is used to estimate the position of nodes.

The estimated localization accuracy is around 75 %,

compared with the accuracy of the guideline of 93 %.

A preliminary study on the evaluation of range-

based localization techniques is proposed in this

work. An evaluation guideline is presented. It en-

ables to build a better knowledge of the performance

of the localization techniques with the deﬁnition of

the network assumptions, mainly, the network size,

communication range, and sending power. This pre-

liminary study provides a good insight for evaluating

the localization range-based technique, where the de-

veloped guideline is based on the different localiza-

tion instances. However, a more detailed study and

evaluation are required to ensure better functionality

of the developed guideline in terms of dependency to

the real-world scenario (e.g., Existence of obstacles,

hardware failure) and dependency on the localization

constraints (e.g., Energy consumption and lifetime).

As a future perspective, a study on design constraints

will be considered along with different localization

techniques. Moreover, the multiple linear regression

module will be compared with another possible alter-

native, which can improve the ﬂexibility and robust-

ness of the developed system.

5 CONCLUSIONS

A generic evaluation guideline based on multiple lin-

ear regression is developed for range-based localiza-

tion techniques. The results enable a better choice of

which standard localization technique to implement.

The localization techniques are evaluated based on

their accuracy and localization error. Having com-

Design of a Guideline for Range-based Localization Algorithms Evaluation using Multiple Linear Regressions

261

pared the guideline results with the results of the se-

lected localization algorithms, it was found that they

had been consistent with the original results of these

algorithms in their original papers. In the future, other

localization algorithms can be incorporated into the

guideline, which can be used by other researchers.

For better assumptions considerations, it is neces-

sary to include the energetic module and deployment

strategies in the design. Moreover, the evaluation

metrics (accuracy) can be extended to network life-

time, energy consumption, and communication.

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