Towards Intelligent Tuning of Frequency and Transmission Power

Adjustment in Beacon-based Ad-Hoc Networks

Javier Rubio-Loyola

, Hiram Galeana-Zapien

, Francisco Aguirre-Gracia

, Christian Aguilar-Fuster

Sandy Bolufé

, Cesar A. Azurdia-Meza

and Samuel Montejo-Sánchez

Centre for Research and Advanced Studies (CINVESTAV), Tamaulipas, Mexico

Department of Electrical Engineering, Universidad de Chile, Santiago, Chile

Keywords: Ad-Hoc Networks, Beacon-based, Genetic Algorithm, Transmission Power, Beacon Frequency.

Abstract: This paper presents a genetic-based approach to determine optimal values of frequency and transmission

power in beacon-based ad-hoc networks. The approach has been evaluated through simulations, and it has

demonstrated to be more efficient than a dynamic control of frequency and transmission power, with

reduction of up to 73% in packet collisions and with reduction of packet losses of up to 63% in an urban

scenario. The approach and the results presented in this paper represent our initial efforts towards a more

efficient control of beacon frequency and transmission power, which can exploit the benefits of a genetic-

based approach but that can be applied in runtime in practical scenarios.

1 INTRODUCTION

Intelligent transportation systems (ITSs) refer to the

integration of information and communication

technologies with transport infrastructures. The goal

is to design novel applications to enhance road

safety and traffic efficiency (Maimaris, 2016). To

this end, modern vehicles are equipped with multiple

sensors such as global positioning system (GPS)

receivers, proximity sensor, cameras, among others.

These sensors are used for different applications

such as parking assistance, lane keeping, pedestrian

detection. In this regard, information exchange

among vehicles is essential to expand the scope of

these applications. However, in order to provide

information to each vehicle, especially those that are

not in the field of vision of drivers, it is of

paramount importance the design of timely efficient

dissemination approaches.

A prominent approach to message dissemination

relies on the deployment of vehicular ad hoc

networks (VANETS). In a VANET, the vehicles are

equipped also with On-Board Units (OBUs) and air

interfaces allowing the information exchange via

either vehicle-to-vehicle (V2V) or vehicle-to-

infrastructure (V2I) communication paradigms

(Fazio, 2013; Reis, 2014). VANETs operate on the

dedicated short-range communication (DSRC)

spectrum at 5.9 GHz to be used exclusively for V2V

and V2I communications (Zhu, 2003). DSRC relies

on several standards designed for vehicular

communications, including the IEEE 802.11-OCB1

operation mode, formerly known as IEEE 802.11p

(IEEE, 2016), which defines physical (PHY) and

medium access control (MAC) layers for Wireless

Access in Vehicular Environments (WAVE) (IEEE,

2017).

VANETs can be considered as a subset of mobile

ad-hoc networks (MANETs), but they have specific

characteristics that distinguish them (Dorronsoro,

2014) in terms of topology changes, low link

availability, communication paradigms, etc. An open

research challenge in VANETs is how to provide

cooperative knowledge among vehicles, which in

turn is a basic requirement of multiple applications

of road safety and traffic management. This

cooperative knowledge is built upon the periodic

exchange of messages called beacons, which contain

important data, about the status of the vehicle, such

as position, speed, and acceleration (ETSI, 2014).

The beaconing process, allows the receiver vehicles

to create a Local Dynamic Map (LDM) based on

surrounding environment information, which is

essential for the proper performance of cooperative

awareness applications, which also require high

reliability and low delays. However, the wireless

cooperation between vehicles is a challenging

648

Rubio-Loyola, J., Galeana-Zapien, H., Aguirre-Gracia, F., Aguilar-Fuster, C., Bolufé, S., Azurdia-Meza, C. and Montejo-Sánchez, S.

Towards Intelligent Tuning of Frequency and Transmission Power Adjustment in Beacon-based Ad-Hoc Networks.

DOI: 10.5220/0006817306480656

In Proceedings of the 4th International Conference on Vehicle Technology and Intelligent Transport Systems (VEHITS 2018), pages 648-656

ISBN: 978-989-758-293-6

problem due to a large amount of dynamic data. In a

VANET, the problem becomes more stringent due to

the mutual interferences (Cailean, 2014).

It is worth noting that, while a fixed beacon

transmission rate can easily increase the channel

load and saturate the network, especially in

scenarios with high vehicle density (Schmidt, 2010),

a reduction of the beacon transmission rate may

result in a reduction of quality and freshness of the

information (Jiang, 2008). Consequently, the

position errors can impact the proper performance of

cooperative safety applications, which rely on real-

time accurate information. In this context, the

Vehicle Safety Communications Consortium

(CAMP, 2005), specifies 10 beacon/s as the

minimum beacon rate required by several

cooperative safety applications, while others can

demand up to 50 beacons/s.

In this regard, defining the beacon

communication parameters that can meet the

requirements of all applications for all potential

scenarios is a very complex task, since the beacon

requirements depend on application type (Sepulcre,

2011a) and vehicular context (Sepulcre, 2011b).

Therefore, it is essential to define the most relevant

metric for safety-critical applications. In this sense,

in previous work (Bolufé, 2017; Ortega, 2018) we

proposed the position error as the priority metric,

due to its impact on the timely detection of

potentially dangerous situations. More specifically,

in (Bolufé, 2017), we proposed an algorithm that

dynamically adjusts the beacon rate based on vehicle

movement status. This approach was experimentally

evaluated in (Ortega, 2018), using test bed

equipment for vehicular communications. The

objective of these previous works is twofold. On the

one hand, these previous works assess the dynamic

adjusting of the beacon rate to achieve a target

position error, that can meet the requirements of

cooperative safety applications. On the other hand,

driven by the vehicle movement status, the approach

adjusts the transmit power considering the channel

load and the beacon rate with the aim of reducing

packet collisions.

More recently, in (Bolufé, 2018), we propose the

use of a novel joint power & rate control distributed

algorithm in cooperative vehicular networks.

Simulation results show that the dynamic control of

beacon transmission rate limits the average position

error, and the use of maximum transmit power leads

to an increase of packet collisions. However, the

joint power & rate control allows reducing the

packet collisions. Although the approach in (Bolufé,

2018) outperforms other beaconing strategies in

terms of a trade-off between the main performance

metrics, we believe that the fine-tuning, by

evolutionary algorithms, of this joint power & rate

control will allow obtaining better results.

This paper presents our initial efforts toward

intelligent tuning of frequency and transmission

power adjustment in beacon-based VANETs. This

paper presents an approach to exploit the benefit of

genetic algorithms in the setting of beacon rate and

power transmission parameters. More specifically,

the contributions of this work are as follows:

• We propose the use of genetic algorithms (GA)

to search for beacon rate and power

transmission parameters that outperform other

approaches in terms of packet losses, packet

delivery, and number of collisions.

• To the best knowledge of the authors, this study

presents the first reference to the use of GAs in

the beaconing process.

This paper is organized as follows. Section 2

presents the state of the art. Section 3 presents the

baseline approach to adapt beacon rate and power

transmission parameters. Section 4 presents our

initial approach towards intelligent control of rate

and power transmissions. Section 5 presents

simulation results. Section 6 concludes the paper.

2 STATE OF THE ART

Adaptive beacon techniques have been the subject of

research since the last decade, all in all, assuming

simplified scenario conditions. The main goal of

these techniques is to adapt the beacon rate and

power effectively considering the channel load and

specific application requirements. To address this

problem, different adaptive beacon strategies have

been proposed (Shah, 2016; Zemouri, 2014;

Sepulcre, 2016; Aygun, 2016). These strategies

combine the control of beacon rate and transmission

power, according to the channel load and specific

application requirements. In (Zemouri, 2014) the

transmission rate is adapted to meet the channel

requirements in terms of collision rate and channel

busy ratio (CBR), while the transmit power is

adjusted according to the required awareness level.

In the algorithm of Sepulcre et al. (Sepulcre, 2016),

the packet rate of each vehicle is set according to the

minimum beacon rate required by each application

and it is set according to the required packet

reception rate at the application warning distance.

The algorithm, of Aygun et al. (Aygun, 2016),

adapts the transmit power in order to reach a desired

awareness ratio at the target distance while adjusting

Towards Intelligent Tuning of Frequency and Transmission Power Adjustment in Beacon-based Ad-Hoc Networks

649

the beacon rate to limit the current channel busy

ratio. However, all these approaches do not consider

the vehicle movement status and the vehicular traffic

dynamics, factors that affect the system

performance.

Optimization methods can be broadly classified

into two main classes: exact and approximate (Talbi,

2009). On one hand, exact methods ensure finding

the optimal solution to the optimization problem.

However, their complexity and high computational

demand are not suitable to tackle real-world

optimization problems. Alternatively, evolutionary

algorithms (EAs), which are population-based

metaheuristics, allow obtaining acceptable solutions

in a reasonable time (Dorronsoro, 2014). EAs have

been widely used in many scientific domains such as

ad-hoc networking (Reina, 2016). Depending on the

execution mode, the EAs can be deployed in

VANETs following an off-line or an on-line

approach. While off-line approaches help to search

for the best suitable parameters configuration, a

special care must be taken in highly dynamic

scenarios. On the other hand, online approaches are

expected to adapt their behavior (i.e., find the best

parameter solutions) during runtime.

In this regard, EAs have been employed in

optimization processes to finding optimum topology

in MANETs (Reina, 2016) as well as to optimize the

deployment of Road Side Units (RSUs) to maximize

the coverage (i.e., number of vehicles covered) in a

given area (O. Dengiz, 2011). More specifically, in

(Galaviz-Mosqueda, 2016), the authors proposed a

component-based methodology using GAs for the

membership functions tuning problem for

broadcasting protocols in VANETs. Other recent

examples of the use of GAs in VANETs are on

optimizing the topology connectivity (Dorronsoro,

2009), realistic vehicular mobility models

(Seredynski, 2012), optimize routing protocols

(Toutouh, 2012), and optimizing broadcasting (Jafer,

2016; Jafer, 2017).

Figure 1: P

as a function of L

and R

, according to (2).

From the aforementioned ideas, it is clear that

genetic algorithms have been applied for optimizing

different parameters in VANETs and MANETs.

However, to the best knowledge of the authors, this

study presents the first reference to the use of GAs

in the beaconing process.

3 DYNAMIC CONTROL OF

TRANSMISSION

PARAMETERS

This section aims to describe the Dynamic Control

of Beacon Transmission Rate and Power (DC-

BTR&P) algorithm developed in (Bolufé, 2018).

Due to its enhanced performance, the DC-TR&P

algorithm will be used as the baseline approach for

intelligent tuning of transmission rate and power

(described in Section 4).

The DC-BTR&P approach adjusts the beacon

transmission parameters to meet the position

accuracy requirements of cooperation aware

applications. DC-BTR&P is capable of adapting to

the vehicular traffic dynamics and to the vehicle

movement status reducing interferences

guaranteeing the vehicle's minimum warning range.

The DC-BTR&P algorithm uses the position error as

a priority metric due to its impact on vehicular

systems ability to detect and mitigate potentially

dangerous traffic situations in real-time. The beacon

rate is computed by the vehicle n

as a function of its

velocity (v

) and acceleration (a

) expressed as:

()

(

)

042

=−+++

iDibDiibi

EtvItavIa

(1)

, where I

is the beacon interval of n

(equivalent to

the inverse of beacon transmission rate R

); t

is the

transmission delay, which is equal for all vehicles by

assuming beacons of the same size and equal data

rate; and Ē

is the average position error computed

by surrounding vehicles. Once the beacon rate has

been set, DC-BTR&P adapts the beacon transmit

power according to the relative channel load and the

preset beacon rate, in order to decrease packet

collisions. The beacon transmit power is adjusted by

using the expression (2).

()

−













−−+=

TTTT

PPPP 1

minmaxmin

(2)

, where PT

min

is the transmit power required by n

generate a minimum warning range, P

Tmax

is the

maximum transmission power allowed, L

is the

RESIST 2018 - Special Session on Resilient Smart city Transportation

650

normalized critical channel load, L

is the normalized

relative channel load on n

and β is the weight factor,

which controls the impact of the beacon rate on the

transmission power. L

is computed considering the

impact of multiple transmitters, and the distance to

each neighbor vehicle. It should be noted that (2)

controls the beacon transmission power between

minimum and maximum transmit power values,

being P

= P

Tmin

for L

= L

, and P

= P

Tmax

for L

≈

0 and R

= 1 beacon/s. Figure 1 shows the transmit

power according to the normalized relative channel

load and beacon transmission rate, with P

Tmin

= 7

dBm, P

Tmax

= 20 dBm, L

= 0.4, and β = 2. Note that

the transmit power decreases when the normalized

relative channel load or/and the beacon transmission

rate increases, while a minimum warning range is

guaranteed.

4 INTELLIGENT CONTROL OF

BEACON TRANSMISSION

RATE AND POWER

This section firstly provides an overview of

fundamental concepts of GAs. Then, we elaborate

on the integration of GAs towards providing

intelligent tuning of frequency and power

parameters in a beacon-based ad-hoc networking

scenario.

4.1 Fundamentals of GAs

The genetic algorithm is based on the evolution

process of living beings. In which, over generations,

the populations evolve following the principles of

natural selection (the survival of the fittest)

postulated by Darwin. This algorithm was proposed

by Goldberg and Holland in 1998 (Goldberg, 1998).

The baseline procedure of a GA is depicted in

Fig. 2. The initial population is randomly created as

a set of candidate solutions, where an objective

function is used a fitness measure. Based on this

fitness, the better individuals (solutions) have a

higher probability to be selected to the next

generation by applying recombination and mutation.

The recombination process is applied to two selected

individuals (parents), resulting in two new solutions.

On the other hand, the mutation process is applied to

one individual and it results in one new solution.

Therefore, by applying recombination and mutation

processes, the algorithm produces a set of new

solutions, called children. Based on their fitness,

these children compete for a place in the next

generation. This procedure can be iterated until a

solution is found or a previously set of generations

limit is reached.

Initial

population

Evaluation

Selection

Crossover

Mutation

Stop

criterion

0 1

F(1,0,1,1)= 11

0 1

Chromosome

0 1

Gen

0 1

0 0

1 0

1 1

Parents

Children

0 1

1 1

Bit Inversion

Yes

Final solution

F(1,1,1,1)= 15

Individual 1

Individual 2

Individual 3

Individual 4

Figure 2: General scheme of a genetic algorithm.

4.2 GA-based Control of Beacon

Transmission Rate and Power

(GA-BTR&P)

This section presents our initial GA-based

Transmission Rate and Power (GA-BTR&P) control

approach. GA-BTR&P adjusts the transmission rate

and power at once, taking into account deterministic

rules based on the vehicle travel speed. For this

purpose, we performed a discretization of the travel

speed values in 10 possible ranges, which are shown

in Table 1. It should be mentioned that the V

max

value is the maximum possible travel speed of a

Table 1: Ranges of the travel speeds for deterministic

rules.

Speed Range (i) Vehicle speed (V

)

1 0 - 10 km/h

2 11 - 20 Km/h

3 21 - 30 Km/h

4 31 - 40 Km/h

5 41 - 50 Km/h

6 51 - 60 Km/h

7 61 - 70 Km/h

8 71 - 80 Km/h

9 81 – 90 Km/h

91 -

Km/h

Towards Intelligent Tuning of Frequency and Transmission Power Adjustment in Beacon-based Ad-Hoc Networks

651

vehicle (

) during a given simulation. The Fig. 3

shows the GA-BTR&P flow diagram to obtain the

values of frequency

and power transmission

of the vehicle k (veh

**V

is the speed of the vehicle k (veh

)

Figure 3: Deterministic & rule-based definition of

frequency and power transmission values.

The GA-BTR&P approach considers the range of

the travel speed to determine the values of frequency

i,k

and power transmission Y

i,k

, for the speed range

of vehicle k (veh

). It is important to mention that the

values of the variables X

i,k,

and Y

i,k

are determined

(i.e. optimized) with the genetic algorithm. For this

reason, we propose the encoding presented in Fig. 4

for the vehicles. Each vehicle is represented by a set

of 20 variables: 10 related to the frequency and 10

related to the power transmission. We consider one

variable of frequency and one variable of power

transmission for each range

of vehicle speed

presented in Table 1.

1,k

2,k

3,k

4,k

5,k

6,k

7,k

8,k

9,k

10,k

1,k

2,k

3,k

4,k

5,k

6,k

7,k

8,k

9,k

10,k

Transmission

frequencies.

Transmission

power.

Vehicle

Figure 4: Proposed vehicle encoding.

Each population individual is represented by a

set of k vehicles, where k is the total number of

vehicles in the scenario as it is graphically shown in

Figure 5. It should be mentioned that each vehicle

consists of a set of 20 variables (see vehicle

encoding in Fig. 4).

(Individual of the population)

Vehicle

… Vehicle

Figure 5: Proposed individual encoding.

In order to lay down the concepts of our GA-

based approach, we make use of a simulation

platform where the GA-based approach can iterate

and refine (i.e. optimize) the values for transmission

rate and power. For this purpose, we use the

OMNet++ simulation platform, although our

approach can be applied to other platforms. Figure 6

represents the optimization scheme for k vehicles

where each vehicle has 20 candidate variables, 10

for transmission power transmission and 10 for

beacon rate (see encoding in Fig. 4). The

performance of each population individual (i.e. the

set of k*20 variables) is evaluated through

OMNet++ simulations. The performance of each

population individual is evaluated in an urban

mobility scenario for which the optimization

procedure is executed. In each simulation (i.e.

evaluation), the number of packet collisions is used

to calculate the fitness value. A lower number of

collisions represents a greater fitness value of the

population individual. Each evaluation of the

population individual is followed by the selection,

recombination, and mutation processes of the

genetic algorithm until the stop criterion is met. At

the end of the evolutionary process, the best

parameters of frequency and transmission power for

each speed range of each vehicle are obtained for a

given urban mobility scenario.

Initial population

Evaluation

Selection

Crossover

Mutation

Stop

criterion

Individual

Parameters

OMNET simulator

Scenario

Metrics (number of collisions)

Best parameters

Vehicle

… Vehicle

Yes

Figure 6: GA-based simulation platform.

5 SIMULATION RESULTS AN

TECHNICAL DISCUSSION

This section compares the performance of the

baseline DC-BTR&P algorithm with our GA-based

RESIST 2018 - Special Session on Resilient Smart city Transportation

652

Transmission Rate and Power (GA-BTR&P) control

approach.

5.1 Simulation Setup

Our simulations are intended to evaluate the

performance of the above approaches to disseminate

cooperative knowledge. The simulations were

performed considering a scenario with a grid-shaped

square of five streets on each side. The square has

sixteen blocks of 200m per side. Each street is

crossed by a car with the direction shown in Fig. 7.

The maximum speed of the vehicles was set to

100km/h, with an acceleration and deceleration of 2

m/s

and 4.5 m/s

respectively. The simulation

parameters are summarized in Table 2.

Table 2: Simulation parameters.

Parameter Value

Map shape grid

Number of streets 5 x 5

Dimensions 0.8 km x 0.8 km

Maximum speed 100 km/h (27.8 m/s)

Acceleration 2 m/s

Number of vehicles 10

Vehicle travel time ≈ 80 sec

Figure 7: Simulation map with directions of vehicles.

5.2 Comparative Results

This section compares the performance of the DC-

BTR&P and GA-BTR&P approaches. Namely, we

evaluate their performance in terms of number of

collisions and packet delivery, which in turn are

representative parameters that allow for efficient

cooperative knowledge. Table 3 shows the

performance of the transmission rate and power

parameters defined by both, the DC-BTR&P and

GA-BTR&P approaches.

Table 3: Performance comparison for cooperative

knowledge.

Metric DC-BTR&P GA-BTR&P

Packets Sent

4051 4052

Lost packets

49 18

Delivery effectiveness

98.79% 99.55%

Number of collisions

48 13

In more detail, Table 4 shows the actual values

of the vehicles in our simulations for each metric.

The results demonstrate that our GA-based approach

can reduce the number of collisions up to a 73% and

the packet losses can be reduced up to a 63% in this

urban scenario. These results demonstrate that the

performance of a dynamic allocation of beacon

transmission rate and power certainly can be

enhanced with the use of an intelligent approach, i.e.

based on a genetic algorithm. Nevertheless, there are

important aspects that need to be considered

regarding these partial conclusions. These are

discussed in the next section.

Table 4: Values produced for each vehicle in simulations.

Metric

Vehicle

Packets

Sent

Packets lost Number of

collisions

BTR&P

Veh[0]

396 396 3 2 3 1

Veh [1]

397 397 1 0 1 0

Veh [2]

396 396 3 0 3 0

Veh [3]

432 432 5 2 5 2

Veh [4]

395 395 13 3 12 2

Veh [5]

398 398 1 1 1 0

Veh [6]

398 398 1 1 1 0

Veh [7]

420 421 15 6 15 5

Veh [8]

397 397 2 0 2 0

Veh [9]

422 422 5 3 5 3

TOTAL

4051 4052 49 18 48 13

5.3 Technical Discussion

The results presented in Section 5.2 are encouraging,

they demonstrate that there is still a reasonable

margin to enhance the performance of the DC-

BTR&P by means of an intelligent approach.

Moreover, there are important aspects that deserve

special attention in this respect.

Towards Intelligent Tuning of Frequency and Transmission Power Adjustment in Beacon-based Ad-Hoc Networks

653

On the one hand, the DC-BTR&P approach is a

distributed beaconing algorithm that performs

transmission frequency (Ftx) and transmission

power (Ptx) adjustments in each vehicle. In the

implementation of this algorithm, each vehicle

calculates the Ftx according to its speed and the

average position error limit perception of its

neighbors. Then the transmitter vehicle calculates

the probability of successful reception of its

neighbors and with the Ftx already calculated, the

Ptx of the same vehicle is calculated. Before sending

a beacon, each vehicle makes these calculations to

minimize the average position error (dynamically

adjusting the Ftx) and to reduce the number of

collisions (by adjusting the Ptx).

On the other hand, the GA-BTR&P approach

uses a genetic algorithm to find in each vehicle, the

best values of Ftx and Ptx to achieve a reduction in

the number of collisions in the scenario for the speed

ranges of each vehicle. Nevertheless, this approach

has to execute a number of iterations, which in turn

makes it computationally expensive so that, it

difficult to be applied to practical scenarios. A trade-

off solution that can exploit the benefits of our GA-

BTR&P approach in favor of a fast and more

efficient version of the DC-BTR&P approach will be

the basis of our future work.

6 CONCLUSIONS AND FUTURE

WORK

This paper has presented our initial steps towards

intelligent tuning of frequency and transmission

power adjustment in beacon-based ad-hoc networks.

A genetic-based beacon control has been proposed

and simulation results have demonstrated that an

intelligent-based approach can outperform a

dynamic control in terms of a number of collisions

(with reductions up to 73%) and packet losses (with

reductions up to 63%) in our urban simulation

scenario. Nevertheless, this advantage can be

considered relative, as an intelligent-based approach

like the one described in this paper can be

computationally prohibitive for real scenarios due to

time constraints. In this regard, we are currently

developing a trade-off solution, where the genetic-

based approach can be used to produce values of

frequency and transmission power that can take into

account a target average error together with the

number of collisions. This way, the genetic

algorithm can be used to produce a regression-based

polynomial function that could be used to estimate

the beacon frequency for a given vehicle speed,

which in turn could be used by the DC-BTR&P

approach to produce frequency values to enhance its

performance closer to the performance of the GA-

BTR&P approach but in runtime.

ACKNOWLEDGEMENTS

This paper has been supported by the National

Council of Research and Technology (CONACYT)

through grant FONCICYT/272278 and the

ERANetLAC (Network of the European Union,

Latin America, and the Caribbean Countries) project

ELAC2015/T100761. This paper is also partially

supported also by project TEC2015-71329-C2-2-R

(MINECO/FEDER).

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APPENDIX I. GLOSSARY OF

ACRONYMS

Channel Busy Ratio (CBR)

Dedicated Short-Range Communication (DSRC)

Dynamic Control of Beacon Transmission Rate and

Power (DC-BTR&P)

European Telecommunications Standards Institute

(ETSI)

Evolutionary Algorithm (EA)

Genetic Algorithm (GA)

Genetic Algorithm-based Transmission Rate and

Power (GA-BTR&P)

Global Positioning System (GPS)

Local Dynamic Map (LDM)

Mobile Ad-hoc Networks (MANETs)

On-Board Units (OBUs)

Physical Layer (PHY)

Road Side Units (RSUs)

Transmission Frequency (Ftx)

Transmission Power (Ptx)

Vehicle-to-Infrastructure (V2I)

Vehicle-to-Vehicle (V2V)

Vehicular Ad-hoc Networks (VANETS)

Wireless Access in Vehicular Environments

(WAVE)

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