Implementation of the Algorithms to Retrieve the Data About Local

and Global Wireless Networks from the Telematics Map

Serge Popov, Maxim Sharagin, Vadim Glazunov and Mikhail Chuvatov

Peter the Great St. Petersburg Polytechnic University, Saint-Petersburg, Russia

Keywords:

Connected Vehicle, Wireless Networks, Wi-Fi, Multiprotocol Unit, Spatial SQL Queries, Telematics Map.

Abstract:

This paper describes implementation and research of the algorithms to select the data about surrounding wire-

less networks from the moving vehicle. The data are retrieved from the telematics map, which is a cloud

service containing the data about all the available wireless networks in the region. The paper contains the

description of three scenarios of data extraction, relational queries to the telematics map which serve these

scenarios, and the experiment to test the data extraction from the cloud service under real road environment.

The experiment has shown that the time needed to extract the data about available local and global wireless

networks does not exceed 0.2 sec, which is acceptable for the tasks of scheduling the wireless connections

between the vehicle and the cloud services during the whole route. The results of this work may be used to

retrieve the list of available wireless networks in the algorithms of intelligent scheduling of bidirectional data

transmission for the connected vehicles.

1 INTRODUCTION

Modern intelligent transportation system (ITS) con-

sider each vehicle as an active source and destina-

tion for data ﬂows containing trafﬁc behaviour infor-

mation, which is important in prevention of road ac-

cidents, reduction of emissions, decreasing the time

spent by the driver and passengers in trafﬁc jams. To

achieve these goals each vehicle keeps bidirectional

connection between the vehicle and cloud services

located outside of the car (Zaborovskiy et al., 2013;

Annese et al., 2011; Kutscher and Ott, 2006), it al-

lows to continuously obtain data about surrounding

road conditions, local trafﬁc ﬂuctuations, unwanted

weather changes. It is not an easy task to provide the

consistent connection because of large number and

dynamic behaviour of the vehicles on the city roads,

short range of local wireless technologies, presence of

the areas without cellular coverage, variability of the

network trafﬁc among the vehicles. The fundamental

approach to this problem is to continuously retrieve

the data about possible connection points not only

near the current vehicle’s location, but at any point of

the selected area. Selection of the current geographic

region networks provides a combination of data man-

agement technologies of the telematics map and the

multiprotocol unit (Glazunov et al., 2015). The pro-

posed idea allows to implement the technologies of

gathering and aggregation of data about available

wireless networks of the region (Mikhail et al., 2015).

The ﬁrst part of our paper contains the review of the

related works. In the second part we describe three

scenarios for the method of selection of the available

wireless networks from the telematics map and the

algorithm of communication between the vehicle and

the cloud service. The third part covers the imple-

mentation of spatial queries to the relational storage

of the telematics map. The fourth part includes the

description of the testbed, description of the experi-

ment, and the result of trials on data retrieval from

the moving vehicle. The proposed approach allows to

select the potentially available wireless networks for

arbitrary number of vehicles, thus providing ﬂexibil-

ity to schedule the data transmission to or from the

connected vehicle.

2 RELATED WORKS AND THE

GOAL OF THE RESEARCH

At the initial stage in the development of vehicle net-

works the problem of efﬁcient selection of the access

points was solved by specifying positions of these

points and sequentially switching among them using

static algorithm (Deshpande et al., 2009). The prob-

Popov, S., Sharagin, M., Glazunov, V. and Chuvatov, M.

Implementation of the Algorithms to Retrieve the Data About Local and Global Wireless Networks from the Telematics Map.

DOI: 10.5220/0006470504470452

In Proceedings of the 14th International Conference on Informatics in Control, Automation and Robotics (ICINCO 2017) - Volume 2, pages 447-452

ISBN: Not Available

447

lem of continuous uninterrupted access to the network

was solved by using multiple Wi-Fi interfaces and

connecting them to different access points simultane-

ously (Annese et al., 2011). Active development of

the ”connected vehicle” technology required to de-

ploy the large amount of road infrastructure equip-

ment, and the continuous connection with these de-

vices must be maintained. These connections are

the important part in tasks of fog (Jain and Singhal,

2016) and cloud (Ko et al., 2016) computing, thus re-

quiring self-organizing sensor, ad-hoc, or mesh net-

works. The other option is to deploy the additional

road infrastructure equipment which communicates

with the vehicles using the WAVE protocol and ad-

hoc technologies (Salahuddin et al., 2015), it is also

possible to use smartphones of drivers and passengers

for continuous access to cloud services (Wahlstr

et al., 2016). But in this case the problem of efﬁ-

cient communication between the vehicle networks

and road infrastructure still exists. To address that

and ﬁnd the suitable connection there are local static

and dynamic algorithms of selection of the best avail-

able network. These algorithms combine the wireless

networks scan mode and connection mode to work

within the selected network (Mouton et al., 2013).

Statistical methods of selection of the best available

network allow to analyze the connection time for each

network and to choose the one which provides the

longest active connection (Mostafa et al., 2011). To

achieve even better results there are fuzzy logic meth-

ods to select the best networks based on the driving

parameters (Ndashimye et al., 2016), or by distribut-

ing the vehicles among the networks based on the

QoS model (Xi et al., 2014). We propose the ap-

proach that allows to retrieve the data about available

wireless networks in case when the number of access

points, their status, and number of vehicles constantly

changes. The goal of this research is to compute and

analyze the execution time of the queries that retrieve

data about the telematics resources of the region. We

consider three different scenarios while acquiring the

data to predict the telematics environment on board of

the vehicle.

3 IMPLEMENTATION OF

SCENARIOS TO RETRIEVE

THE LIST OF NETWORKS

FROM THE MOVING VEHICLE

The data upload and download may occur when the

vehicle is moving on the route. When these data ﬂows

appear we should select the best available point of

connection to the wireless network and perform the

transmission. To select the speciﬁc point we take into

account the set of high-level parameters, such as: link

bandwidth, time when the vehicle is in the coverage

of the network, trafﬁc cost, connection establishment

time. However, we can redistribute the incoming and

outgoing trafﬁc if we know the data size, the traf-

ﬁc ﬂows priorities, the time when these data should

be ready for use, and the vehicle’s route. Then the

data management scenarios could be local, when we

request the data in the area close to the vehicle, or

global, when we work with the data relevant for the

whole route. Based on this idea we can deﬁne three

basic scenarios of retrieving the data by the moving

vehicle from the telematics map when driving in city

or highway cycles: local, which is based on the vehi-

cle’s current position, and global, which is based on

the vehicle’s proposed route:

1. When the vehicle is on the parking or in the

garage, or when the vehicle is moving at a low

speed, or if the network suitable for scheduled

data transmission is present at some place on the

route - acquire the list of networks inside the rect-

angular area close to the vehicle and sort this list

by signal level or by the coverage in the selected

area.

2. When we schedule the continuous connection of

the vehicle to the wireless networks throughout

the route - acquire the list of networks which cover

the whole route of the vehicle, sequentially switch

among them to be continuously connected to the

cloud services.

The picture shows a graphic representation of

scenarios1.

To implement these scenarios we set up the telem-

atics map cloud service and propose the method to

extract the data in form of relational queries to the

database. The database schema is described in work

(Glazunov et al., 2015).

a) Scenario 1: obtaining data on the telematics en-

vironment around the vehicle. The initial data

for determining the telematic situation is the cur-

rent position of the vehicle, getting from the GPS

global positioning system in the WGS84 system,

and the size of the area for requesting data about

wireless networks. The size of the area is deter-

mined by factors such as the driving direction of

the vehicle and the sensitivity of the wireless re-

ceiver. The calculation of the area of interest for

determining the wireless networks is carried out

so that the data on the situation are available the

nearest 20s or 200m around the vehicle. The re-

quest returns the SSID of the wireless network and

ICINCO 2017 - 14th International Conference on Informatics in Control, Automation and Robotics

448

Figure 1: Graphic representation of scenarios to extract the

data about the wireless networks of the region.

the percentage of the area of the area of interest

on which it will be available, taking into account

the sensitivity of the receiver in the vehicle. The

vehicle on-board software can select the best net-

work from the list and connect to it to receive or

transmit data.

b) Scenario 2: obtaining data on the wireless net-

works situation on the planned route. The ini-

tial data for the execution of the scenario is the

planned route of the vehicle’s movement that to

be received from the car’s navigation system or

the user’s smartphone. The planned route is deter-

mined by a set of points in the WGS84 coordinate

system and the times at which the vehicle is sup-

posed to be in this position. The conﬁguration of

the area of interest is determined by factors such

as the planned route of the vehicle and the sen-

sitivity of the wireless receiver. The calculation

of the area of interest for determining the wire-

less networks is carried out so that the data on the

situation are available the nearest 20s and 200m

around the vehicle. The request returns the List of

the wireless networks, the percentage of the area

of the area of interest on which it will be available

and the position on the planned route, from where

this network is available. The wireless network

availability point is calculated taking into account

the sensitivity of the vehicle receiver.

The presented algorithms are implemented as

client-server application on the testbed with mul-

tiprotocol nodes (Popov et al., 2014). A multipro-

tocol server includes: WiFi, LTE transceivers and

the GPS positioning receiver. Also navigation

Algorithm 1: The procedure for requesting data

about wireless networks around the vehicle.

Data:

- Wi-Fi receiver sensitivity — level;

- Length the area of interests L

area

;

- Latitude, longitude of vehicle — lat

, lon

, t

;

- Data new acquisition area (lat

, lon

;lat

, lon

Result: Net{SSID, S} list of networks

Map{SSID, level},

1 , sorted by area of coverage.

2 while (new request of wireless network list) do

3 begin

4 Get data from GPS receiver

5 (lat

, lon

) ← GetGPSPosition()

6 Calculate the area of interest arround

vehicle

7 (lat

, lon

;lat

, lon

) ←

GetArea(L

area

, level)

8 Create the SQL Query

9 SELECT SSID, S

10 FROM map NATURAL JOIN network

11 NATURAL JOIN network value

12 WHERE network level ¡ level and

13 latitude ¿ lat1 AND latitude ¡ lat2 AND

14 longitude ¿lon1 AND longitude ¡ lon2

15 Run the SQL query

16 Query result ← Query

17 Save a network list

18 Net{SSID, S} ← Query result

19 end

20 end

system with a route-laying function are connected

with multiprotocol server in the vehicle. The data

from the hardware, sent by HTTP protocol to the

cloud service of the telematics map over available

wireless networks. The requests received in the

cloud service and its results are send back to the

vehicle. Schema of data streams under the dis-

play of 2. Data streams are generated inside the

vehicle from the GPSd server includes NMEA co-

ordinates. The navigation system (Yandex Maps)

generates a trafﬁc route and sends merged packet

with data to the cloud service via a HTTP POST

request. On the server, the received data is pro-

cessed by the WEB-application and accumulated

in the MySQL database. The server returns a list

of the networks sorted by the coverage area of the

networks relative to the route of the vehicle.

Implementation of the Algorithms to Retrieve the Data About Local and Global Wireless Networks from the Telematics Map

449

Algorithm 2: The procedure for requesting data

about wireless networks on the vehicle route.

Data:

- Wi-Fi receiver sensitivity — level;

- Latitude, longitude and time of vehicle — lat

, lon

, t

;

- Planned vehicle route — R(lat

area

, lon

area

Result: List of networks Map{SSID, S, {lat, lon}},

sorted by area of coverage and the point

where this network can become available

1 . while (new request of wireless network list) do

2 begin

3 Get data from GPS receiver

4 (lat

, lon

) ← GetGPSPosition()

5 Get data from navigation system

6 R(lat

, lon

) ← GetRoute()

7 while (for each point from R) do

8 Calculate the area of interest arround

point

9 (lat

, lon

;lat

, lon

) ←

GetArea(R(lat

area

, lon

area

, level)

10 Extend the query to a new area

11 Area(lat, lon) ← (lat

, lon

;lat

, lon

)

12 end

13 Create the SQL Query

14 SELECT SSID, S, lat, lon

15 FROM map NATURAL JOIN network

16 NATURAL JOIN network value

17 WHERE network level ¡ level and

18 (latitude AND latitude) in Area(lat, lon)

19 Run the SQL Query

20 Query result ← Query

21 Save a network list

22 Map{SSID, S, {lat, lon}} ← Query result

23 end

24 end

4 REALIZATION AND TESTING

THE ALGORITHMS TO

RETRIEVE DATA FROM THE

TELEMATICS MAP

The goal of the experiment is to obtain data on the

average time execution of two scenarios for obtaining

data about wireless networks from the vehicle in the

real trafﬁc situation. The trajectory of vehicle driving

during the experiments is shown in the ﬁgure 3. Blue

indicates the part of actual trajectory, green - planned

for the request of the situation.

To implement the ﬁrst scenario, the vehicle moved

along the path and at a frequency of 1Hz and re-

quested data on the wireless networks from the telem-

atics map. The time of the beginning of the transfer of

the query and the end of data reception is registered

and accepted as full time of the query execution.

To implement the second scenario, the vehicle

Figure 2: Data ﬂows transmitted between the vehicle and

the cloud service.

Figure 3: Trajectory of vehicle movement in the process of

receiving data from the telematics card.

moves along a pre-selected path and requests data on

the telematics situation for the next 30 seconds of the

planned route. The request was repeated at a fre-

quency of 0.2Hz. The query execution time is reg-

istered in the same way as in scenario one.

In the table 1 the parameters and constants of the

ICINCO 2017 - 14th International Conference on Informatics in Control, Automation and Robotics

450

experiment on the execution of query’s to the telem-

atics card are presented.

Table 1: Parameters and constants of the experiment.

Parameters, constants Values

Data transmission technologies 802.11n/b/g, LTE

System software Apache, PHP,

Mysql

Operstion system Debian Linux

Scenario 1

Query frequency, Hz 1

Number of query, pcs 5000

Scenario 2

Query frequency, Hz 0.2

Number of query, pcs 4900

Length route for query, point 30-60

Data allows analyzing the distribution of the exe-

cution time requests to the telematics map cloud ser-

vice from the vehicle. Such a query frequency pro-

vides the vehicle with real-time data. The ﬁgure 4

shows the distribution of the query execution time

when data is fetched around the vehicle. It can be

seen from the distribution that a larger number of re-

quests are executed in a time less than 0.2s, which al-

lows receiving data with frequency up to 1Hz, which

is comparable to real-time data acquisition.

Figure 4: The distribution of time execution of queries

about telematics resources around vehicle.

The ﬁgure 5 shows the distribution of the query

execution time for the planned vehicle route. The dis-

tribution shows the predicted trafﬁc route of at least

30 seconds, the query is no more than 0.3s.

Figure 5: The distribution of time execution of queries

about telematics resources on the vehicle route.

5 CONCLUSION AND FUTURE

WORK

The article describes the technology of interaction of

a vehicle with a telematics card for obtaining data

about wireless networks around the car and on the

whole planned route of the vehicle. The realization of

the prototype of cloud service and vehicle on-board

software demonstrated the efﬁciency of the technol-

ogy. As a result of the conducted experiments it was

shown that the query execution time averages 0.2 s

for the local scenario and 0.3s for the scenario with

the intended path. This allows you to update data on

available networks on a vehicle with a frequency of

up to 1Hz or to increase the length of the intended

path. Results can be used for choosing and direct con-

nection of a vehicle in the current point or for use as

networks list for intellectual algorithms of data trans-

mission from the vehicle. Further of this work may

be increase in the number of connected vehicles in the

real trafﬁc situation, the optimization of the frequency

of the execution of query and lengths of the planned

route, the integration with the subsystem of planning

the telematics trafﬁc on the board. Alternative way of

future research is the use the modeling system such as

SUMO or NS-3 for study the high intensivity network

trafﬁc with the real and virtual vehicles.

ACKNOWLEDGMENTS

The research is supported by the government contract

of Russian Federation #2.9198.2017/8.9.

Implementation of the Algorithms to Retrieve the Data About Local and Global Wireless Networks from the Telematics Map

451

REFERENCES

Annese, S., Casetti, C., Chiasserini, C. F., Maio, N. D.,

Ghittino, A., and Reineri, M. (2011). Seamless con-

nectivity and routing in vehicular networks with in-

frastructure. IEEE Journal on Selected Areas in Com-

munications, 29(3):501–514.

Deshpande, P., Kashyap, A., Sung, C., and Das, S. R.

(2009). Predictive methods for improved vehicular

wiﬁ access. In Proceedings of the 7th International

Conference on Mobile Systems, Applications, and Ser-

vices, MobiSys ’09, pages 263–276, New York, NY,

USA. ACM.

Glazunov, V., Kurochkin, L., Popov, S., and Chuvatov,

M. (2015). Prototype of the telematics map cloud

service. In Open Innovations Association (FRUCT),

2015 17TH Conference of, pages 50–55.

Jain, A. and Singhal, P. (2016). Fog computing: Driving

force behind the emergence of edge computing. In

2016 International Conference System Modeling Ad-

vancement in Research Trends (SMART), pages 294–

297.

Ko, B., Liu, K., and Son, S. H. (2016). Towards efﬁcient

data services in vehicular networks via coopera-

tive infrastructure-to-vehicle and vehicle-to-vehicle

communications. In 2016 Intl IEEE Conferences

on Ubiquitous Intelligence Computing, Advanced

and Trusted Computing, Scalable Computing and

Communications, Cloud and Big Data Computing,

Internet of People, and Smart World Congress

(UIC/ATC/ScalCom/CBDCom/IoP/SmartWorld),

pages 82–89.

Kutscher, D. and Ott, J. (2006). Service maps for hetero-

geneous network environments. In 7th International

Conference on Mobile Data Management (MDM’06),

pages 27–27.

Mikhail, C., Vadim, G., Michail, K., and Serge, P. (2015).

Methods of interaction between multiprotocol unit

and telematics map cloud service. In Proceedings of

the 1st International Conference on Vehicle Technol-

ogy and Intelligent Transport Systems - Volume 1: VE-

HITS,, pages 161–166.

Mostafa, A., Vegni, A. M., Singoria, R., Oliveira, T., Little,

T. D. C., and Agrawal, D. P. (2011). A v2x-based ap-

proach for reduction of delay propagation in vehicular

ad-hoc networks. In 2011 11th International Confer-

ence on ITS Telecommunications, pages 756–761.

Mouton, M., Castignani, G., Frank, R., Codeca, L., and

Engel, T. (2013). On the evaluation of make-before-

break handovers in urban wiﬁ networks for moving

vehicles. In 2013 10th Annual Conference on Wireless

On-demand Network Systems and Services (WONS),

pages 170–177.

Ndashimye, E., Sarkar, N. I., and Ray, S. K. (2016). A

novel network selection mechanism for vehicle-to-

infrastructure communication. In 2016 IEEE 14th Intl

Conf on Dependable, Autonomic and Secure Com-

puting, 14th Intl Conf on Pervasive Intelligence and

Computing, 2nd Intl Conf on Big Data Intelligence

and Computing and Cyber Science and Technology

Congress(DASC/PiCom/DataCom/CyberSciTech),

pages 483–488.

Popov, S., Kurochkin, M., Kurochkin, L. M., and Glazunov,

V. (2014). Hardware and Software Equipment for

Modeling of Telematics Components in Intelligent

Transportation Systems, pages 598–608. Springer In-

ternational Publishing, Cham.

Salahuddin, M. A., Al-Fuqaha, A., and Guizani, M. (2015).

Software-deﬁned networking for rsu clouds in support

of the internet of vehicles. IEEE Internet of Things

Journal, 2(2):133–144.

Wahlstr

om, J., Skog, I., and H

andel, P. (2016). Smartphone-

based vehicle telematics - A ten-year anniversary.

CoRR, abs/1611.03618.

Xi, L., Heli, Z., and Quansheng, X. (2014). Optimal ac-

cess scheme for mobile vehicular small cells in lay-

ered heterogenous networks. In 2014 4th IEEE In-

ternational Conference on Network Infrastructure and

Digital Content, pages 46–50.

Zaborovskiy, V., Lukashin, A., Popov, S., and Vostrov, A.

(2013). Adage mobile services for its infrastructure.

In ITS Telecommunications (ITST), 2013 13th Inter-

national Conference on, pages 127–132.

ICINCO 2017 - 14th International Conference on Informatics in Control, Automation and Robotics

452