=Paper=
{{Paper
|id=Vol-2144/paper5
|storemode=property
|title=A study to Integrate VANET and ArcGIS for Civil Defense Services in Urban areas
|pdfUrl=https://ceur-ws.org/Vol-2144/paper5.pdf
|volume=Vol-2144
|authors=Hanaa Basheer,Zaid Makki
}}
==A study to Integrate VANET and ArcGIS for Civil Defense Services in Urban areas==
https://ceur-ws.org/Vol-2144/paper5.pdf
A study to Integrate VANET and ArcGIS for Civil Defense
Services in Urban areas
Hanaa S. Basheer Zaid F. Makki
DSST DSST
Lebanese University Lebanese University
Lebanon, Beirut Lebanon, Beirut
Hana@ilps.uobaghdad.edu.iq zaid.makki@st.ul.edu.lb
Kifah Tout Carole Bassil
Faculty of Science I Faculty of Science I
Lebanese University Lebanese University
Lebanon, Beirut Lebanon, Beirut
ktout@ul.edu.lb cbassil@ul.edu.lb
ABSTRACT globally about 1.3 million people die in road crashes each year
When a car accident happens either on urban roads or and an additional of 20-30 million injured. The solution for
highways the main consideration is how to provide first aids to the minimizing road crash risks is mainly depend on providing
sufferer citizens as quickly as possible. Health organizations travelers with information.
declared that the quick the help reaches the injured persons at the In 2001 a project was presented and funded by the Greek
accident location, the more the lives saved. The main problem Secretariat of Research and Technology to coordinate and lead
facing civil defense vehicles is the blocking roads due to the ambulance vehicles to appropriate hospitals. This study depends
increasing in cars density in the neighboring area, which may on GIS, GPS, and global system for mobile communication GSM
cause delays in first aid services. Vehicular ad hoc network technologies [1]. Although it is possible to use cellular networks,
(VANET) systems are proposed to support transportation with but these networks may suffer from messages congestion that
real time safety information by using vehicles communication causes service delay. In 2005 a suggested work of five phases was
with each other or by infrastructure connecting to take the presented for accident diagnosis based on GIS technology and
privilege of the cloud network traffic services. Moreover, Road Accident View system RAV. The work has described a
geographic information system GIS is a business information framework for prototype in establishment a GIS-RAV System for
management system that helps in capturing, analyzing, and traffic accident application [2]. In our knowledge the system did
presenting special geographic information. Using GIS can be not deploy in a real-life environment. In 2013, distinct GIS has
useful in making better decisions for an everyday life living. In been designed to present all types of geographic data, to acquire
this study, we present the first steps of a new method that can flow intensities of roads in a city for map services. Maps on the
combine the real time information coming from vehicles with the internet have application programming interface (API) that
abilities of GIS in geographic analyzing to decide the shortest and support the GIS applications. Figure 1 shows the work‟s idea of
the spare path to the accident location that civil defense vehicles traffic flow acquisition processing. The method procedure starts
can use to reach and help in saving lives. The contribution of this by map service API, and then a bash script was designed for
study is the use of the SignalR library with GIS services to give a traffic flow image collecting. Moreover the authors used image
safety real time support. Our study goal is to enhance the processing software to extract road intensity and saved it to a
responding of civil defense vehicles to the emergency calls by specific date and time into the traffic flow acquisition [3]. The
leading the ambulance and fire cars from where they are to the work is not designed for real time processing.
accident location then to the nearest health center quickly through
the shortest and sparse paths. In our study we concentrate on developing a method that can
provide the civil defense vehicles such as ambulance and fire
vehicles with real roads information to help them reach their
Keywords destination through shortest and spare part, so to give their
VANET, ArcGIS, SignalR library helping services to reduce accident effects.
1. INTRODUCTION 2. VEHICULAR NETWORKS and CLOUD
Recently with the wide expansion of the transportation PROTOCOLS
system, a high number of vehicles reside on the roads almost all VANETs are self-organized networks that support intelligent
the time. An abnormal situation may suddenly occur and cause an transportation with information through wireless communications
increasing in the road density because of the irritable in vehicle among vehicles on the road. The protocol wireless access
movement. Thus, civil defense vehicles will face difficulty to vehicular environment (WAVE) has introduced to the
reach the accident location in a suitable time to give help and try transportation community to enable vehicular communication
rescues human lives. This is a huge problem which engorges the using detected short-range communication (DSRC) frequencies.
vehicle industries to participate in the research field to solve road WAVE allows safety-related and non-safety related vehicular
density problem to give humanity better services. The main issue applications over single-radio using multi-channel operations
here is saving human lives, where, according to the statistic of the defined in the IEEE 1609.4 protocol [4]. The goal for creating
association for safe international road travel (ASIRT) web site,
such network is to improve traffic safety by supplementing
different services to the drivers. To establish vehicular
communications, vehicles' are equipped with sensors, antennas,
and on-board unit device OBU. Moreover, road side units RSUs
are placed on fixed places on the roads to help vehicles to
communicate with the infrastructure [5]. On August 2010 Prof.
Olariu and his co-workers had promoted the vision of vehicular
clouds (VCs), where the cloud computing (CC) technique used to
help with vehicle service applications. The National Institute of
Standards and Technology (NIST), gives a formal definition of
CC as: “a model for enabling convenient, on demand network
access to a shared pool of configurable computing resources (e.g.,
Networks, servers, storage, applications, and services) that can be Fig. 3: Schematic diagram of cloud architecture which
rapidly provisioned and released with minimal management effort included VANET as IaaS [12]
or service provider interaction” [6]. While The Vehicular Cloud
Computing VCC, (or might be called autonomous vehicle cloud
AVC) defined by Olariu S. As “A group of largely autonomous
vehicles whose corporate computing, sensing, communication and
3. Esri ArcGIS SERVER
By using public published maps and Geo-processing services
physical resources can be coordinated and dynamically allocated
created by the GIS team, a user application can be built to help in
to authorized users.” [7]. The authors suggested to start creating a
supporting multiple services that must react in real time. One of
new scenario using AVC facilities to help in enhancing the
the best servers, used is the Esri ArcGIS Server supported by API,
vehicle environment. Hussain et al. In 2012 proposed three kinds
which is used together to create and manage GIS Web services,
of architectural framework for VANET-based clouds, Vehicular
applications, and data [13]. GIS with the new SignalR library is
Clouds (VC), Vehicles using Clouds (VuC), and Hybrid Clouds
used to develop high-frequency messaging and real-time web
(HC) [8]. Services offered by VuC include the CAA (Cooperative
functionality easy. The signalR library allows bi-directional
Awareness Applications), real-time traffic information, warning
communication between server and client and its main goal is to
messages, and infotainment. From the VANET application
deliver a real-time experience over HTTP. This library also lets
standpoint, CAA is of prime importance. Keeping in mind the size
you broadcast messages to all connected clients simultaneously or
and frequency of data generated by CAA, authors of reference [9]
to specific clients [14].
indicates that VuC would be the ideal framework for providing
services to VANET as illustrated in figure 2. 4. THE MODEL STRUCTURE
Our goal is to support civil defense vehicles with important
information about the road situation by following the decision that
is made after analyzing the incoming data from cooperative V2V
networks. Throughout the coming subsections we are going to
review our assumptions and scheme algorithm briefly.
4.1 Assumptions and Problem Statements
We based our study on a suggested model with four
fundamentals:
- Platform: The platform is the urban roads, where every vehicle
can be connected at least one time to one of the available RSU
Fig. 2: VANET using Clouds (VuC) Framework [9] during its journey; this connection helps to exchange and update
the traffic database information in the VuC frequently. This is
In [10, 11] three level of cloud computing services was, done by feeding both; the vehicle and the database with the latest
defined as; Infrastructure as a Service (IaaS) that provide users road situations. We assumed that the digital road map is divided
with storage, processors and network resources, Platform as a into segments with fixed sized depending on the GPS information,
Service (PaaS) provides users with development tools, and where each segment assigns with a unique (SID).
Software as a Service (SaaS) provides customers with application - Dissemination mechanism: Our concentration is about
services. In [12] a proposed algorithm is presented where IaaS can propagating the warning message between vehicles (V2V). Then
get up to date information from the wireless sensor network and the message will transfer to VuC database, and GIS center. This
electronic equipment connected to the cars and collect the in-car mechanism can provide helpful information to the civil defense
information, traffic and road information. This information is then vehicles considering the short and the spare path to the accident
passed to SaaS through PaaS. This kind of system will location. Many methods are presented for safety messages
communicate with the users within the cloud, process all dissemination to alert all the neighboring vehicles [3]. A
information and give useful services, as figured in figure 3. This dissemination method for safety messages (DMSM) is addressed
method can be useful to propagate an emergency message to the in a previous work by two of the authors of this study, which we
GIS system to create the final report that helps the civil defense suggested to be adopted [15].
vehicles with the needed information about road situation. - Beacon and warning message structure: Vehicles which are
connected to each other (V2V) continue exchanging packets. A
beacon is sent in a particular interval of time, and carries the
vehicle's status, (e.g. Vehicle identity, location, time, direction, For more details we are going to review our study algorithm
velocity, neighboring vehicles aggregates to it, and road segment through three main phases; phase 1 represents the mechanism of
identity). There is no problem with storage space on vehicles data collection in normal situation and repeated at every time
OBU, so we based our idea on creating a table stored in each interval, phase 2 presents node action when it senses an abnormal
vehicle contains all the neighboring node status. Figure 4, shows situation where the nearest node (source node) starts creating the
our suggested packet's structure created from a pair of two values; alert message and send it to VuC through RSU, while phase 3
the beacon and the warning message, which will remain empty start when the alert message reaches the GIS center to create the
until sensing an abnormal situation ahead. final report to be sent to the civil defense center, so their vehicles
- Warning message data: Messages can be classified into many start their rescue journey using the most suitable path. The phases
kinds based on their information [16]. We suggest adding a filed were named as follows:
in the message structure that states the priority value to each 1- Data collection phase
message kind depending on how important to be first processed.
Fig. 4 illustrates the five fields of the warning message structure; 2- Creating and propagating alert message phase
the tag that indicates weather the node is a source node of the 3- ArcGIS server decision phase
warning message, the time the message created, the forwarder
Phase 1 and 2 subsections show the V2V ability of dissemination
node identity that the source node waits for its acknowledgement
the warning message, while phases 3 subsections show the
of receiving, message codes, and the priority value to help in
contribution of our work in improving the rescue procedure of the
filtering the incoming messages as shown in figure 5.
ambulance and fire vehicles by using the GIS technology.
Phase1: Data collection
With the help of OBU device, sensors, and GPS device any
Fig. 4: Beacon information fields paired with M, where intelligent vehicle can exchange hello beacon with the
the counter refers to the aggregating number of nodes neighboring vehicles to prepare its communication. Each beacon
connected to the node at a unit of time and M is empty in contains the vehicle status and is stored in a list in all connected
the normal case. nodes. The list of the status in any vehicle is updated every
interval of time. Moreover the vehicles on the urban roads can
Warning info.
have a connection with one of the RSUs every now and then.
Tag Time stamp F_ID Code Priority With RSU‟s connection a vehicle can be informed with the latest
road traffic situation and at the same time, the vehicle can update
Fig. 5: Message M fields, where tag =1 refers to the the traffic database in the VuC with its carrying road information.
source vehicle, F_ID is the nearest unit identity, and Our suggestion is based on adopting the dissemination method
code is the message kind (DMSM) of reference [15], thus the pack format consists of a pair
of data ; where B represents the beacon data, and (M)
refers to the warning message type, which will remain empty
4.2 Proposed Scheme Algorithm during normal situation. Authors of reference [17] mentioned that
As clarified in figure 6, there are V2V and V2I the vehicle collision avoidance (VCA) latency has the long-run
communications that can handle any alert messages of an average time elapsed between sending and receiving VCA packet
abnormal event to the VuC database, which in return transmit successfully and should be less than 100 msec. Thus we assumed
ArcGIS server to make a decision about the clearest road's path to that each vehicle placed in front of other vehicles sends its hello
be sent to the civil defense center within a real time action. Our beacon backward every 50msec time interval and waited for
suggested scheme starts when an accident occurs and a near another 50msec for a reply.
intelligent vehicle senses this abnormal situation, the source node
will immediately create a warning message and start sending it Phase2: Creating and propagating the alert message
backward. The event source vehicle will keep resending its
emergency message to the chosen forwarder node from the same When an abnormal situation such as car accident sensed by
area until receiving an ACK reply. Likewise, every forwarder will neighboring vehicles, the nearest one with less distance away is
rebroadcast the message further through multi-hop until receiving considered as the source node and starts to create immediately the
a positive ACK from the nearest RSU. At this point the roads, alert message and keep resending it every time period. With the
traffic database stored in the VuC will be updated and GPS device's help, the source node already knows the position of
immediately inform the GIS center with the new situation to start the nearest RSU, thus it starts disseminating the emergency
analyzing and preparing the information on the shortest path to be message through a set of intermediate nodes until reaching a final
sent to the civil defense center. destination which is the nearest RSU. We are suggesting that the
adopted method DMSM must be modified to let the source node
stop sending immediately after receiving acknowledgement
(ACK) from the chosen forwarder which receives an ACK from
RSU simultaneously. Meanwhile RSU sends the message
information to the VuC traffic database, to start updating it with
the new data to begin phase 3. Figure 7 shows the algorithm steps
of phase 2.
Fig. 6: The framework of our system
1 Event: disseminating safety message 5. CONCLUSION
Combining ArcGIS server services with VANET safety
2 A source node sends M backward to the forwarder every services to predict road traffic densities is not a new idea, but
time interval using ArcGIS with SignalR library to create real time decision for
2.1 Check: if the source node receives an ACK from the the wireless vehicular networks is totally new. This study
forwarder the stop sending and go to step 3 otherwise go to produces a simple and new generation of real time services that
step 2 can support moving objects like vehicles with information all the
3 The forwarder rebroadcast M every 50 msec through time in the daily life. Our aspirations are to enhance and
multi-hops implement this study to reach the optimal level of application
performance that is used for social purposes. We intend to
3.1 Check: if the forwarder receives an ACK from RSU then implement the idea of this study to be the starting point for a
stop rebroadcasting and go to step 4, otherwise go to step 3 future project to be applied in practice.
4 RSU handle M to VuC traffic data base
5 start phase 3 6. REFERENCES
[1] Derekenaris, Grigoris, et al. "Integrating GIS, GPS and GSM
6 end
technologies for the effective management of
ambulances." Computers, Environment and Urban
Fig. 7: Algorithm steps for disseminating the safety Systems 25, no. 3 (2001): 267-278.
message M until reaching the infrastructure [2] Liang, Lim Yu, et al. "Traffic accident application using
geographic information system." Journal of the Eastern Asia
Society for Transportation Studies 6 (2005): 3574-3589.
Phase3: ArcGIS server decision [3] Tostes, Anna Izabel J., Fátima de LP Duarte-Figueiredo,
Renato Assunção, Juliana Salles, and Antonio AF Loureiro.
When the method reaches this phase, vehicles handle the “From data to knowledge: city-wide traffic flows analysis
responsibility to the GIS to analyze the incoming data and make a and prediction using bing maps.” In Proceedings of the 2nd
path decision. We suggest using the SignalR library to create real- ACM SIGKDD International Workshop on Urban
time, bidirectional, and asynchronous admin applications, which Computing, p. 12. ACM, 2013.
is based on standard web technologies. The application's output [4] Ali Jawad Ghandour. "On IEEE 802.11p/WAVE-based
can alert the center of defense to lead their vehicles during this vehicular networks: modeling, evaluation, and
emergency situation in an efficient way. The algorithm steps of enhancements." PhD thesis, Department of Electrical and
making the decision are illustrated in figure 8 and for more Computer Engineering, American University of Beirut, 2013.
clarification figure 9 demonstrated the sequence stages of the idea. [5] Rola Naja, “Wireless Vehicular Networks for Car Collision
Avoidance,” eBook by Springer Science and Business
Media, New York, 2013.
1 Event: making a decision
[6] Md Whaiduzzaman, Mehdi Sookhak, Abdullah Gani, and
2 The traffic database using SQL server transmit the new Rajkumar Buyya, "A survey on vehicular cloud computing,"
information to GIS server Journal of Network and Computer Applications, Elsevier, 40,
3 ArcGIS API supporting by signalR library process a real time pp. 325–344, 2014.
decision and choose the suitable roads path [7] Stephan Olariu1, Mohamed Eltoweissy, and Mohamed
Younis, "Towards autonomous vehicular clouds," ICST
4 Civil defense center receives the information and propagate it Transactions on Mobile Communications and Applications,
to lead all the civil defense vehicles in the area through the 2011.
suggested path [8] R. Hussain, J. Son, H. Eun, S. Kim, and H. Oh, "Rethinking
5 end vehicular communications: merging VANET with cloud
computing," Proceedings of IEEE 4th International
Conference on Cloud Computing Technology and Science
(CloudCom‟12), pp. 606 - 609, 2012.
Fig. 8: Algorithm steps of making real time decision [9] Rasheed Hussain and Heekuck Oh, "Cooperation-Aware
VANET Clouds Providing Secure Cloud Services to
Vehicular Ad Hoc Networks," J Inf Process Syst, Vol.10,
No.1, pp.103-118, March 2014.
[10] Jin Wang, Jinsong Cho, Sungyoung Lee, and Tinghuai Ma,
"Real Time Services for Future Cloud Computing Enabled
Vehicle Networks," IEEE, 2011.
[11] Junjie Peng, Xuejun Zhang, Zhou Lei, Bofeng Zhang, Wu
Zhang, and Qing Li, "Comparison of Several Cloud
Computing Platforms," Second International Symposium on
Information Science and Engineering, IEEE, 2009.
[12] Md Ali Al Mamun, Khairul Anam, Md Fakhrul Alam
Onik, and A M Esfar- E- Alam, "Deployment of Cloud
Computing into VANET to Create Ad Hoc Cloud Network
Architecture," Proceedings of the World Congress on
Fig. 9: collecting, and analyzing data to make a real
time decision
Engineering and Computer Science Vol. I, Oct. 24-26, San information networking and applications workshops
Francisco, USA, 2012. (WAINA), Switzerland, 2016.
[13] Nasser, Hussein. “Administering ArcGIS for Server.” Packt [16] Ghassan Samara, and Tareq Alhmiedat, “Intelligent
Publishing Ltd, 2014. emergency message broadcasting in VANET Using PSO,”
[14] Nayyeri, Keyvan, and Darren White. Pro ASP. NET World of Computer Science and Information Technology
SignalR: Real-time Communication in. NET with SignalR Journal (WCSIT), ISSN: 2221-0741, vol. 4, no. 7, 2014, pp.
2.1. Apress, 2014. 90-100.
[15] Hanaa Basheer, Carole Bassil, Bilal Chebaro, “A [17] Yin, Jijun, et al. „Performance evaluation of safety
Framework for Disseminating Safety Message in V2V applications over DSRC vehicular ad hoc networks.”
Communication.” 30th international conference on advance In Proceedings of the 1st ACM international workshop on
Vehicular ad hoc networks, pp. 1-9. ACM, 2004.