Fuzzy-assisted social-based routing for urban vehicular environments
Rashid Hafeez Khokhar
0
Rafidah Md Noor
0
Kayhan Zrar Ghafoor
2
Chih-Heng Ke
1
Md Asri Ngadi
2
0
Faculty of Computer Science and Information Technology, University of Malaya
, 50603 Lembah Pantai, Kuala Lumpur,
Malaysia
1
Department of Computer Science and Information Engineering, National Quemoy University
, Jinning, Kinmen 892,
Taiwan
, ROC
2
Faculty of Computer Science and Information Systems, Universiti Teknologi Malaysia
, 81310 Skudai, Johor,
Malaysia
In the autonomous environment of Vehicular Ad hoc NETwork (VANET), vehicles randomly move with high speed and rely on each other for successful data transmission process. The routing can be difficult or impossible to predict in such intermittent vehicles connectivity and highly dynamic topology. The existing routing solutions do not consider the knowledge that behaviour patterns exist in real-time urban vehicular networks. In this article, we propose a fuzzy-assisted social-based routing (FAST) protocol that takes the advantage of social behaviour of humans on the road to make optimal and secure routing decisions. FAST uses prior global knowledge of real-time vehicular traffic for packet routing from the source to the destination. In FAST, fuzzy inference system leverages friendship mechanism to make critical decisions at intersections which is based on prior global knowledge of realtime vehicular traffic information. The simulation results in urban vehicular environment for with and without obstacles scenario show that the FAST performs best in terms of packet delivery ratio with upto 32% increase, average delay 80% decrease, and hops count 50% decrease compared to the state of the art VANET routing solutions.
1 Introduction
Recently, the social-based networks have been built to
bring different groups of people within range for
potential communication. Such social-based networks are not
only used to connect the computers for global
communications network but it can also be used to connect
vehicles in urban environments. Social-based routing in
Vehicular Ad hoc NETwork (VANET) is attracted the
attention of research community where the traffic
information that behaviour patterns exist allow us to make
better routing decisions. VANET provides the ability for
vehicles to communicate wirelessly among nearby
vehicles and road-side wireless sensors to transfer
information for safe driving, dynamic route planning, mobile
sensing and in-car entertainment. Existing VANETs
routing protocols, for example, GPSR [1], GPCR [2],
LOUVRE [3], geographical greedy traffic-aware routing
(GyTAR) [4], RBVT-R [5], GeoCross [6] and ReTARS
[7], only work well in cooperative urban environments.
Currently, the vehicles have short radio communication
range from 300 to 1000 m based on IEEE 802.11p, and
VANET routing protocols need more vehicles to
transfer data to make one-one communications across wider
area. Consequently, it is necessary to develop efficient
routing protocols for growing vehicular networks.
Geographical routing protocols [1,2,4,8-11] are the
well-suited protocols for VANETs environments. These
protocols use Global Positioning System (GPS) to locate
nodes on the map instead of establishing routes to
forward data packets from source to the destination
through intermediate nodes (neighbors). Figure 1a
illustrates the routing strategy in these routing protocols in
ideal urban scenario with moderate, low or high
mobility. The source node S first transmits the message to its
neighbor nodes using greedy or geographical forwarding
method in the street and perimeter probing at
intersections. The message has been reached at intersection I2
through route R1 to R2 where the decision-making node
N takes an important decision. The node N selects
route R4 and finally reaches at destination node D
through R5. However, Figure 1b depicts the two
problems arise when these protocols are implemented on
real-world urban traffic scenario. First, it might be
possible that there is no node at intersection I2 within the
period of Time-to-Live (TTL) to make an important
decision. In this case, the message is forwarded to next
(a) Routes established in ideal city scenario
(b) Routes failure in real-world city scenario
Figure 1 Routing strategy in existing VANET routing protocols without prior global knowledge.
available node away from the intersection. Second, if
there is no vehicle on next routes, R4 and R6, it can
cause unnecessary traffic overhead in the network and
longer delays for packets.
Another major problem in VANET routing protocols
is the dead-end roads that may cause many data packets
dropped, failure notification increases significantly, low
delivery ratios and fail to find shortest path. As
illustrated in Figure 2, in most of the existing geographical
routing protocols the message forwards to nodes A, B
and C on a dead-end road which is the shortest path
from S to D. However, the message should follow the
dotted path as depicted in Figure 2. Greedy distributed
spanning (...truncated)