Fuzzy-assisted social-based routing for urban vehicular environments

EURASIP Journal on Wireless Communications and Networking, Nov 2011

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 real-time 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.

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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)


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Rashid Hafeez Khokhar, Rafidah Md Noor. Fuzzy-assisted social-based routing for urban vehicular environments, EURASIP Journal on Wireless Communications and Networking, 2011, pp. 178, Volume 2011, Issue 1, DOI: 10.1186/1687-1499-2011-178