- Open Access
Fuzzy-assisted social-based routing for urban vehicular environments
© Khokhar et al; licensee Springer. 2011
- Received: 20 July 2011
- Accepted: 23 November 2011
- Published: 23 November 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.
- Source Node
- Data Packet
- Destination Node
- Fuzzy Inference System
- Road Segment
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 , GPCR , LOUVRE , geographical greedy traffic-aware routing (GyTAR) , RBVT-R , GeoCross  and ReTARS , 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.
In this article, we propose a FAST protocol to make dynamic routes based on prior global knowledge using friendship mechanism. Instead of simply forwarding the message to next available node towards destination like in existing VANET routing protocols, we use more reliable approach with the help of social relations of vehicles for optimal routing. The route message is forwarded to next available node in streets if and only if the intersection is far away from the node. In FAST, the packet career node at intersection plays a key role to select the best next road segments and leverages fuzzy inference system to make reliable and secure routing towards destination. The rest of the article is organized as follows. Section 2 presents the proposed FAST protocol with examples from urban environment. In Section 3, we evaluate the performance of FAST by comparing with some existing VANET routing protocols and the article concludes with some future studies in Section 4.
The performance of FAST is compared with the most related and widely used geographical and topology-based VANETs routing protocols such as GPSR , GPCR , RBVT-R  and GyTAR . A brief review of how each of these protocols operate is given as follows. GPSR is a geographical routing protocol which forwards data packets using greedy forwarding from the source node to the destination node. When a node cannot find a neighbor node closer to the destination position than itself, a recovery strategy based on planar graph traversal is applied. Similarly, GPCR  is an enhancement of GPSR routing protocol that utilizes the fact that the urban street map naturally forms a planar graph. If the nodes are in the street a restricted greedy routing is used and if the nodes are at intersection the repair strategy decides which street the data packet should follow next (by right-hand rule). RBVT-R is a topology-based reactive routing protocol which creates paths containing the successions of road intersections with high probability and network connectivity using real-time vehicular traffic information. GyTAR used traffic-information before establishing routes to handle intersection and dead-end roads, same as FAST has also addressed these problems. GyTAR is an intersection-based geographical greedy traffic-aware routing protocol which finds best routes in urban environments. It creates routes from source to destination based on sequence of connected intersections.
3.1 Simulation setup
Parameter values used in simulation for proposed FAST
940 m × 750 m
Number of vehicles
Number of CBR sources
CBR packet size
IEEE 802.11b DCF
Data packet size
With and without
The performance of the routing protocols was evaluated by varying numbers of concurrent flows, node densities and CBR data rates. PDR, average delay and average path length are the most straightforward methods of evaluating the application's performance. The metrics used to assess the performance are as follows:
Packet delivery ratio: PDR calculates the number of data packets sent by the source node and how much data packets (in %) the destination node successfully received. The duplicated data packets are not included that were generated by loss of acknowledgments at the MAC layer. The PDR shows the ability of the routing protocols to transfer vehicle-to-X data packets successfully.
Average delay: The average delay calculates the total time a message was posted by the source to destination node. The average delay characterizes the latency generated by the routing protocols.
Average path length: This evaluation metric calculates the number of hops which take part in the data packet forwarding from source to destination nodes. The hop count is used to determine the quality of path. This metric is used to verify if there is a correlation between the path length, average delivery ratio and average delay, respectively.
3.3 Simulation results in urban environment (with obstacles scenario)
GyTAR has 20% higher end-to-end delay than FAST in case of lower node density, 100 nodes, as shown in Figure 12a. GyTAR faces the problem of local optimum in sparse network as the next forwarding node might not be close to the next anchor. The average delays of RBVT-R are about 0.75s higher than FAST protocol in all cases, as depicted in Figure 12a-c. Similarly, the average delays of GPCR and GPSR are apparently higher than other protocols in first two cases, in Figure 12a,b. However, in case of dense network the difference is reduced about 1s, as shown in Figure 12c. This is because of GPCR and GPSR forward data packets between intersections based on the location of destination node. There are two side effects of this approach such as (1) it might be possible that the road segments are congested and overall quality of communication suffers significantly, and (2) the data packets forward across the intersection that may take high delay.
3.4 Simulation results in urban environment (without obstacles scenario)
In this scenario, the same Suffolk city map is used in with obstacle scenario. The obstacles are removed from the map in order to evaluate the performance of protocols under increased network congestion. The increase in the level of data sending rate will give us the noticeable increase in the level of contention in the network. The transmission range is set to 250 m for 150 nodes. With this range, it might be possible that the nodes can communicate with other nodes on the parallel streets. Figure 10 shows minimum distance between few streets less than 250 m. The 150 nodes are placed on the map using the random placement model and repeat the experiment for 15 flows. In each experiment, ten source and destination nodes pairs with different CBR and UDP packets are selected randomly. The other simulation parameters are almost the same as described in Table 3.
To evaluate the performance of protocols, the PDR and average delay are used by increasing packet/second from 0.5 to 5. The results for other node densities that were used in with obstacle scenario are same as for 150 nodes density. Therefore, only this scenario is used to describe the results for without obstacle.
In this article, we proposed a FAST protocol called FAST to make better routing decisions in urban vehicular environments. Instead of simply forwarding the messages to the next available node towards destination, FAST makes dynamic routes based on friendship mechanism and fuzzy inference system for significance performance of VANET routing protocol. The simulation results in urban environment for with and without obstacles scenario show that the FAST has high PDR, low average delay, fewer hops counts as compared to some existing VANET routing protocols.
Our future study includes designing a comprehensive and fully operational misuse and anomaly intrusion detection system for FAST protocol. Also, we are currently working on the design of a mechanism to tune fuzzy membership function universes with the volatile characteristics of VANET. Our intended optimization algorithms are Artificial Bees Algorithm, Genetic Algorithm or Particle Swarm Optimization. Then, the one offer less computation overhead will be the choice in vehicular environment.
We would like to thank University of Malaya to provide fund for this research and many thanks to anonymous reviewers for their useful criticism and suggestions to improve the quality of this article.
- Karp B, Kung HT: GPSR: greedy perimeter stateless routing for wireless networks. In MobiCom '00: Proceedings of the 6th annual international conference on Mobile computing and networking. New York, NY, USA (ACM); 2000:243-254.View ArticleGoogle Scholar
- Lochert C, Mauve M, Fubler H, Hartenstein H: Geographic routing in city scenarios. SIGMOBILE Mob Comput Commun Rev 2005, 9: 69-72.View ArticleGoogle Scholar
- Lee K, Le M, Harri J, Gerla M: LOUVRE: landmark overlays for urban vehicular routing environments. IEEE 68th Vehicular Technology Conference, 2008. VTC 2008-Fall 2008, 1-5.Google Scholar
- Jerbi M, Senouci SM, Rasheed T, Ghamri-Doudane Y: Towards efficient geographic routing in urban vehicular networks. IEEE Trans Veh Technol 2009, 58(9):5048-5059.View ArticleGoogle Scholar
- Nzouonta J, Rajgure N, Wang G, Borcea C: VANET routing on city roads using real-time vehicular traffic information. IEEE Trans Veh Technol 2009, 58(7):3609-3626.View ArticleGoogle Scholar
- Lee KC, Cheng PC, Gerla M: GeoCross: a geographic routing protocol in the presence of loops in urban scenarios. Ad Hoc Netw 2010, 8(5):474-488. 10.1016/j.adhoc.2009.12.005View ArticleGoogle Scholar
- Khokhar RH, Asri MN, Latiff MS, Amin MA: Reactive traffic-aware routing strategy for urban vehicular environments. Int J Ad Hoc Ubiq Comput 2011, in press.Google Scholar
- Bose P, Morin P, Stojmenovic I, Urrutia J: Routing with guaranteed delivery in ad hoc wireless networks. Wirel Netw 2001, 7(6):609-616. 10.1023/A:1012319418150View ArticleGoogle Scholar
- Kuhn F, Wattenhofer R, Zhang Y, Zollinger A: Geometric ad-hoc routing: of theory and practice. In PODC '03: Proceedings of the twenty-second annual symposium on Principles of distributed computing. New York, NY, USA (ACM); 2003:63-72.View ArticleGoogle Scholar
- Lee K, Haerri J, Lee U, Gerla M: Enhanced perimeter routing for geographic forwarding protocols in urban vehicular scenarios. Globecom Workshops, 2007 2007, 1-10. (IEEE)Google Scholar
- Chen KH, Dow CR, Chen SC, Lee YS, Hwang SF: HarpiaGrid: a geography-aware grid-based routing protocol for vehicular ad hoc networks. J Inf Sci Eng 2010, 26: 817-832.Google Scholar
- Leong B, Liskov B, Morris R: Geographic routing without planarization. In NSDI'06: Proceedings of the 3rd conference on Networked Systems Design & Implementation. Berkeley, CA, USA (USENIX Association); 2006:25.Google Scholar
- Kim YJ, Govindan R, Karp B, Shenker S: Geographic routing made practical. In Proceedings of the 2nd conference on Symposium on Networked Systems Design & Implementation. Volume 2. NSDI'05, Berkeley, CA, USA (USENIX Association); 2005:217-230.Google Scholar
- Razak SA, Furnell SM, Clarke NL, Brooke PJ: Friend-assisted intrusion detection and response mechanisms for mobile ad hoc networks. Ad Hoc Netw 2008, 6: 1151-1167. [http://portal.acm.org/citation.cfm?id=1389584.1389880] 10.1016/j.adhoc.2007.11.004View ArticleGoogle Scholar
- Razak SA, Samian N, Maarof MA, Furnell SM, Clarke NL, Brooke PJ: A friend mechanism for mobile ad hoc networks. J Inf Assur Secur 2009, 4: 440-448.Google Scholar
- Huang C, Chen I, Hu K, Shen H, Chen Y, Yang D: A load balancing and congestion-avoidance routing mechanism for teal-time traffic over vehicular networks. Univer Comput Sci 2009, 15(13):2506-2527.Google Scholar
- Zrar Ghafoor K, Abu Bakar K, van Eenennaam M, Khokhar R, Gonzalez A: A fuzzy logic approach to beaconing for vehicular ad hoc networks. Telecommun Syst 2011, 1-11.Google Scholar
- Mamdani E: Application of fuzzy logic to approximate reasoning using linguistic synthesis. IEEE Trans Comput 1977, C-26(12):1182-1191.View ArticleGoogle Scholar
- Briesemeister L, Hommel G: Role-based multicast in highly mobile but sparsely connected ad hoc networks, in. In MobiHoc '00: Proceedings of the 1st ACM international symposium on Mobile ad hoc networking & computing. Piscataway, NJ, USA (IEEE Press); 2000:45-50.Google Scholar
- Li F, Wang Y: Routing in vehicular ad hoc networks: a survey. IEEE Veh. Technol Mag 2007, 2(2):12-22.View ArticleGoogle Scholar
- Bernsen J, Manivannan D: Unicast routing protocols for vehicular ad hoc networks: a critical comparison and classification. Pervas Mob Comput 2009, 5: 1-18. 10.1016/j.pmcj.2008.09.001View ArticleGoogle Scholar
- Lee KC, Lee U, Gerla M: Survey of routing protocols in vehicular ad hoc networks. IGI Global 2010Google Scholar
- Tiger: tiger/line and tiger-related products. U.S. Census Bureau2011. [http://www.census.gov/geo/www/tiger/]
- Swans++: Swans++ Simulator2011. [http://www.aqualab.cs.northwestern.edu/projects/swans++/]
- Choffnes DR, Bustamante FE: An integrated mobility and traffic model for vehicular wireless networks. In VANET '05: Proceedings of the 2nd ACM international workshop on Vehicular ad hoc networks. New York, NY, USA (ACM); 2005:69-78.View ArticleGoogle Scholar
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