Investigating the Impact of Adaptive Beaconing on GEOADV Performance

Routing in VANETs is challenging due to the high mobility and dynamic topology. Geographic routing is considered suitable for such networks. In geographic routing, nodes are able to maintain up-to-date information on their neighbors by using beaconing. Beaconing is successful in VANET environments but causes a large amount of routing overhead. This paper investigates the impact adaptive beaconing has on the Geographic Ad hoc On-Demand Distance Vector (GEOADV) protocol. In our adaptive beaconing scheme, each vehicle starts with the same initial broadcast interval. After the first broadcast, the next beacon interval is then determined by supplying the fuzzy inference engine with three inputs. These inputs are the number of neighbors in a one-hop distance, the speed of the vehicles, and the link quality of the network. GEOADV performance is evaluated with an adaptive beacon rate network and compared to its performance in three different fixed beacon rates networks. We are able to show that the adaptive beaconing scheme is able to reduce routing overhead, increases the average delivery ratio, and decreases the average delay. We are also able to show that by reducing beacon frequency the information accuracy is degraded and packets which are forwarded are less suitable or get dropped due to the route becoming stale.

[1]  Falko Dressler,et al.  Poster: A simulator for heterogeneous vehicular networks , 2014, 2014 IEEE Vehicular Networking Conference (VNC).

[2]  Reinhard German,et al.  Bidirectionally Coupled Network and Road Traffic Simulation for Improved IVC Analysis , 2011, IEEE Transactions on Mobile Computing.

[3]  Brad Karp,et al.  GPSR : Greedy Perimeter Stateless Routing for Wireless , 2000, MobiCom 2000.

[4]  J. Mendel Fuzzy logic systems for engineering: a tutorial , 1995, Proc. IEEE.

[5]  Imad Mahgoub,et al.  An enhanced directional greedy forwarding for VANETs using link quality estimation , 2016, 2016 IEEE Wireless Communications and Networking Conference.

[6]  Robert LIN,et al.  NOTE ON FUZZY SETS , 2014 .

[7]  Zhizhong Ding,et al.  An Improved GPSR Routing Strategy in VANET , 2012, 2012 8th International Conference on Wireless Communications, Networking and Mobile Computing.

[8]  Anil Kumar Verma,et al.  Simulation and Analysis of AODV Routing Protocol in VANETs , 2012 .

[9]  Cristian Borcea,et al.  VANET Routing on City Roads Using Real-Time Vehicular Traffic Information , 2009, IEEE Transactions on Vehicular Technology.

[10]  M. Azizur Rahman,et al.  Adaptive beaconing system based on fuzzy logic approach for vehicular network , 2013, 2013 IEEE 24th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC).

[11]  Lawrence Wai-Choong Wong,et al.  A reliable routing protocol for VANET communications , 2011, 2011 7th International Wireless Communications and Mobile Computing Conference.

[12]  Mayank Dave,et al.  A Review of Various VANET Data Dissemination Protocols , 2012 .

[13]  Imad Mahgoub,et al.  Fuzzy Logic-Based Broadcast in Vehicular Ad Hoc Networks , 2016, 2016 IEEE 84th Vehicular Technology Conference (VTC-Fall).

[14]  Vahid Sattari-Naeini,et al.  Application of fuzzy logic for selecting the route in AODV routing protocol for vehicular ad hoc networks , 2015, 2015 23rd Iranian Conference on Electrical Engineering.

[15]  Imad Mahgoub,et al.  A geographical hybrid solution for Inter-Vehicular Communication in VANET , 2016, 2016 International Wireless Communications and Mobile Computing Conference (IWCMC).

[16]  E. H. Mamdani,et al.  An Experiment in Linguistic Synthesis with a Fuzzy Logic Controller , 1999, Int. J. Man Mach. Stud..

[17]  Gaurav Sharma,et al.  A study of position based VANET routing protocols , 2016, 2016 International Conference on Computing, Communication and Automation (ICCCA).

[18]  Sung-Ju Lee,et al.  Mobility prediction and routing in ad hoc wireless networks , 2001, Int. J. Netw. Manag..

[19]  Bertrand Ducourthial,et al.  Enhancing ns-2 simulator for high mobility ad hoc networks in Car-to-Car communication context , 2005 .

[20]  Limouchi Elnaz,et al.  BEFLAB: Bandwidth efficient fuzzy logic-assisted broadcast for VANET , 2016 .

[21]  Imad Mahgoub,et al.  Fuzzy logic based localization for vehicular ad hoc networks , 2014, 2014 IEEE Symposium on Computational Intelligence in Vehicles and Transportation Systems (CIVTS).

[22]  Daniel Krajzewicz,et al.  Recent Development and Applications of SUMO - Simulation of Urban MObility , 2012 .

[23]  Md. Abu Naser Bikas,et al.  VANET Routing Protocols: Pros and Cons , 2011, ArXiv.

[24]  Celimuge Wu,et al.  Flexible, Portable, and Practicable Solution for Routing in VANETs: A Fuzzy Constraint Q-Learning Approach , 2013, IEEE Transactions on Vehicular Technology.

[25]  W. C. Wong,et al.  A fuzzy-decision-based routing protocol for mobile ad hoc networks , 2002, Proceedings 10th IEEE International Conference on Networks (ICON 2002). Towards Network Superiority (Cat. No.02EX588).