Performance Evaluation of Efficient Data Dissemination Approach For QoS Enhancement In VANETs

Performance evaluation of Efficient Data Dissemination
Approach For QoS Enhancement In VANETs
Er. Sachin Khurana
Research Scholar Department of Computer Science & Engineering,
Shri venkateshwara, University,
Gujraula
Sachin331@gmail.com
Dr. Gaurav Tejpal
Professor ,
Shri venkateshwara, University,
Gujraula
Gaurav_tejpal@rediffmail.com
Dr. Sonal Sharma
Assistant Professor, Department of computer Applications
Uttaranchal University
Dehradun, India
Sonal_horizon@rediffmail.com
Abstract Vehicular ad hoc networks (VANETs) have seen tremendous growth in the last decade, providing a vast
range of applications in both military and civilian activities. The temporary connectivity in the vehicles can also
increase the driver’s capability on the road. However, such applications require heavy data packets to be shared on
the same spectrum without the requirement of excessive radios. Thus, e-client approaches are required which can
provide improved data dissemination along with the better quality of services to allow heavy traffic to be easily
shared between the vehicles. In this paper, an e-client data dissemination approach is proposed which not only
improves the vehicle to vehicle connectivity but also improves the QoS between the source and the destination. The
proposed approach is analyzed and compared with the existing state-of-the-art approaches. The effectiveness of the
proposed approach is demonstrated in terms of the significant gains attained in the parameters namely, end to end
delay, packet delivery ratio, route acquisition time, throughput, and message dissemination rate in comparison with
the existing approaches.
Keywords VANETs, delay, QoS, Data Dissemination, Fuzzy sets
1 Introduction
Vehicular ad hoc networks (VANETs) comprise ground nodes moving in a particular topology over the road
segment with an ability to interact with each other as well as with the road side units (RSUs). The RSU forms the
infrastructure part of these networks. The intermittent connectivity between the vehicles allows these networks
temporary connectivity between the vehicles using the IEEE 802.11p and are also capable of supporting other IEEE
standards for inter-network communications [3] [4]. A sample VANETs network is illustrated in Fig. 1.
International Journal of Computer Science and Information Security (IJCSIS),
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Fig. 1 An Illustration of the VANETs
One of the crucial tasks in these networks is the e client delivery of data between the source and the destination
along with the provisioning of QoS to end users [5] [6]. There exist a number of approaches and solutions which
provide e-client data dissemination in these ad hoc formations, however, most of them are unable to provide QoS
which is the demand of the current scenario as more heavy traffic is being demanded over these networks. Thus,
data dissemination along with the provisioning of QoS to end users in VANETs is taken as a problem in this paper,
and an e-client strategy is proposed to resolve it.
In this paper, a novel strategy for data dissemination along with the provisioning of the QoS over VANETs is
presented. The proposed approach utilizes the features of the existing re y optimization algorithm and uses fuzzy
logic to derive the functional dependency of the parameters. Then, the proposed approach uses the derived value of
attraction to select the nodes to form a route between the source and the destination. The key highlights of the
proposed approach include, selection of the appropriate routes with minimum route acquisition delay, high packet
delivery ratio, high throughput and a low average end to end delay. The proposed approach is evaluated using the
simulations and is compared with the existing state-of-the-art approaches.
The remaining part of the paper is structured as follows: Section 2 provides the insight to existing literature for
data dissemination and quality of services over VANETs. Section 3 demonstrates the network model used for
developing the proposed approach. Section 4 gives the details of the proposed approach, Section 5 evaluates the
proposed approach in comparison with the existing solutions. Finally, Section 6 concludes the paper.
2 Related Work
Vehicular ad hoc networks have seen a tremendous shift in the approach towards the provisioning of QoS between
the vehicles. This allows support of heavy traffic at a better data rate which causes bulk messages to be transferred
between the vehicles, RSUs, and the central authority. A lot of work has been carried in the direction of data
dissemination, but these approaches are not exactly e-client to provide real-time QoS.
In a work by Chang et al. [7], the authors have presented a comparative study on the EDF-CSMA and QoS-
aware Hybrid Coordination Function (HCF) controlled channel access (HCCA) for improvement of data forwarding
in VANETs. Their work is based on the real-time video forwarding over CSMA. The approach provides a better
channel utilization in V2V networks. Al-Ani and Seitz [8] have worked in the ad hoc networks over the task of QoS
by developing a routing scheme based on the properties of Ant Colony Optimization. Their proposed approach uses
an estimation of QoS parameters to select optimally a route in dynamically changing topologies. Their work is
completely based on the simulations and is not applicable to the real environment.
Rizzo et al. [9] carried a study on the architectural requirement of the dense traffic networks. The authors carried
a study on the content-centric, software-de ned network, and context aware network formation in vehicular
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networks. The authors further investigated the requirement of tight QoS support by the vehicular networks. Kaur and
Malhotra [10] have carried a study on the selection of an appropriate routing approach in Nakagami propagation
model over VANETs. With varying densities and traffic size, the authors carried their analysis on the throughput,
packet delivery ratio, routing load and end-to-end delay as network parameters.
QoS based data dissemination is another aspect of networking with VANETs. In an approach by Iadicicco [11],
the authors focused on ETSI Geo networking standards to efficiently handle the traffic forwarding over VANETs.
Their approach is suitable for urban scenarios. But, the test of practicality is not carried on the real-time traffic. In
another work by Chaqfen and Lakas [12], the authors have provided a novel strategy for data dissemination in multi-
hop VANET. Their approach relies on the traffic estimation to provide selective broadcast in vehicular networks.
Their approach provides low overheads and high packet delivery ratio. Ali et al. [13] provided a strategy for
cooperative load balancing in VANETs which uses a xed road layouts to provide dynamic data forwarding with
tolerable delays. Their approach provides a workload balance among the junction-RSUs and edge-RSUs.
Mu'azu et al. [1] discussed an approach for QoS in VANETs which is analyzed in terms of throughput,
connection duration, and packet-loss. They discussed the importance of the clustering approach for analyzing the
importance of TCP and UDP in provisioning QoS in VANETs. Wahab et al. [14] introduced a new QoS-OLSR
protocol for VANETs. Their protocol relies on the formation of the stable clusters which are capable of maintaining
stable links even in the case of failures to provide an optimized QoS using the properties of the Ant Colony
Optimization algorithms. Reduced communication overheads are the key task of their pro-posed approach. Zhao et
al. [15] considered the two different aspects, namely, data pouring and buffering in VANETs to optimize the data
rates. The authors provide an analytical model to identify the dissemination capacity of the network which can be
further used to maximize the network parameters for better services.
The study and the literature presented above justifies the data dissemination and QoS provisioning as important
aspects of the VANETs, and also provides an insight that further new approaches are required which can improve
the quality of service using the relativity between the parameters rather than based on the absolute approaches to
attain high data rates. Considering the issues in the existing approaches, a new data dissemination approach is
proposed in the paper which not only estimates the network parameters but also enhance the QoS using a reliable
Neuro-fuzzy model.
3. Proposed Approach
The proposed data dissemination approach is derived from the properties of the re y optimization algorithm. Further,
in order to evaluate the attraction of the vehicles, fuzzy inference system is used to select the current value of
attraction between the vehicles. This section of the paper presents the detailed procedure for the route selection,
route maintainable, and rehabilitation on the basis of the network model derived in above section.
3.1 Route Selection
The route selection between the vehicles is based on the attraction value between each node to be selected as next
for data transmission. The complete area around a node is divided into different zones based on the attraction value.
The attraction value decreases with increase in the distance as well as an increase in the relative speed (opposite)
between the two vehicles. These zones help to select the node with optimal attraction value towards the destination.
A threshold zone is identified on the basis of the attraction value and any vehicle within this threshold zone can be
selected as next hop providing a cycle is not formed during the vehicle selection. This is done to prevent a routing
loop which is liable to occur in zone based data dissemination approaches. The diagram view of the zone around the
vehicle based on the attraction value and the threshold zone is illustrated in Fig. 7. According to this figure, the
vehicle can communicate with all vehicles except the vehicle with Id D". This threshold can be set on the basis of
the transmission rate (Tr) attained with an increase in the distance and a decrease in the radio range. Tr is accounted
for the threshold zone with a value above which the packet drop in a network increases. Alternatively, this threshold
value can be calculated on the basis of as its average for all the rules, as shown in Fig. 2. In case no vehicle is
available within the threshold value, two possible aspects are chosen for data transmission. One is to wait until a
vehicle arrives in the threshold zone of the requested source, the second is to increase the threshold value provided
that packet loss is not too high as shown in Fig. 8. Further, if power variation is allowed, vehicles can increase its
radio range to cover nearby vehicles for possible transmission. The steps for the route selection are presented in
Algorithm 1.
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Algorithm 1 performs the same steps demonstrated in the network model and in the proposed approach for route
selection. The algorithm is operated over each vehicle and is capable of selecting the route with high attraction
value. This selection criterion also provides a reliable and robust route selection as nodes with most optimal values
are selected for the formation of the routes between the source and the destination.
3.2 Route Rehabilitation
The route rehabilitation is performed during the continuous network operation of data dissemination between the
vehicles. This phase helps in maintenance of the route once selected in the initial phase. This phase also allows
selection of the alternative routes in case of failures or non-availability of the nodes. The steps for route
rehabilitation are shown in Algorithm 2. The algorithm checks for the route by re-beacons over the same source and
selecting the vehicles with the best attraction value which is not selected in the earlier route formation. If
Algorithm 1: Route Selection Based on the Attraction Value
1: Input: Vehicles and ROI
2: set a Threshold value
3: Divide the signal range into zones around a source
4: while Transmission is not completed do
5: Send beacon requesting for neighbors
6: calculate the attraction value between each available hop
7: arrange the vehicles in descending order of attraction value
8: if No Cycle Formed then
9: select the top vehicle
10: else
11: select the next best vehicle
12: end if
13: if No vehicle selected then
14: Increase the threshold value
15: Repeat steps 8 onwards
16: if still no vehicle selected then
17: Wait for a new vehicles arrival
18: end if
19: end if
20: check for continuous route towards destination
21: transmit
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22: end while
23: exit and check for maintenance
Algorithm 2: Route Rehabilitation
1: Input: Vehicles and ROI, failed vehicles
2: check for current state of vehicles
3: while transmission halted do
4: Resend the beacon from the source
5: collect attraction value data from replies
6: check if the nodes available already selected as next hop
7: identify hops not used before
8: select the node with highest attraction value from the identified hops
9: form link and proceed
10: check the route
11: if route is OK then
12: break
13: end if
14: end while
15: exit and continue with transmission
No new vehicle with better attraction value is found, the network tries to continue with already selected vehicles
by changing the parametric values.
The procedure of the route selection and rehabilitation allows selection of the optimal route between the
source and the destination. The attraction proper-ties account for stabilized zone formation which enables quick
selection of the next hop with minimum route acquisition time and lesser delay.
4.Results and Discussions
The proposed approach for the data dissemination was analyzed over simulations. At the initial level, the net-work
parameters were determined for comparison with the A-STAR [19] and GyTAR [20].
4.1 Taxonomy of Parameters
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In order to evaluate the proposed model and perform comparative analysis, following parameters were used:
Packet Delivery Ratio: It denotes the ratio of the successful delivery of the packets to the total number of packets
transmitted.
I. End-to-End Delay: In order to ensure the QoS, it is mandatory that the proposed approach should not cause
much delay as it would cause serious loss of information to the end users. Thus, for evaluating this factor,
end-to-end delay is computed which is the sum of the propagational delay, transmission de-lay, queuing
delay and processing delay.
II. Average Throughput: A network with better band-width utilization offers better QoS over the connected
channel. Thus, with throughput closer to the offered bandwidth, the network provides a stable and reliable
connectivity between the nodes. Here, average throughput is the number of bits actually transferred with
respect to the time taken.
III. Message Dissemination Rate: Message dissemination rate computes the number of messages transferred
per second over the connected link. This metric allows the testing of the reliable connectivity between the
network nodes along with the QoS.
4.2 Performance Evaluation
The proposed network approach for data dissemination was analyzed over an area of 5000x5000 sq.m. The number
of vehicles approaches the ROI with an average speed of 50Kmph following a Poisson distribution. The number of
RSUs was 10 per segment with a radio range of 500 m operating over the same spectrum with variable MAC for
vehicular communication and infrastructure communications. The details of parameters configured for the
simulations are shown in Table 1.
The proposed network was analyzed for the metrics defined in the taxonomy. Initially, the network was analyzed
for the end to end delay vs. the number of vehicles. With number of vehicles entering into the ROI during the same
instance, packet collision increases which cause more number of packets to be retransmitted, thus,
Table 1 Parameter Configurations
Parameter Value Description
A
S 80 Kmph Maximum Speed
4.0 Pathloss
Time 500 Vehicle
1000S Distribution
MAC IEEE 802.11 b Simulation Time
MAC IEEE 802.11 p Wi-Fi standard
RSU 10 per segment Road Side Units
R 500m Radio Range
Channel
Frequency 2.5GHz Frequency
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Fig. 2 End-to-End Delay vs. Vehicles
Increasing the end to end delay. Results show that the proposed model causes 60% less end to end delay than the A-
STAR, and 30% less end to end delay than the GyTAR approaches. Although, the increase in a number of vehicles
causes delays to increase, but this in-crease in delay is low enough that the proposed approach is not much affected
by this. The plot for the end to end delay comparison vs. the number of vehicles is shown in Fig.
Next, the proposed model was compared for the packet delivery ratio (PDR) with the GyTAR and the A-Star.
With number of vehicles, despite packet collision, number of packets is exchanged between the vehicles. This
increase in PDR is observed more in the case of the proposed approach in comparison with the GyTAR and A-
STAR. The proposed approach provided a PDR in the range of 60% to 96% with vehicles increasing from 50 to 500;
whereas A-STAR provided PDR in the range of 58% to 68% and GyTAR provided PDR in the range of 58% to 77%
over a same number of vehicles. Thus, with a requirement of the quality of services, it is always desired to have
higher PDR which is provided by the proposed approach.
Fig. 3 Packet Delivery Ratio vs. Vehicles
Message dissemination rate is accounted for the pure packets transmitted between the vehicles. Although, packet
delivery ratio increases with increase in the number of vehicles, but the useful messages shared between the vehicles
gradually decreases with increase in the density of the vehicles. The proposed approach pro-vided 58% and 42%
better message dissemination rate than the A-STAR and GyTAR, respectively, as shown in Fig.
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The other metric for the measurement of the performance is the average throughput over the network.
Throughput is directly related to the useful content shared between the vehicles. This useful content is analyzed in
terms of the message dissemination rate which causes the throughput to follow the similar trend. On an average, the
proposed approach provided 15% and
Fig. 4 Message Dissemination Rate vs. Vehicles
Fig. 5 Average Throughput vs. Vehicles
Fig. 6Average End-to-End Delay vs. Total Operational Time
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10% better throughput than the A-STAR and GyTAR, respectively.
The quality of service over the network is completely based on the end to end delay observed during the com-
plete network operations. A network with fewer delays accounts for the better quality of service. This allows the
formation of a stable, reliable and an e client net-work. The complete results traced for the end to end delay over the
proposed model show that the average values of the end to end delay were quite low than the other approaches as
shown in Fig. 13. The graph shows that the average end to end delay was negligible in the case of the proposed
approach, thus, provides the better quantity of service along with the data dissemination. The other detailed results
are shown in Table 2.
5.Conclusion
Data dissemination is one of the key issues with the VANETs. Although, several approaches have been pro-posed
over the years to provide e client data dissemination, yet provisioning of the quality of services is still an issue with
these networks. Considering this, a novel approach is proposed in this paper which utilizes the properties of re y
optimization algorithm in collaboration with the fuzzy logic to provide e client data dissemination. The proposed
approach is capable of providing e client data forwarding along with the improvement in Quality of Services in
comparison with the existing approaches
Compliance with Ethical StandardsConflict of Interest: The authors declare that they have no conflict of interest.
Informed consent: Authors have studied the COPE guidelines and have made sure that the manuscript falls well
under the standard rules for publication.
References
[1] A. Muazu, L. T. Jung, H. Hasbullah, I. A. Lawal, and P. A. Shah, Guaranteed qos for udp and tcpows to
measure throughput in vanets," in Advances in Computer Science and its Applications, pp. 1137{1143,
Springer, 2014.
[2] D. Al-Ani and J. Seitz, Qos-aware routing in multirate ad hoc networks based on ant colony optimization,"
Network Protocols and Algorithms, vol. 7, no. 4, pp. 1{25, 2016.
[3] Kaur and J. Malhotra, On the selection of e_clientback-o_ with qos aware routing in vanet,"
InternationalJournal of Future Generation Communication and Net-working, vol. 9, no. 2, pp. 163{176,
2016.
[4] Bezdek, James C. Pattern recognition with fuzzy objective function algorithms. Springer
Science & Business Media, 2013.
[5] Lochert, M. Mauve, H. F•uler, and H. Hartenstein, Geographic routing in city scenarios," ACM SIGMO-
BILE mobile computing and communications review, vol. 9, no. 1, pp. 69{72, 2005.
[6] C.-Y. Chang, H.-C. Yen, and D.-J. Deng, V2v qos guaranteed channel access in ieee 802.11 p vanets,"
IEEE Transactions on Dependable and Secure Comput-ing, vol. 13, no. 1, pp. 5{17, 2016.
[7] Chitra, M., S. Sivasathya, and B. Muthamizh. "Performance Analysis of Topology Based
Routing Protocols in VANET." (2014).
[8] B. Rawat, D. C. Popescu, G. Yan, and S. Olariu, En-hancingvanet performance by joint adaptation of
trans-mission power and contention window size," IEEE Trans-actions on Parallel and Distributed Systems,
vol. 22, no. 9, pp. 1528{1535, 2011.
[9] Dua, Amit, Neeraj Kumar, and Seema Bawa. "A systematic review on routing protocols
for vehicular adhoc networks." Vehicular Communications, no. 1 (2014).
[10]Eriksson, Jakob, Hari Balakrishnan, and Samuel Madden. "Cabernet: vehicular content
delivery using WiFi." Proceedings of the 14th ACM international conference on Mobile
computing and networking. ACM, 2008.
[11]G. M. N. Ali, P. H. J. Chong, S. K. Samantha, and E. Chan, E_client data dissemination in cooperative
multi-rsu vehicular ad hoc networks (vanets)," Journal of Systems and Software, vol. 117, pp. 508{527,
2016.
[12]G. Rizzo, M. R. Palattella, T. Braun, and T. Engel,Content and context aware strategies for
qossupportinvanets," in 2016 IEEE 30th International Conferenceon Advanced Information Networking
and Applications(AINA), pp. 717{723, IEEE, 2016.
Vol. 15, No. 8, Augus 2017
ISSN 1947-5500

[13]J. Zhao, Y. Zhang, and G. Cao, Data pouring and bu_ering on the road: A new data dissemination
paradigm for vehicular ad hoc networks," IEEE trans-actions on vehicular technology, vol. 56, no. 6, pp.
3266{ 3277, 2007.
[14]Kristiana, Lisa, Corinna Schmitt, and Burkhard Stiller. "Investigating a Reliable Inter-
Vehicle Network in a Three Dimensional Environment." (2015).
[15]M. Chaqfeh and A. Lakas, A novel approach for scalable multi-hop data dissemination in vehicular ad hoc
networks," Ad Hoc Networks, vol. 37, pp. 228{239, 2016.
[16]M. Jerbi, R. Meraihi, S.-M. Senouci, and Y. Ghamri- Doudane, Gytar: improved greedy tra_c aware
routingprotocol for vehicular ad hoc networks in city environments," in Proceedings of the 3rd international
workshopon Vehicular ad hoc networks, pp. 88{89, ACM, 2006
[17]O. A. Wahab, H. Otrok, and A. Mourad, Vanetqosolsr: Qos-based clustering protocol for vehicular ad hoc
networks," Computer Communications, vol. 36, no. 13, pp. 1422{1435, 2013.
[18]P. Tomar, B. K. Chaurasia, and G. Tomar, State of the art of data dissemination in vanets," International
Journal of Computer Theory and Engineering, vol. 2, no. 6, p. 957, 2010.
[19]S. Iadicicco, S. Infante, P. Salvo, A. Baiocchi, and F. Cuomo, Multi-originator data dissemination in
vanets," in 201612th Annual Conference on Wireless On-demand Network Systems and Services (WONS),
pp. 1{8, IEEE, 2016.
[20]T. Nadeem, P. Shankar, and L. Iftode, A comparative study of data dissemination models for vanets," in
Mobile and Ubiquitous Systems-Workshops, 2006. 3rd Annual International Conference on, pp. 1{10,
IEEE, 2006.
[21]Sharma, I. You, and R. Kumar, Energy e_client data dissemination in multi-uav coordinated wireless
sensor networks," Mobile Information Systems, vol. 2016, 2016.
[22]W. Chen, R. K. Guha, T. J. Kwon, J. Lee, and Y.-Y. Hsu, A survey and challenges in routing and data
dissemina-tion in vehicular ad hoc networks," Wireless Communi-cations and Mobile Computing, vol. 11,
no. 7, pp. 787-795, 2011.
[23]X.-S. Yang, Firey algorithms for multimodal optimization," in International Symposium on Stochastic
Algo- rithms, pp. 169{178, Springer, 2009.
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