BF<sup>2</sup> VHDR based dynamic routing with hydrodynamics for QoS development in WSN

Suresh Y; Kalaivani T; Senthilkumar J; Mohanraj V; Suresh Y; Kalaivani T; Senthilkumar J; Mohanraj V

doi:10.3934/mbe.2020050

Mathematical Biosciences and Engineering

2020, Volume 17, Issue 1: 930-947. doi: 10.3934/mbe.2020050

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Research article Special Issues

BF² VHDR based dynamic routing with hydrodynamics for QoS development in WSN

1.
Department of Information Technology, Sona College of Technology, Salem-636005
2.
Department of Electronics and Communication Engineering, Sona College of Technology, Salem-636005

Received: 01 February 2019 Accepted: 30 September 2019 Published: 07 November 2019

The Hydrodynamic characteristics has been considered for routing in Wireless Sensor Networks by various researchers and presented several methods. The flow and friction based routing approaches would produces sustainable results but would not improve the QoS. To improve the performance of F²VHDR (Flow –Flow-Friction-Velocity Based Hydro Dynamic Routing), this paper present BF²VHDR (Back Flow –Flow-Friction-Velocity Based Hydro Dynamic Routing) algorithm. The F²VHDR method misses the back flow of packets due to route failure or higher traffic conditions which affects the service performance. As a solution to this, the BF²VHDR algorithm is presented. The proposed BF²VHDR approach monitors the flow in both the sides of the route. The back flow occurs when it exist a route failure and higher traffic. Also, it may occur when the routing protocol of the other nodes would choose the reverse route as a best way to reach the same destination. Monitoring the back flow, general route flow, friction by traffic and velocity measures, the proposed method computes backward hydrology routing weight and forward hydrology routing weight. Using both the measures, the proposed method computes a route support weight for each route which has been used to perform route selection. The proposed approach improves the performance of throughput and increases the lifetime of the sensor nodes.
- hydrodynamics,
- WSN,
- dynamic routing,
- BF² VHDR,
- QoS
Citation: Suresh Y, Kalaivani T, Senthilkumar J, Mohanraj V. BF2 VHDR based dynamic routing with hydrodynamics for QoS development in WSN[J]. Mathematical Biosciences and Engineering, 2020, 17(1): 930-947. doi: 10.3934/mbe.2020050

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Abstract

The Hydrodynamic characteristics has been considered for routing in Wireless Sensor Networks by various researchers and presented several methods. The flow and friction based routing approaches would produces sustainable results but would not improve the QoS. To improve the performance of F²VHDR (Flow –Flow-Friction-Velocity Based Hydro Dynamic Routing), this paper present BF²VHDR (Back Flow –Flow-Friction-Velocity Based Hydro Dynamic Routing) algorithm. The F²VHDR method misses the back flow of packets due to route failure or higher traffic conditions which affects the service performance. As a solution to this, the BF²VHDR algorithm is presented. The proposed BF²VHDR approach monitors the flow in both the sides of the route. The back flow occurs when it exist a route failure and higher traffic. Also, it may occur when the routing protocol of the other nodes would choose the reverse route as a best way to reach the same destination. Monitoring the back flow, general route flow, friction by traffic and velocity measures, the proposed method computes backward hydrology routing weight and forward hydrology routing weight. Using both the measures, the proposed method computes a route support weight for each route which has been used to perform route selection. The proposed approach improves the performance of throughput and increases the lifetime of the sensor nodes.

References

[1]	Y. H. Su, J. X. Li, Z. Q. Qin, et al., Maximizing the network lifetime by using PACO routing algorithm in wireless sensor networks, in Advances in Wireless Sensor Networks CWSN 2013, 418 (2013),155-165.
[2]	M. Kariman-Khorasani, M. Ali Pourmina and A. Salahi, Energy balance based lifetime maximization in wireless sensor networks employing joint routing and asynchronous duty cycle scheduling techniques, Wireless Pers. Commun., 83 (2015),1057-1083.
[3]	H. R. Karkvandi, E. Pecht and O. Yadid, Effective lifetime-aware routing in wireless sensor networks, IEEE Sensors J., 11 (2011), 3359-3367.
[4]	P. E. Linda, S. Dinesh and P. Edreena, Network lifetime maximization for estimation in multihop wireless sensor networks, Int. J. Adv. Res. Comput. Sci. Software Eng., 4 (2014).
[5]	N. V. Reddy and L. Jeba, Lifetime maximization in wireless sensor network, Int. J. Eng. Trends Technol., 9 (2014), 218-222.
[6]	M. Gribaudo, C. F. Chiasserini, R. Gaeta, et al., A spatial fluid-based framework toanalyze large-scale wireless sensor networks, in Proceedings of the International Conference on Dependable Systems and Networks, (2005), 694-703.
[7]	M. R. Pac, A. M. Erkmen and I. Erkmen, Towards fluent sensor networks: A scalable and robust self-deployment approach, in Proceedings of the 1st NASA/ESA Conference on Adaptive Hardware and Systems (AHS '06), (2006), 365-372.
[8]	W. Zhao, X. Y. Yan, F. Shao, et al., Collaborative routing protocol based on hydrodynamics for wireless sensor networks, Int. J. Distrib. Sensor Networks, 1 (2015).
[9]	A. Schillings and K. Yang, VGTR: A collaborative, energy and information aware routing algorithm for wireless sensor networks through the use of game theory, Geo. Sensor Networks, 5659 (2015), 51-62.
[10]	K. Khawam, A. E. Samhat, M. Ibrahim, et al., Fluid model for wireless adhoc networks, in Proceedings of the 18^th IEEE International Symposium on Personal, Indoor and Mobile Radio Communications, (2007),1-5,
[11]	C. F. Chiasserini, R. Gaeta, M. Garetto, et al., Fluid models for large-scale wireless sensor networks, Perform. Evalu., 64 (2007), 715-736.
[12]	H. Kim and C. Jennifer, A fast simulation framework for IEEE 802.11-operated wireless LANs, ACM SIGMETRICS Perform. Evalu. Rev., 32 (2004), 143-154.
[13]	T. C. Aysal and K. E. Barner, Constrained decentralized estimation over noisy channels for sensor networks, IEEE Trans. Signal Process, 56 (2008), 1398-1410.
[14]	T. C. Aysal and K. E. Barner, Blind decentralized estimation for bandwidth constrained wireless sensor networks, IEEE Trans. Wireless Commun., 7 (2008), 1466-1471.
[15]	M. Z. Hasan and F. Al-Turjman, Optimizing multipath routing with guaranteed fault tolerance in Internet of Things, IEEE Sensors J., 17(2017), 6463-6473.
[16]	G. Singh and F. Al-Turjman, A data delivery framework for cognitive information-centric sensor networks in smart outdoor monitoring, Comput. Commun. J., 74 (2015), 38-51.
[17]	Y. Zeng, L. Xu and Z. Chen, Fault-tolerant algorithms for connectivity restoration in wireless sensor networks, Sensors, 16 (2016).
[18]	W. H. Lim and N. A. M. Isa, Particle swarm optimization with adaptive time-varying topology connectivity, Appl. Soft Comput., 24 (2014), 623-642.
[19]	H. Bagci, I. Korpeoglu and A. Yazici, A distributed fault-tolerant topology control algorithm for heterogeneous wireless sensor networks, IEEE Trans. Parall. Distrib. Syst., 26(2015), 914-923.
[20]	Y. H. Robinson and M. Rajaram, Energy-aware multipath routing scheme based on particle swarm optimization in mobile ad hoc networks, Sci. World J., (2015), Article ID 284276.
[21]	H. Mostafaei, Energy efficient algorithm for reliable routing of wireless sensor networks, IEEE Transact. Indust. Elect, 66(2019), 5567-5575.

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