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International Journal of Sensors, Wireless Communications and Control

Editor-in-Chief

ISSN (Print): 2210-3279
ISSN (Online): 2210-3287

Research Article

Optimization Based Sink Deployment Technique in WSN to Improve Network Performance

Author(s): Sonal Telang Chandel* and Sanjay Sharma

Volume 10, Issue 2, 2020

Page: [217 - 230] Pages: 14

DOI: 10.2174/2213275912666190410150204

Price: $65

Abstract

Background & Objective: Currently, WSN (Wireless Sensor Networks) provides a variety of services in industrial and commercial applications. WSN consists of nodes that are used to sense the environments like humidity, temperature, pressure, sound, etc. As the use of WSN grows there are some issues like coverage, fault tolerance, a deployment problem, localization, Quality of Service, etc. which needs to be resolved. Sink deployment is a very important problem because it is not the only impact on performance, but also influence on deployment cost. In traditional WSN, a single sink is deployed in the network, which aggregates all the data. Due to this, the whole network is suffering from some serious issues like delay, congestion, network failure that reduces network performance.

Methods: One solution is to deploy multiple sinks instead of a single sink. Deploying multiple sinks can improve network performance, but increases sink deployment cost. In this paper, an ISDOA (Improved Sink Deployment Optimization Algorithm) is proposed to find the optimum number of sinks and their optimum location in ROI. Simulation is carried out in Matlab simulator. The impact of sensors and sinks on various network performance parameters like throughput, network lifetime, packet delivery ratio, energy consumption and cost of the network is analyzed.

Results & Conclusion: It is shown by simulation results that the number of sinks varies inversely with energy consumption of the nodes; and it is linearly proportional to the network lifetime, throughput and packet delivery ratio. Furthermore, results show that the proposed approach outperforms random deployment with 25% higher throughput, 30% better network lifetime, 15% lesser energy consumption and 21% optimized cost of the network, respectively.

Keywords: Cost of the network, deployment problem, energy consumption, network lifetime, optimization technique, packet delivery ratio, throughput, wireless sensor network.

Graphical Abstract

[1]
Dina SD, Yasser G. An ant colony optimization approach for the deployment of reliable wireless sensor networks In: IEEE Translations. 2017; pp. 10744-56.
[2]
Flammini A, Sisinni E. Wireless sensor networking in the internet of things and cloud computing era. Procedia Eng 2014; 87: 672-9.
[http://dx.doi.org/10.1016/j.proeng.2014.11.577]]
[3]
[4]
Harrop DP, Das R. Wireless Sensor Networks (WSN) forecast, technologies, players, IDTechEx 2014; 2012-2.www.idtechex.com
[5]
[6]
Akbas S, Achirt N. Performance evaluation of PIR sensor deployment in critical area surveillance networks. 2014 IEEE International Conference on Distributed Computing in Sensor Systems. Marina Del Rey, CA, USA, 2014. 2014.
[http://dx.doi.org/10.1109/DCOSS.2014.56]
[7]
Basu D, Moretti G. Wireless sensor network based smart home: Sensor selection, deployment and monitoring. Proc IEEE Sensor Appl Symp 2013 2013; 49-54.
[http://dx.doi.org/10.1109/SAS.2013.6493555]
[8]
Ke W, Liu BH, Tsai MJ. Constructing a wireless sensor network to fully cover critical grids by deploying minimum sensors on grid points is NP-complete. IEEE Trans Comput 2007; 56(5): 710-5.
[http://dx.doi.org/10.1109/TC.2007.1019]
[9]
Zhao C, Wc C, Wang X. Maximizing lifetime of a wireless sensor network via joint optimizing sink placement and sensor-to-sink routing. Appl Math Model 2007; 49: 319-37.
[10]
Tiegang F, Guifa T, Limin H. Deployment strategy of WSN based on minimizing cost per unit area. Comput Commun 2014; 38: 26-35.
[http://dx.doi.org/10.1016/j.comcom.2013.10.002]
[11]
Arkin EM, Efrat A, Mitchell JSB. Data transmission and base-station placement for optimizing the lifetime of wireless sensor networks. Ad Hoc Netw 2015; 12: 201-18.
[12]
Rebai M, Leberre M, Snoussi H, Hnaien F, Khoukhi L. Sensor deployment optimization methods to achieve both coverage and connectivity in wireless sensor networks. Comput Oper Res 2015; 59: 11-21.
[http://dx.doi.org/10.1016/j.cor.2014.11.002]
[13]
Lee JH, Moon I. Modelling and optimization of energy efficient routing in wireless sensor networks. Appl Math Model 2014; 38: 2280-9.
[14]
Nayyar A, Gupta A. A comprehensive review of cluster-based energy efficient routing protocols in wireless sensor networks. Int J Res Comput Commun Technol 2014; 3(1): 104-10.
[15]
Kumar A, Nayyar A. Energy efficient routing protocols for Wireless Sensor Networks (WSNS) based on clustering. Int J Sci Eng Res 2014; 5(6): 440-8.
[16]
Sharma S, Gupta M, Nayyar A. Review of routing techniques driving wireless sensor networks. Int J Comput Sci Mobile Comput 2014; 3(5): 112-22.
[17]
Gupta A, Gupta M, Nayyar A. Approaches for combating delay and achieving optimal path efficiency in wireless sensor networks. Int J Comput Science Mobile Comput 2014; 3(5): 105-11.
[18]
Dai S, Tang C, Qia S, Xu K, Li H, Zhu J. Optimal multiple sink nodes deployment in wireless sensor networks based on gene expression programming. 2010 Second International Conference on Communication Software and Networks Singapore.
[http://dx.doi.org//10.1109/ICCSN.2010.33]
[19]
Sitanayah L, Brown KN, Sreenan CJ. Planning the deployment of multiple sinks and relays in wireless sensor networks. J Heuristics 2015; 21: 197-232.
[http://dx.doi.org/10.1007/s10732-014-9256-z]
[20]
Safa H, El-Hajj W, Zoubian H. Particle swarm optimization based approach to solve the multiple sink placement problem in WSNs. IEEE International Conference on Communications (ICC) Ottawa, ON, Canada,.
[http://dx.doi.org/10.1109/ICC.2012.6363906]
[21]
Safa H, Moussa M, Artail H. An energy efficient genetic algorithm based approach for sensor-to-sink binding in multi-sink wireless sensor networks. Wirel Netw 2014; 20: 177-96.
[http://dx.doi.org/10.1007/s11276-013-0600-2]
[22]
Kim D, Wang W, Sohaee N. Minimum data-latency-bound k-sink placement problem in wireless sensor networks. IEEE/ACM Trans Netw 2011; 19(5): 1344-53.
[http://dx.doi.org/10.1109/TNET.2011.2109394]
[23]
Snigdh I, Gosain D, Gupta N. Optimal sink placement in backbone assisted wireless sensor networks. Egypt Inform J 2016; 17: 217-25.
[http://dx.doi.org/10.1016/j.eij.2015.09.004]
[24]
Kuila P, Jana PK. Energy efficient clustering and routing algorithms for wireless sensor networks: Particle swarm optimization approach, 2014.
[25]
Kosar R, Ersoy C. Sink placement on a 3D terrain for border surveillance in wireless sensor networks. Eng Appl Artif Intell 2012; 25: 82-93.
[26]
Huang G, Chen D, Liu X. A node deployment strategy for blindness avoiding in wireless sensor networks. IEEE Commun Lett 2015; 19(6): 1005-8.
[http://dx.doi.org/10.1109/LCOMM.2014.2379713]
[27]
Liu X. A deployment strategy for multiple types of requirements in wireless sensor networks. IEEE Trans Cybern 2015; 45(10): 2364-76.
[http://dx.doi.org/10.1109/TCYB.2015.2443062]
[28]
Ted TTL, Chen WJ, Li KH, Huang P, Chu HH. TriopusNet: Automating wireless sensor network deployment and replacement in pipeline monitoring. Proceedings of the International Conference on Information Processing in Sensor Networks 2012.
[29]
Aznoli F, Navimipour NJ. Deployment strategies in the wireless sensor networks: Systematic literature review, classification, and current trends. Wirel Pers Commun 2017; 95(2): 819-46.
[http://dx.doi.org/10.1007/s11277-016-3800-0]
[30]
Chang BJ, Peng JB. On the efficient and fast response for sensor deployment in sparse wireless sensor networks. Comput Commun 2007; 30(18): 3892-903.
[http://dx.doi.org/10.1016/j.comcom.2007.10.004]
[31]
Ram SR, Shailender K, Sonia M, Sambit B. Comparison and analysis of node deployment for efficient coverage in sensor network. In: Intelligent Computing. Networking, and Informatics 2013; pp. 31-43.
[32]
Mahmud S, Wu H, Xue J. Efficient energy balancing aware multiple base station deployment for WSNs. European Conference on Wireless Sensor Networks 2011.
[http://dx.doi.org/10.1007/978-3-642-19186-2_12]
[33]
Al-karaki JN, Amjad G. The optimal deployment, coverage, and connectivity problems in wireless sensor networks: Revisited. IEEE Access 2017; 5: 18051-65.
[http://dx.doi.org/10.1109/ACCESS.2017.2740382]
[34]
Jis MJ, Anita J. Improving lifetime of structured deployed wireless sensor network using sleepy algorithm. ICECCS 2012: Ecofriendly computing and communication systems 2012; 47-53.
[35]
MATLAB. MATLAB 2014 In: The Math Works, Natick. 2014.
[36]
Nayyar A, Singh R. A comprehensive review of simulation tools for Wireless Sensor Networks (WSNs). J Wirel Commun Netw 2015; 5(1): 19-47.

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