Culminate Coverage for Sensor Network through Bodacious-Instance Mechanism

Shahzad Ashraf*
College of Internet of Things Engineering, Hohai University, Changzhou, China.
Periodicity:October - December'2019
DOI : https://doi.org/10.26634/jwcn.8.3.17310

Abstract

Due to unavoidable environmental factors, the wireless sensor networks are facing numerous tribulations regarding network coverage. This arose due to the uncouth deployment of the sensor nodes that influence the performance. To enhance the network coverage, a node deployment based Bodacious-instance Mechanism (BiM) has been proposed. Each instance describes a solution for the deployment of sensor nodes individually. Further variations of various parameters of BiM such as loudness, pulse emission rate, maximum frequency, grid points, and sensing radius has been explored and optimized values of these parameters are identified. The simulation results of node deployment based on tuned Bodacious-instance mechanism is also compared with BiM and fruit fly optimization algorithm (FOA) based node deployment in terms of mean coverage rate, computation time, and standard deviation. The coverage rate curve for various numbers of iterations and sensor nodes are also presented for tuned Bodacious-instance Mechanism, BiM, and FOA. The results demonstrate the effectiveness of tuned Bodacious-instance mechanism as it achieves more coverage rate than BiM and FOA.

Keywords

Bodacious-Instance, WirelessCoverage,NodeDeployment, SensorNodes,GridPoints, Iterations,Optimization.

How to Cite this Article?

Ashraf, S. (2019). Culminate Coverage for Sensor Network through Bodacious-Instance Mechanism. i-manager’s Journal on Wireless Communication Networks , 8(3), 1-9. https://doi.org/10.26634/jwcn.8.3.17310

References

[1]. Ashraf, S., Ahmed, T., Raza, A., & Naeem, H. (2020 a). Design of shrewd underwater routing synergy using porous energy shells. Smart Cities, 3(1), 74-92. https://doi.org/ 10.3390/smartcities3010005
[2]. Ashraf, S., Gao, M., Mingchen, Z., Ahmed, T., Raza, A., & Naeem, H. (2020). USPF: Underwater shrewd packet flooding mechanism through surrogate holding time Wireless Communications and Mobile Computing, 1–12. https://doi.org/10.1155/2020/9625974.
[3]. Ashraf, S., & Ahmed, T. (2020). Underwater Routing Protocols: Analysis of Intrepid Link Selection Mechanism, Challenges and Strategies. International Journal of Scientific Research in Computer Science, Engineering, 8(2), 1–9.https://doi.org/10.26438/ijsrcse/v8i2.19
[4]. Ashraf, S., Raza, A., Aslam, Z., Naeem, H., & Ahmed, T. (2020). Underwater resurrection routing synergy using astucious energy pods. Journal of Robotics and Control (JRC), 1(5), 173-184.
[5]. Ashraf, S., Ahmed, T., Saleem, S., & Aslam, Z. (2020). Diverging Mysterious in Green Supply Chain Management. Oriental Journal of Computer Science and Technology, 13(1), 22-28. http://doi.org/10.13005/ojcst13.01.02
[6]. Ashraf, S., Gao, M., Chen, Z., Kamran, S., & Raza, Z. (2017). Efficient node monitoring mechanism in WSN using contikimac protocol. International Journal of Advanced Computer Science and Applications, 8(11), 429-437.
[7]. Ashraf, S., & Ahmed, T. (2020). Challenging strategic trends in green supply chain management. International Journal of Engineering and Applied Sciences, 5(2), 71–74.https://doi.org/10.46565/jreas.2020.v05i02.006
[8]. Ashraf, S., Arfeen, Z. A., Khan, M. A., & Ahmed, T. (2014). SLM-OJ: Surrogate learning mechanism during outbreak juncture. International Journal for Modern Trends in Science and Technology, 6(5), 162–167. https://doi.org/10. 46501/IJMTST060525
[9]. Aoudia, F. A., Gautier, M., Magno, M., Berder, O., & Benini, L. (2016). A generic framework for modeling MAC protocols in wireless sensor networks. IEEE/ACM Transactions on Networking, 25(3), 1489-1500.
[10]. Balsamo, S., Marin, A., & Vicario, E. (Eds.). (2018). New Frontiers in Quantitative Methods in Informatics. Springer International Publishing.
[11]. Das, S., Biswas, A., Dasgupta, S., & Abraham, A. (2009). Bacterial foraging optimization algorithm: theoretical foundations, analysis, and applications. In Foundations of computational intelligence volume 3 (pp. 23-55). Heidelberg, Berlin: Springer. https://doi.org/10. 1007/978-3-642-01085-9_2
[12]. Dervis, K. (2010). Artificial bee colony algorithm. Scholarpedia, 5(3), 6915.
[13]. Erciyes University. (n.d.). Artificial Bee Colony (ABC) Algorithm Homepage. Erciyes University. https://abc.erc iyes.edu.tr
[14]. Franklin, J. E., & Urick, R. J. (1979). A binary detection model for at-sea sonar prediction. The Journal of the Acoustical Society of America, 66, S15. https://doi. org/10.1121/1.2017631
[15].Goyal, S., & Patterh, M. S. (2015, December). Flower pollination algorithm based localization of wireless sensor network. In 2015 2nd International Conference on Recent Advances in Engineering & Computational Sciences (RAECS) (pp. 1-5). IEEE. http://doi.org/10.1109/RAECS. 2015.7453299
[16]. Li, M., Du, X., Liu, X., & Li, C. (2019). Shortest path routing protocol based on the vertical angle for underwater acoustic networks. Journal of Sensors, 2019, 1-14. https:// doi.org/10.1155/2019/9145675
[17]. Marinaki, M., & Marinakis, Y. (2016). A glowworm swarm optimization algorithm for the vehicle routing problem with stochastic demands. Expert Systems with Applications, 46, 145-163. https://doi.org/10.1016/j.eswa. 2015.10.012
[18]. NOAA. (n.d.). Sonar. National Oceanic and Atmospheric Administration, U.S. Department of Commerce. https://oceanexplorer.noaa.gov/technology/sonar/sonar.html.
[19]. Pan, W. T. (2012). A new fruit fly optimization algorithm: taking the financial distress model as an example. Knowledge - Based Systems, 26, 69 - 74. https://doi.org/10.1016/j.knosys. 2011.07.001
[20]. Ren, Y., Li, J., Shi, S., Li, L., Wang, G., & Zhang, B. (2016). Congestion control in named data networking–a sur vey. Computer Communications, 86, 1-11. https://doi.org/10. 1016/j.comcom.2016.04.017
[21]. Stringer, H. (2007). Behavior of variable-length genetic algorithms under random selection. (Postgraduate Dessetation), University of Central Florida, Orlando, FL. 2004-2019.
[22]. Robinson, A. (2019, March 2). How to Calculate Euclidean Distance. Sciencing. https://sciencing.com /how-to-calculate-euclidean-distance-12751761.html
[23]. Zhang, J., Lei, Y., Chen, C., & Lin, F. (2016). Directional probability perceived nodes deployment based on particle swarm optimization. International Journal of Distributed Sensor Networks, 12(4), 2046392.
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