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ISSN E 2409-2770
ISSN P 2521-2419

Improved Steiner Tree Scheme Applied to Wireless Sensor Networks for Path and Energy Optimization


Vol. 6, Issue 10, PP. 347-353, October 2019


Keywords: Minimum Spanning Tree, Optimization, Particle Swarm, Shortest Path Tree, Wireless Sensor Networks

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This research work presents two optimization algorithms to optimize the path and energy in the wireless sensor network. Minimum Spanning Tree (MST) and Particle Swarm Optimization (PSO) algorithms both are utilized to optimize the path and energy of a system, which is connected on a fifty nodes network deployed randomly on 100x100 meters region. The proposed scheme is for the constrained improvement problem, or more explicitly, a weighted spanning tree problem and its appliance to Wireless Sensor Network (WSN) is examined here where definite exploratory discoveries on the energy improvement of the network have been exhibited.

  1. Muhammad Zahoor, , Department of Computer Systems Engineering, University of Engineering and Technology, Peshawar, Pakistan.

Muhammad Zahoor Improved Steiner Tree Scheme Applied to Wireless Sensor Networks for Path and Energy Optimization International Journal of Engineering Works Vol. 6 Issue 10 PP. 347-353 October 2019

[1]      S. Bouarafa, R. Saadane, and M. Rahmani, “Inspired from Ants Colony: Smart Routing Algorithm of Wireless Sensor Network,” Information, vol. 9, no. 1, p. 23, 2018.

[2]      A. Khan et al., “A localization-free interference and energy holes minimization routing for underwater wireless sensor networks,” Sensors (Switzerland), vol. 18, no. 1, pp. 1–17, 2018.

[3]      A. Ali, Y. Ming, T. Si, S. Iram, and S. Chakraborty, “Enhancement of RWSN lifetime via firework clustering algorithm validated by ANN,” Inf., vol. 9, no. 3, pp. 1–13, 2018.

[4]      A. Darwish and A. E. Hassanien, “Wearable and implantable wireless sensor network solutions for healthcare monitoring,” Sensors, vol. 11, no. 6, pp. 5561–5595, 2011.

[5]      S. Tomic and I. Mezei, “Improvements of DV-Hop localization algorithm for wireless sensor networks,” Telecommun. Syst., vol. 61, no. 1, pp. 93–106, 2015.

[6]      M. S. Elgamel and A. Dandoush, “A modified Manhattan distance with application for localization algorithms in ad-hoc WSNs,” Ad Hoc Networks, vol. 33, pp. 168–189, 2015.

[7]      R. Lachowski, M. Pellenz, M. Penna, E. Jamhour, and R. Souza, “An Efficient Distributed Algorithm for Constructing Spanning Trees in Wireless Sensor Networks,” Sensors, vol. 15, no. 12, pp. 1518–1536, 2015.

[8]      A. G. Bakirtzis, P. N. Biskas, C. E. Zoumas, and V. Petridis, “Optimal power flow by enhanced genetic algorithm,” IEEE Trans. Power Syst., vol. 17, no. 2, pp. 229–236, 2002.

[9]      B. Risteska Stojkoska, “Nodes Localization in 3D Wireless Sensor Networks Based on Multidimensional Scaling Algorithm,” Int. Sch. Res. Not., vol. 2014, pp. 1–10, 2014.

[10]   I. Banerjee, I. Roy, A. R. Choudhury, B. D. Sharma, and T. Samanta, “Shortest path based geographical routing algorithm in wireless sensor network,” Commun. Devices Intell. Syst. (CODIS), 2012 Int. Conf., pp. 262–265, 2012.

[11]   P. Chen, H. Ma, S. Gao, and Y. Huang, “SSL: Signal similarity-based localization for ocean sensor networks,” Sensors (Switzerland), vol. 15, no. 11, pp. 29702–29720, 2015.

[12]   J. Cota-Ruiz, J.-G. Rosiles, P. Rivas-Perea, and E. Sifuentes, “A Distributed Localization Algorithm for Wireless Sensor Networks Based on the Solutions of Spatially-Constrained Local Problems,” IEEE Sens. J., vol. 13, no. 6, p. 11, 2013.

[13]   Y. Shang, W. Ruml, Y. Zhang, and M. P. J. Fromherz, “Localization from mere connectivity,” Proc. 4th ACM Int. Symp. Mob. ad hoc Netw. Comput.  - MobiHoc ’03, p. 201, 2003.

[14]   A. a Kannan, G. Mao, and B. Vucetic, “Simulated Annealing based Wireless Sensor Network Localization with Flip Ambiguity Mitigation,” Most, vol. 00, no. c, 2006.

[15]   [C. Alippi and G. Vanini, “A {RSSI}-Based and Calibrated Centralized Llocalization Technique for Wireless Sensor Networks,” Proc. IEEE Int. Conf. Pervasive Comput. Commun. Work., 2006.

[16]   [B. Huang, C. Yu, and B. D. O. Anderson, “Analyzing Error Propagation in Range-based Multihop Sensor Localization,” Proc. IEEE Conf. Decis. Control, pp. 865–870, 2009.

[17]    G. Anastasi, M. Conti, M. Di Francesco, and A. Passarella, “Energy conservation in wireless sensor networks: A survey,” Ad Hoc Networks, vol. 7, no. 3, pp. 537–568, 2009.

[18]   R. Rajagopalan and P. Varshney, “Data aggregation techniques in sensor networks: A survey,” pp. 48–63, 2006.

[19]   K. Akkaya and M. Younis, “A survey on routing protocols for wireless sensor networks,” Ad Hoc Networks, vol. 3, no. 3, pp. 325–349, 2005.

[20]   S. Sudevalayam and P. Kulkarni, “Energy harvesting sensor nodes: Survey and implications,” IEEE Commun. Surv. Tutorials, vol. 13, no. 3, pp. 443–461, 2011.

[21]   I. Dietrich and F. Dressler, “On the lifetime of wireless sensor networks,” ACM Trans. Sens. Networks, vol. 5, no. 1, pp. 1–39, 2009.

[22]   C. E. Jones, K. M. Sivalingam, P. Agrawal, and J. C. Chen, “A survey of energy efficient network protocols for wireless networks,” Wirel. Networks, vol. 7, no. 4, pp. 343–358, 2001.

[23]   J. N. Al-Karaki and A. E. Kamal, “Routing techniques in wireless sensor networks: A survey,” IEEE Wirel. Commun., vol. 11, no. 6, pp. 6–27, 2004.

[24]   A. A. Abbasi and M. Younis, “A survey on clustering algorithms for wireless sensor networks,” Comput. Commun., vol. 30, no. 14–15, pp. 2826–2841, 2007.