Document Type : Original Article


1 Department of Engineering, Faculty of Engineering and Computer Science, Qaiwan International University, Sulaymaniyah, Iraq, Department of Information Technology, Kurdistan Technical Institute, Sulaymaniyah, Kurdistan Region, Iraq

2 Computer Science, College of Science, University of Halabja

3 Department of Information Technology, College of Science and Technology, University of Human Development, Sulaymaniyah, Iraq


Wireless sensor networks (WSNs) have found great appeal and popularity among researchers, especially in the field of monitoring and surveillance tasks. However, it has become a challenging issue due to the need to balance different optimization criteria such as power consumption, packet loss rate, and network lifetime, and coverage. The novelty of this research discusses the applications, structures, challenges, and issues we face in designing WSNs. And proposed new Topology control mechanisms it will focus more on building a reliable and energy efficient network topology step by step through defining available amount of energy for each node within its cluster, sorting all within header, and selecting an active one (more power header) for signal routing. While sensor cover topology demonstrates network monitoring capability, connection topology should remain as a requirement for the successful delivery of information including queries, data collected, and control messages. How to build an optimized coating topology while remaining efficient and low-cost connection is not well understood and needs further research. Power control and power management are two different types of topology controllers. Also in our study, we examine network lifetime, compared to other schemas time of death of the first node and the last node, and found that network lifetime was increased. Finally, a topology control method for extending network lifetime is presented.


  1. References

    1. G. E. Weir and US Naval History Center, “The American Sound Surveillance System: Using the ocean to hunt Soviet submarines, 1950-1961,” Int. J. Nav. Hist., vol. 5, no. 2, 2006.
    2. R. Silva, J. Sa Silva, and F. Boavida, “Mobility in wireless sensor networks - Survey and proposal,” Comput. Commun., vol. 52, pp. 1–20, 2014, doi: 10.1016/j.comcom.2014.05.008.
    3. A. O. Barznji, T. A. Rashid, and N. K. Al-salihi, “Computer Network Simulation of Firewall and VoIP Performance Monitoring A review of previous research works related to computer network is conducted in,” pp. 4–18.
    4. V. Shnayder, M. Hempstead, B. R. Chen, G. W. Allen, and M. Welsh, “Simulating the power consumption of large-scale sensor network applications,” SenSys’04 - Proc. Second Int. Conf. Embed. Networked Sens. Syst., pp. 188–200, 2004, doi: 10.1145/1031495.1031518.
    5. W. Qin, M. Hempstead, and Y. Woodward, “A realistic power consumption model for wireless sensor network devices,” 2006 3rd Annu. IEEE Commun. Soc. Sens. Adhoc Commun. Networks, Secon 2006, vol. 1, pp. 286–295, 2006, doi: 10.1109/SAHCN.2006.288433.
    6. W. Liu, K. Lu, J. Wang, G. Xing, and L. Huang, “Performance analysis of wireless sensor networks with mobile sinks,” IEEE Trans. Veh. Technol., vol. 61, no. 6, pp. 2777–2788, 2012, doi: 10.1109/TVT.2012.2194747.
    7. A. Hasan, T. A. Rashid, B. Ismael, and N. K. AL-Salihi, “Transmission Control Protocol Performance Monitoring for Simulated Wired University Computer Network using OPNET,” UKH J. Sci. Eng., vol. 3, no. 1, pp. 18–28, 2019, doi: 10.25079/ukhjse.v3n1y2019.pp18-28.
    8. C. G. Cassandras, W. Tao, and S. Pourazarm, “Optimal routing and energy allocation for lifetime maximization of wireless sensor networks with nonideal batteries,” IEEE Trans. Control Netw. Syst., vol. 1, no. 1, pp. 86–98, 2014, doi: 10.1109/TCNS.2014.2304367.
    9. G. P. Joshi, S. Y. Nam, and S. W. Kim, Cognitive radio wireless sensor networks: Applications, challenges and research trends, vol. 13, no. 9. 2013.
    10. K. Chen and W. Bi, “A new genetic algorithm for community detection using matrix representation method,” Phys. A Stat. Mech. its Appl., vol. 535, p. 122259, 2019, doi: 10.1016/j.physa.2019.122259.
    11. Q. M. Qadir, T. A. Rashid, N. K. Al-Salihi, B. Ismael, A. A. Kist, and Z. Zhang, “Low power wide area networks: A survey of enabling technologies, applications and interoperability needs,” IEEE Access, vol. 6, pp. 77454–77473, 2018, doi: 10.1109/ACCESS.2018.2883151.
    12. U. Bilstrup, K. Sjöberg, B. Svensson, and P. A. Wiberg, “Capacity limitations in wireless sensor networks,” IEEE Int. Conf. Emerg. Technol. Fact. Autom. ETFA, vol. 1, no. January, pp. 529–536, 2003, doi: 10.1109/ETFA.2003.1247752.
    13. R. Sharma, B. S. Sohi, and N. Mittal, “Hierarchical energy efficient MAC protocol for wireless sensor networks,” Int. J. Appl. Eng. Res., vol. 12, no. 24, pp. 14727–14738, 2017.
    14. L. M. Rodrigues, C. Montez, G. Budke, F. Vasques, and P. Portugal, “Estimating the lifetime of wireless sensor network nodes through the use of embedded analytical battery models,” J. Sens. Actuator Networks, vol. 6, no. 2, 2017, doi: 10.3390/jsan6020008.
    15. A. H. Pereira and C. B. Margi, “Energy management for wireless sensor networks,” SenSys 2012 - Proc. 10th ACM Conf. Embed. Networked Sens. Syst., vol. 1, no. 1, pp. 329–330, 2012, doi: 10.1145/2426656.2426692.
    16. H. Yetgin, K. T. K. Cheung, M. El-Hajjar, and L. Hanzo, “A Survey of Network Lifetime Maximization Techniques in Wireless Sensor Networks,” IEEE Commun. Surv. Tutorials, vol. 19, no. 2, pp. 828–854, 2017, doi: 10.1109/COMST.2017.2650979.
    17. M. Čagalj, J. P. Hubaux, and C. Enz, “Minimum-energy broadcast in all-wireless networks: NP-completeness and distribution issues,” Proc. Annu. Int. Conf. Mob. Comput. Networking, MOBICOM, pp. 172–182, 2002.
    18. S. Banerjee and A. Misra, “Minimum energy paths for reliable communication in multi-hop wireless networks,” Proc. Int. Symp. Mob. Ad Hoc Netw. Comput., pp. 146–156, 2002, doi: 10.1145/513800.513818.
    19. X. Y. Li and P. J. Wan, “Constructing minimum energy mobile wireless networks,” Proc. 2001 ACM Int. Symp. Mob. Ad Hoc Netw. Comput. MobiHoc 2001, vol. 5, no. 4, pp. 283–286, 2001, doi: 10.1145/501449.501460.
    20. L. Li and J. Y. Halpern, “Minimum-energy mobile wireless networks revisited,” IEEE Int. Conf. Commun., vol. 1, pp. 278–283, 2001, doi: 10.1109/icc.2001.936317.
    21. F. Li and I. Nikolaidis, “On Minimum-Energy Broadcasting in All-Wireless Networks,” pp. 193–202, 2001.
    22. V. Rodoplu and T. H. Meng, “Minimum energy mobile wireless networks,” IEEE J. Sel. Areas Commun., vol. 17, no. 8, pp. 1333–1344, 1999, doi: 10.1109/49.779917.
    23. A. Srinivas and E. Modiano, “Finding minimum energy disjoint paths in wireless ad-hoc networks,” Wirel. Networks, vol. 11, no. 4, pp. 401–417, 2005, doi: 10.1007/s11276-005-1765-0.
    24. P. J. Wan, G. Cǎlinescu, X. Y. Li, and O. Frieder, “Minimum-energy broadcasting in static Ad Hoc wireless networks,” Wirel. Networks, vol. 8, no. 6, pp. 607–617, 2002, doi: 10.1023/A:1020381720601.
    25. Y. Wu, P. A. Chou, and S. Y. Kung, “Minimum-energy multicast in mobile ad hoc networks using network coding,” IEEE Trans. Commun., vol. 53, no. 11, pp. 1906–1918, 2005, doi: 10.1109/TCOMM.2005.857148.
    26. D. Das, R. Misra, and A. Raj, “Approximating geographic routing using coverage tree heuristics for wireless network,” Wirel. Networks, vol. 21, no. 4, pp. 1109–1118, 2015, doi: 10.1007/s11276-014-0837-4.
    27. N. Li, J. C. Hou, and L. Sha, “Design and analysis of an MST-based topology control algorithm,” IEEE Trans. Wirel. Commun., vol. 4, no. 3, pp. 1195–1206, 2005, doi: 10.1109/TWC.2005.846971.
    28. M. Saravanan and M. Madheswaran, “A hybrid optimized weighted minimum spanning tree for the shortest intrapath selection in wireless sensor network,” Math. Probl. Eng., vol. 2014, 2014, doi: 10.1155/2014/713427.
    29. S. Hussain and O. Islam, “An energy efficient spanning tree based multi-hop routing in wireless sensor networks,” IEEE Wirel. Commun. Netw. Conf. WCNC, pp. 4386–4391, 2007, doi: 10.1109/WCNC.2007.799.
    30. L. Li, J. Y. Halpern, P. Bahl, Y. M. Wang, and R. Wattenhofer, “Analysis of a cone-based distributed topology control algorithm for wireless multi-hop networks,” Proc. Annu. ACM Symp. Princ. Distrib. Comput., pp. 264–273, 2001, doi: 10.1145/383962.384043.
    31. L. Li, J. Y. Halpern, P. Bahl, Y. M. Wang, and R. Wattenhofer, “A cone-based distributed topology-control algorithm for wireless multi-hop networks,” IEEE/ACM Trans. Netw., vol. 13, no. 1, pp. 147–159, 2005, doi: 10.1109/TNET.2004.842229.
    32. A. S. Shamsaldin, T. A. Rashid, R. A. Al-Rashid Agha, N. K. Al-Salihi, and M. Mohammadi, “Donkey and smuggler optimization algorithm: A collaborative working approach to path finding,” J. Comput. Des. Eng., vol. 6, no. 4, pp. 562–583, 2019, doi: 10.1016/j.jcde.2019.04.004.
    33. Hasan, A., Rashid, T. A., Ismael, B., & Al-Salihi, N. K. (2019). Transmission Control Protocol Performance Monitoring for Simulated Wired University Computer Network using OPNET. UKH Journal of Science and Engineering, 3(1), 18-28.
    34. Rashid, T. A., & Barznji, A. O. (2018). A virtualized computer network for salahaddin university new campus of HTTP services using OPNET simulator. In Online Engineering & Internet of Things (pp. 731-740). Springer, Cham.
    35. Rahman, C. M., & Rashid, T. A. (2020). A new evolutionary algorithm: Learner performance based behavior algorithm. Egyptian Informatics Journal.
    36. M. Dorigo and T. Stützle, Ant Colony Optimization. Cambridge, MA, USA: MIT Press, 2004.
    37. E. Bonabeau, M. Dorigo, and G. Theraulaz, ``Inspiration for optimization from social insect behaviour,'' Nature, vol. 406, no. 6791, pp. 39_42, Jul. 2000.
    38. J.-W. Lee and J.-J. Lee, ``Ant-colony-based scheduling algorithm for energy-ef_cient coverage of WSN,'' IEEE Sensors J., vol. 12, no. 10, pp. 3036_3046, Oct. 2012.
    39. J.-H. Zhong, J. Zhang, ``Energy-ef_cient local wake-up scheduling in wireless sensor networks,'' in Proc. IEEE Congr. Evol. Comput. (CEC), Jun. 2011, pp. 2280_2284.
    40. J.-W. Lee, B.-S. Choi, and J.-J. Lee, ``Energy-ef_cient coverage of wireless sensor networks using ant colony optimization with three types of pheromones,'' IEEE Trans. Ind. Informat., vol. 7, no. 3, pp. 419_427, Aug. 2011.
    41. X.-M. Hu and J. Zhang, ``Ant colony optimization for enhancing scheduling reliability in wireless sensor networks,'' in Proc. IEEE Int. Conf. Syst. Man Cybern. (SMC), Oct. 2012, pp. 785_790.
    42. X. Liu, ``Sensor deployment of wireless sensor networks based on ant colony optimization with three classes of ant transitions,'' IEEE Commun. Lett., vol. 16, no. 10, pp. 1604_1607, Oct. 2012.