Wireless Communication

Open Access Open Access  Restricted Access Subscription Access
Open Access Open Access Open Access  Restricted Access Restricted Access Subscription Access

Wireless Sensor Node Deployment for Multi Hop Directional Network using Fuzzy Selection Optimization Algorithm


Affiliations
1 Department of Computer Applications (MCA), Kongu Engineering College/Anna University, Perundurai-60, Erode, India
     

   Subscribe/Renew Journal


In this paper, the problem of deploying heterogeneous mobile sensors over a target area is addressed. Traditional approaches to mobile sensor deployment are specifically designed for homogeneous networks.  Nevertheless, network and device homogeneity is an unrealistic assumption in most practical circumstances, and previous approaches fail when adopted in heterogeneous operative settings. For this reason, a generalization of the Voronoi-based approach which exploits the Laguerre geometry is introduced. The paper proves the appropriateness of the proposal to the optimization of heterogeneous networks. In addition, it demonstrates that it can be extended to deal with dynamically generated events or uneven energy depletion due to communications. Finally, by means of simulations, it shows that it provides a very stable sensor behavior, with fast and guaranteed termination and moderate energy consumption. It also shows that it performs better than its traditional counterpart and other methods based on virtual forces. In addition, this paper aims to identify optimal deployment locations of the given sensor nodes with a pre-specified sensing range, and to schedule them such that the network and coverage level.  This paper uses fuzzy selection optimization algorithm for sensor deployment problem followed by an effective for scheduling. In addition, fuzzy selection optimization algorithm is used to provide maximum network lifetime utilization. The comparative study shows that fuzzy selection optimization algorithm performs better than other optimization algorithm for sensor deployment problem. The proposed fuzzy logic was capable to reach the simulation value in all the experimented cases.


Keywords

Sensor Network, Sensor Deployment, Voronoi Diagram, Fuzzy Selection Optimization, Optimization, Virtual Forces Algorithm.
User
Subscription Login to verify subscription
Notifications
Font Size

  • BRORING, A. et al. New generation sensor web enablement. Sensors, 11, 2011, pp. 26522699. ISSN 1424-8220

  • KUMAR, S. and SHEPHERD, D. Sensit: Sensor information technology for the warfighter. Proceedings of the 4th International Conference on Information Fusion (FUSION’01), 2011, pp. 3-9.

  • CHONG, C.-Y. and KUMAR, S. P. Sensor networks: Evolution, opportunities, and challenges. Proceedings of the IEEE 91(8), 2013, pp. 1247-1256.

  • Mainwaring, A., Culler, D., Polastre, J., Szewczyk, R., Anderson, J.: Wireless sensor networks for habitat monitoring. In: Proceedings of the 1st ACM International Workshop on Wireless Sensor Networks and Applications, pp. 88–97. ACM (2012)

  • Sohraby, K., Minoli, D., Znati, T.: Wireless Sensor Networks: Technology, Protocols, and Applications. John Wiley and Sons Inc., New Jersey (2017)

  • Yi Zou , Krishnendu Chakra and A. K. Pujari, “Sensor Deployment Using Virtual Force Algorithm in wireless sensor networks,” in Proc. Int. Conf. Describe. Compute. Network. 2018, pp. 325–330

  • Yunxia Subgenus Chen and Y.-C. Tseng, “Sensor Placement for Maximizing Lifetime per Unit Cost in WSN,” in Proc. 2nd ACM Int. Conf. Wireless Sensor Network. Appl., 2016, pp. 115–121.

  • P. Corked, C.-Y. Chong and S. Kumar, “Autonomous Deployment Using Unmanned Aerial Vehicle Robot: Evolution, opportunities, and challenges,” Proc. IEEE, vol. 91, no. 8, pp. 1247–1256, Aug. 2016.

  • Krishnan Chakra D. Karaboga and B. Akay, “Grid Coverage for Surveillance in Distributed WSN and Algorithms simulating bee swarm intelligence,” Artif. Intel. Rev., vol. 31, nos. 1–4, pp. 61–85, 2015.

  • Pankaj K. Agarwa D. Karaboga and B. Basturk, “Efficient Sensor Placement for Surveillance Problems of artificial bee colony (ABC) algorithm,” Appl. Soft Compute., vol. 8, pp. 687–697, Jan.2018.

  • Jing LI, D. Karaboga, B. Gorkemli, C. Ozturk, and N. Karaboga, “Voronoi-Based Coverage Optimization algorithm and applications,” Artif. Intel. Rev., 2012, pp.1–37.

  • D. Karaboga, S. Okdem, and C. Ozturk, “Cluster based wireless sensor network routing using artificial bee colony algorithm,” Wireless Netw. vol. 18, no. 7, pp. 847–860, 2012.

  • G. Tan, S. Jarvis, and A.-M. Kenmare, “Connectivity-guaranteed and obstacle-adaptive deployment schemes for mobile sensor networks,” in Proc. 28th Int. Conf. Distribute. Computer. Syst., Jun. 2008, pp. 429–437.

  • Habit Mostafaei, Mehdi Esnaashari, and Mohammad Reza Meybodi , “A Coverage Monitoring algorithm based on Learning Automata for Wireless Sensor Networks” , Compute. Sci. Technol., vol. 26, no. 1, pp. 117–129, 2015.

  • J. Wang, R. Gosh, and S. Das, “A survey on sensor localization,” J. Control Theory Appl., vol. 8, no. 1, pp. 2–11, 2012.

  • S. Mini, S. K. Udgata, and S. L. Sabot, “A heuristic to maximize network lifetime for target coverage problem in wireless sensor networks,” Ad Hoc Sensor Wireless Netw., vol. 13, nos. 3–4, pp. 251–269, 2016.


Abstract Views: 256

PDF Views: 0




  • Wireless Sensor Node Deployment for Multi Hop Directional Network using Fuzzy Selection Optimization Algorithm

Abstract Views: 256  |  PDF Views: 0

Authors

K. Srihariakash
Department of Computer Applications (MCA), Kongu Engineering College/Anna University, Perundurai-60, Erode, India
J. Malarvizhi
Department of Computer Applications (MCA), Kongu Engineering College/Anna University, Perundurai-60, Erode, India
T. Kavitha
Department of Computer Applications (MCA), Kongu Engineering College/Anna University, Perundurai-60, Erode, India
S. Hemalatha
Department of Computer Applications (MCA), Kongu Engineering College/Anna University, Perundurai-60, Erode, India

Abstract


In this paper, the problem of deploying heterogeneous mobile sensors over a target area is addressed. Traditional approaches to mobile sensor deployment are specifically designed for homogeneous networks.  Nevertheless, network and device homogeneity is an unrealistic assumption in most practical circumstances, and previous approaches fail when adopted in heterogeneous operative settings. For this reason, a generalization of the Voronoi-based approach which exploits the Laguerre geometry is introduced. The paper proves the appropriateness of the proposal to the optimization of heterogeneous networks. In addition, it demonstrates that it can be extended to deal with dynamically generated events or uneven energy depletion due to communications. Finally, by means of simulations, it shows that it provides a very stable sensor behavior, with fast and guaranteed termination and moderate energy consumption. It also shows that it performs better than its traditional counterpart and other methods based on virtual forces. In addition, this paper aims to identify optimal deployment locations of the given sensor nodes with a pre-specified sensing range, and to schedule them such that the network and coverage level.  This paper uses fuzzy selection optimization algorithm for sensor deployment problem followed by an effective for scheduling. In addition, fuzzy selection optimization algorithm is used to provide maximum network lifetime utilization. The comparative study shows that fuzzy selection optimization algorithm performs better than other optimization algorithm for sensor deployment problem. The proposed fuzzy logic was capable to reach the simulation value in all the experimented cases.


Keywords


Sensor Network, Sensor Deployment, Voronoi Diagram, Fuzzy Selection Optimization, Optimization, Virtual Forces Algorithm.

References