Localization of Nodes in Wireless Sensor Networks using a Combination of Krill Herd Algorithm with Ant Colony Optimization | ||
| Journal of Communication Engineering | ||
| مقاله 10، دوره 9، شماره 2 - شماره پیاپی 19، مهر 2020، صفحه 316-336 اصل مقاله (1.3 M) | ||
| نوع مقاله: Research Paper | ||
| شناسه دیجیتال (DOI): 10.22070/jce.2022.14943.1196 | ||
| نویسندگان | ||
| Jafarsadegh kamfar1؛ Hessam Zandhessami* 2؛ Mahmood Alborzi3 | ||
| 1Jafarsadegh Kamfar: Department of Information Technology management, Science and Research Branch, Islamic Azad University, Tehran, Iran. | ||
| 2Department 0f Information Technology Management, Science and Research branch, Islamic Azad University, Tehran, Iran | ||
| 3Department 0f Information Technology Management, Science and Research branch, Islamic Azad University, Tehran, Iran. | ||
| چکیده | ||
| Abstract- The industrial revolution and the spread of electronic technologies and wireless communications have led to the production of small smart sensors with low consumption and low-cost benefits. Sensor nodes work as autonomous systems with low cost, small size, and wireless communication media but work with low resources. The most significant item in the operation of Wireless Sensor Networks (WSNs) is finding the spatial information of objects, including retrieval and identification of events, routing according to geometric position, monitoring and tracking. Localization in WSNs divides into two range-based and range-free categories. In this paper, to overcome the weaknesses of DV-Hop, a hybrid model based on the Krill Herd Algorithm and Ant Colony Optimization called KHAACO was proposed for locating unknown nodes. The aim of this study is to provide an approach for estimating the location of sensor nodes with minimal error and using KHAACO to estimate the location of unknown nodes and using the motion characteristics of other krill, foraging, and spatial dispersion of the KHA and optimizing it with ACO. The evaluation of the hybrid model in the MATLAB environment has been done based on error criteria and energy consumption. The results showed that the hybrid model compared to DV-Hop, DV-Hop-ACO, and DV-Hop-PSO reduced the Localization error. The value of reduction of localization error for 90 anchor nodes and 450 sensor nodes was equal to 9.95%. | ||
| کلیدواژهها | ||
| Wireless sensor networks؛ Localization؛ DV-Hop؛ Krill Herd optimization؛ Ant colony optimization | ||
| مراجع | ||
|
1- D. T. Tchakonte, E. Simeu, and M. Tchuente, “Lifetime optimization of wireless sensor networks with sleep mode energy consumption of sensor nodes,” Wireless Networks, vol. 26, no. 1, pp. 91-100, Jan. 2020.
2- Z. Al Aghbari, A. M. Khedr, W. Osamy, I. Arif, and D. P. Agrawal, “Routing in wireless sensor networks using optimization techniques: a survey,” Wireless Personal Communications, vol. 111, pp. 2407-2434, April 2020.
3- W. Zhao, S. Su, and F. Shao, “Improved DV-Hop algorithm using locally weighted linear regression in anisotropic wireless sensor networks,” Wireless Personal Communications, vol. 98, no.1, pp. 3335-3353, Feb. 2018.
4- A. J. Al-Mousawi, “Evolutionary intelligence in wireless sensor network: routing, clustering, localization and coverage,” Wireless Networks, vol. 26, no. 1, pp. 5595-5621, Nov. 2020.
5- V. Annepu and A. Rajesh, “Implementation of an efficient artificial bee colony algorithm for node localization in unmanned aerial vehicle assisted wireless sensor networks,” Wireless Personal Communications, vol. 114, no. 1, pp. 2663-2680, Oct. 2020.
6- X. Yan, P. Zhou, Q. Luo, C. Wang, J. Ding, and C. Hu, “UAM-RDE: an uncertainty analysis method for RSSI-based distance estimation in wireless sensor networks,” Neural Computing and Applications, vol. 32, no. 1, pp. 13701-13714, Sep. 2020.
7- J. Mass-Sanchez, E. Ruiz-Ibarra, J. Cortez-González, A. Espinoza-Ruiz, and L. A. Castro, “Weighted hyperbolic dv-hop positioning node localization algorithm in WSNs,” Wireless Personal Communications, vol. 96, pp. 5011-5033, Oct. 2017.
8- Z. Fang, Z. Zhao, D. Geng, Y. Xuan, L. Du, and X. Cui, “RSSI variability characterization and calibration method in wireless sensor network,” in The 2010 IEEE International Conference on Information and Automation, pp. 1532-1537, July 2010.
9- D. Niculescu and N. Badri, “Ad hoc positioning system (APS) using AOA,” in IEEE INFOCOM 2003. Twenty-second Annual Joint Conference of the IEEE Computer and Communications Societies (IEEE Cat. No.03CH37428), vol. 3, pp. 1734-1743, July 2003.
10- F. Gustafsson and F. Gunnarsson, “Positioning using time-difference of arrival measurements,” in 2003 IEEE International Conference on Acoustics, Speech, and Signal Processing, 2003. Proceedings. (ICASSP '03), pp. VI-553, April 2003.
11- K. Dogançay and H. Hmam, “Optimal angular sensor separation for AOA localization,” Signal Processing, vol. 88, pp. 1248-1260, May 2008.
12- N. Bulusu, J. Heidemann, and D. Estrin, “GPS-less low-cost outdoor localization for very small devices,” IEEE Personal Communications, vol. 7, pp. 28-34, Oct. 2000.
13- D. Niculescu and B. Nath, “DV based positioning in ad hoc networks,” Telecommunication Systems, vol. 22, pp. 267-280, Jan. 2003.
14- R. Nagpal, H. Shrobe, and J. Bachrach, “Organizing a global coordinate system from local information on an ad hoc sensor network,” presented at the Proceedings of the 2nd international conference on Information processing in sensor networks, Palo Alto, CA, USA, vol.2634, pp:333-348, April 2003.
15- S. Yi and W. Ruml, “Improved MDS-based localization,” in IEEE INFOCOM 2004, vol. 4, pp. 2640-2651, March 2004
16- X. S. Zhou Yong, Ding Shifei, Zhang Lei, and A. Xin, “An Improved APIT Node Self-Localization Algorithm in WSN Based on Triangle-Center Scan,” Journal of Computer Research and Development, vol. 46, no.4 , pp. 566-574, April 2009.
17- A. H. Gandomi and A. H. Alavi, “Krill herd: A new bio-inspired optimization algorithm,” Communications in Nonlinear Science and Numerical Simulation, vol. 17, no. 1, pp. 4831-4845, Dec. 2012.
18- M. Dorigo and L. M. Gambardella, “Ant colony system: a cooperative learning approach to the traveling salesman problem,” IEEE Transactions on Evolutionary Computation, vol. 1, no.1 , pp. 53-66, April 1997.
19- A. Hadir, K. Zine-Dine, M. Bakhouya, and J. El Kafi, “An Enhanced Localization Approach for Three-Dimensional Wireless Sensor Networks,” in Advanced Intelligent Systems for Sustainable Development (AI2SD’2018), Cham, 2019, pp. 941-954.
20- V. Kanwar and A. Kumar, “DV-Hop-based range-free localization algorithm for wireless sensor network using runner-root optimization,” The Journal of Supercomputing, vol. 77, no. 1, pp. 3044-3061, July 2020.
21- Q.-w. Chai, S.-C. Chu, J.-S. Pan, P. Hu, and W.-m. Zheng, “A parallel WOA with two communication strategies applied in DV-Hop localization method,” EURASIP Journal on Wireless Communications and Networking, vol. 2020, pp. 50, Feb. 2020.
22- A. Dwivedi and P. R. Vamsi, “DV-HOP Based Hybrid Range-Free Localization Methods for Wireless Sensor Networks,” in Futuristic Trends in Network and Communication Technologies, Singapore, 2019, pp. 452-463.
23- Kaur, P. Kumar, and G. P. Gupta, “Nature Inspired Algorithm-Based Improved Variants of DV-Hop Algorithm for Randomly Deployed 2D and 3D Wireless Sensor Networks,” Wireless Personal Communications, vol. 101, pp. 567-582, July 2018.
24- F. Zhou and S. Chen, “DV-Hop node localization algorithm based on improved particle swarm optimization,” in Communications, Signal Processing, and Systems, vol.423, pp. 541-550, Aug. 2018.
25- Liu, S. Liu, W. Zhang, and D. Zhao, “The performance evaluation of hybrid localization algorithm in wireless sensor networks,” Mobile Networks and Applications, vol. 21, no.6 , pp. 994-1001, Dec. 2016.
26- F. Liu and G.-z. Feng, “Research on improved dv-hop localization algorithm based on rssi and feedback mechanism,” in Advances in Wireless Sensor Networks, Berlin, Heidelberg, 2015, pp. 144-154.
27- X. Yu and M. Hu, “Hop-count quantization ranging and hybrid cuckoo search optimized for dv-hop in wsns,” Wireless Personal Communications, vol. 108, pp. 2031-2046, Oct. 2019.
28- X. Lv, X. Sun, X. Zhou, and G. Xu, “DV-Hop-MSO based localization algorithm in wireless sensor networks,” in Advances in Wireless Sensor Networks, Berlin, Heidelberg, 2014, pp. 313-323.
29- Q. -g. Zhang and M. Cheng, “A node localization algorithm for wireless sensor network based on improved particle swarm optimization,” in Mechatronics and Automatic Control Systems, Cham, 2014, pp. 135-144.
30- H. Shi and L. Peng, “An improved dv-hop node localization algorithm combined with rssi ranging technology,” in Proceedings of the 5th International Conference on Electrical Engineering and Automatic Control, Berlin, Heidelberg, 2016, pp. 269-276.
31- J. Liu, Z. Wang, M. Yao, and Z. Qiu, “VN-APIT: virtual nodes-based range-free APIT localization scheme for WSN,” Wireless Networks, vol. 22, pp. 867-878, April 2016.
32- F. Shahzad, T. R. Sheltami, and E. M. Shakshuki, “Multi-objective optimization for a reliable localization scheme in wireless sensor networks,” Journal of Communications and Networks, vol. 18, no.5 , pp. 796-805, Dec. 2016.
33- X. Li, K. Wang, B. Liu, J. Xiao, and S. Han, “An improved range-free location algorithm for industrial wireless sensor networks," EURASIP Journal on Wireless Communications and Networking, vol. 2020, p. 81, April 2020.
34- S. Shen, L. Sun, Y. Dang, Z. Zou, and R. Wang, “Node localization based on improved pso and mobile nodes for environmental monitoring WSNs,” International Journal of Wireless Information Networks, vol. 25, pp. 470-479, Dec. 2018.
35- F. Zeng, W. Li, and X. Guo, “An improved dv-hop localization algorithm based on average hop and node distance optimization,” in 2018 2nd IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference (IMCEC), 2018, pp. 1336-1339.
36- F. Khelifi, A. Bradai, A. Benslimane, M. L. Kaddachi, and M. Atri, “Energy-Saving performance of an improved dv-hop localization algorithm for wireless sensor networks,” in GLOBECOM 2017 - 2017 IEEE Global Communications Conference, 2017, pp. 1-6. | ||
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