Putri Kevin, Dian viely


The movement assisted sensor deployment is the most common design issue in mobile design. Various models, assumptions, goals, and shortcomings are identified, and formulas are mentioned. A taxonomy of motion-assisted sensor deployment algorithms that captures fundamental variations between current solutions is introduced. Six approaches are identified; they use a particular theory to shift the node from the original position to the target position. The comparison of the self-deployment algorithm and classes is discussed in detail in this paper.


mobile sensor network, Deployment, Dense, anchor, WSN

Full Text:



S. S. Kulkarni, “TDMA Services for Sensor Networks,”Proc. 24th Int’l. Conf. Distrib. Comp. Sys. Wksps., Mar.2004, pp. 604–09.

W. Ye, J. Heidemann, and D. Estrin, “Medium Access Control with Coordinated Adaptive Sleeping for Wireless Sensor Networks,” IEEE/ACM Trans. Net., vol. 12, no. 3, June 2004, pp. 493–506.

A. El-Hoiydi, “Spatial TDMA and CSMA with Preamble Sampling for Low Power Ad Hoc Wireless Sensor Networks,” Proc. ISCC 2002, July 2002, pp. 685–92.

Ahmad, T., Li, X. J., & Seet, B. C. (2017). Parametric loop division for 3D localization in wireless sensor networks. Sensors, 17(7), 1697.

V. Rajendran, K. Obraczka, and J. J. Garcia-Luna-Aceves, “Energy-Efficient, Collision-Free Medium Access Control for Wireless Sensor Networks,” Proc. ACM SenSys ‘03, Los Angeles, CA, Nov. 2003, pp. 181–92.

L. Bao and J. J. Garcia-Luna-Aceves, “A New Approach to Channel Access Scheduling for Ad Hoc Networks,” 7th Ann. Int’l. Conf. Mobile Comp. and Net., 2001, pp. 210– 21.

Ahmad, Tanveer, Xue Jun Li, and Boon-Chong Seet. "A self-calibrated centroid localization algorithm for indoor ZigBee WSNs." In 2016 8th IEEE International Conference on Communication Software and Networks (ICCSN), pp. 455-461. IEEE, 2016.

Dawadi, B. R., Rawat, D. B., Joshi, S. R., & Keitsch, M. M. (2020). Towards energy efficiency and green network infrastructure deployment in Nepal using software defined IPv6 network paradigm. The Electronic Journal of Information Systems in Developing Countries, 86(1), e12114.

Ahmad, Tanveer, Xue Jun Li, and Boon-Chong Seet. "3D localization based on parametric loop division and subdivision surfaces for wireless sensor networks." In 2016 25th Wireless and Optical Communication Conference (WOCC), pp. 1-6. IEEE, 2016.

K. Jamieson, H. Balakrishnan, and Y. C. Tay, “Sift: A MAC Protocol for Event-Driven Wireless Sensor Networks,” MIT Lab. Comp. Sci., Tech. rep. 894, May 2003, available at

Y. C. Tay, K. Jamieson, and H. Balakrishnan, “CollisionMinimizing CSMA and Its Applications to Wireless Sensor Networks,” IEEE JSAC, vol. 22, no. 6, Aug. 2004, pp. 1048–57.

Ahmad, Tanveer, Xue Jun Li, and Boon-Chong Seet. "3D localization using social network analysis for wireless sensor networks." In 2018 IEEE 3rd international conference on communication and information systems (ICCIS), pp. 88-92. IEEE, 2018.

Chen, Hongyang, Pei Huang, Marcelo Martins, Hing Cheung So, and Kaoru Sezaki. "Novel centroid localization algorithm for three-dimensional wireless sensor networks." In 2008 4th International Conference on Wireless Communications, Networking and Mobile Computing, pp. 1-4. IEEE, 2008.

Chen, Kai, Zhong-hua Wang, Mei Lin, and Min Yu. "An improved DV-Hop localization algorithm for wireless sensor networks." (2010): 255-259.

zeng Wang, Ji, and Hongxu Jin. "Improvement on APIT localization algorithms for wireless sensor networks." In 2009 International Conference on Networks Security, Wireless Communications and Trusted Computing, vol. 1, pp. 719-723. IEEE, 2009.

Ahmad, Tanveer, Xue Jun Li, and Boon-Chong Seet. "Noise Reduction Scheme for Parametric Loop Division 3D Wireless Localization Algorithm Based on Extended Kalman Filtering." Journal of Sensor and Actuator Networks 8, no. 2 (2019): 24.

Ahmad, Tanveer, Xue Jun Li, and Boon-Chong Seet. "Fuzzy-Logic Based Localization for Mobile Sensor Networks." In 2019 2nd International Conference on Communication, Computing and Digital systems (CCODE), pp. 43-47. IEEE, 2019.

G. Lu, B. Krishnamachari, and C. S. Raghavendra, “An Adaptive Energy-Efficient and Low-Latency MAC for Data Gathering in Wireless Sensor Networks,” Proc. 18th Int’l. Parallel and Distrib. Processing Symp., Apr. 2004, p. 224.

Guerrero, Esteban, H. G. Xiong, Qiang Gao, Gabriel Cova, Ricardo Ricardo, and J. Estévez. "ADAL: A distributed range-free localization algorithm based on a mobile beacon for wireless sensor networks." In 2009 International Conference on Ultra Modern Telecommunications & Workshops, pp. 1-7. IEEE, 2009.

Ahmad, Tanveer, Xue Jun Li, Boon-Chong Seet, and Juan-Carlos Cano. "Social Network Analysis Based Localization Technique with Clustered Closeness Centrality for 3D Wireless Sensor Networks." Electronics 9, no. 5 (2020): 738.

hakila, R., and B. Paramasivan. "An improved range based localization using Whale Optimization Algorithm in underwater wireless sensor network." Journal of Ambient Intelligence and Humanized Computing (2020): 1-11.

Ahmad, T. (2019). 3D Localization Techniques for Wireless Sensor Networks (Doctoral dissertation, Auckland University of Technology).

R. Niu and P. Varshney, “Target location estimation in wireless sensor networks using binary data,” in Proceedings of the 38th International Conference on Information Sciences and Systems, pp. 17–19, Princeton, NJ, USA, March 2004.

Ahmad, T. (2019). 3D Localization Techniques for Wireless Sensor Networks (Doctoral dissertation, Auckland University of Technology).

M. P. Michaelides and C. G. Panayiotou, “SNAP: fault tolerant event location estimation in sensor networks using binary data,” IEEE Transactions on Computers, vol. 58, no. 9, pp. 1185– 1197, 2009.

Ahmad, T., Li, X. J., Wenchao, J., & Ghaffar, A. (2020, September). Frugal Sensing: A Novel approach of Mobile Sensor Network Localization based on Fuzzy-Logic. In Proceedings of the ACM MobiArch 2020 The 15th Workshop on Mobility in the Evolving Internet Architecture (pp. 8-15).

K. Lu, X. Xiang, D. Zhang, R. Mao, and Y. Feng, “Localization algorithm based on maximum a posteriori in wireless sensor networks,” International Journal of Distributed Sensor Networks, vol. 2012, Article ID 260302, 7 pages, 2012.

L. Cheng, C. D. Wu, Y. Z. Zhang, and Y. Wang, “Indoor robot localization based on wireless sensor networks,” IEEE Transactions on Consumer Electronics, vol. 57, no. 3, pp. 1099– 1104, 2011.

Y. Wang, X. Wang, D. Wang, and D. P. Agrawal, “Range-free localization using expected hop progress in wireless sensor networks,” IEEE Transactions on Parallel and Distributed Systems, vol. 20, no. 10, pp. 1540–1552, 2009.

H. Xu, Y. Tu, W. Xiao, Y. Mao, and T. Shen, “An archimedes curve-based mobile anchor node localization algorithm in wireless sensor networks,” in Proceedings of the 8th World Congress on Intelligent Control and Automation (WCICA ’10), pp. 6993–6997, Jinan, China, July 2010.

J. Lee, W. Chung, and E. Kim, “Robust DV-Hop algorithm for localization in wireless sensor network,” in Proceedings of the International Conference on Control, Automation and Systems, pp. 2506–2509, Gyeonggi-do, South Korea, October 2010.

Ware, R.; Lad, F. Approximating the Distribution for Sum of Product of Normal Variables; Research report; the Mathematics and Statistics department at Canterbury University: Christchurch, New Zealand, 2003.

Wen, C.-Y.; Hsiao, Y.-C. Decentralized anchor-free localization for wireless ad-hoc sensor networks. In Proceedings of IEEE International Conference on Systems, Man, and Cybernetics, Singapore, October 2008; pp. 2777-2785.

Roberts, G.; Gelman, A.; Gilks, W. Weak Convergence and Optimal Scaling of Random Walk Metropolis Algorithms. Technical Report; University of Cambridge: Cambridge, UK, 1994.

Chan, Fu and Chih-yu “Adaptive AoA/ToA localziation using fuzzy particle for mobile WSNs” in 2011 IEEE 73rd VTC Spring 2011.

Liu, Q.; Ihler, A. T.; Smyth, P. Particle filtered MCMC-MLE with connections to contrastive divergence. In Proceedings of the 27 th International Conference on Machine Learning, Haifa, Israel, June 2010; pp. 1-8.

Canovas, J. P.; LeBlanc, K.; Saffiotti, A. Robust multi-robot object localisation using fuzzy logic. In Proceedings of 2004 Int. Robocup Symposium, Lisbon, Portugal, July 2004; pp. 247-261.

Liu, C.; Wu, K.; He, T. Sensor localization with ring overlapping based on comparison of received signal strength indicator. In Proceedings of IEEE Mobile Ad-hoc and Sensor Systems (MASS’04), Fort Lauderdale, FL, USA, October 2004; pp. 516-518.

Doucet, A.; de Freitas, N.; Gordon, N. Sequential Monte Carlo Methods in Practice; Springer-Verlag: New York, NY, USA, 2001.

Chib, S.; Greenberg, E. Understanding the Metropolis-Hastings algorithm. Amer. Statist. 1995, 49, 327-335.

Chintalapudi, K.K.; Dhariwal, A.; Govindan, R.; Sukhatme, G. Ad-hoc localization using ranging and sectoring. In Proceedings of INFOCOM, Hong Kong, China, 7–11 March 2004; pp. 2662-2672.

Ihler, A.T.; Fisher, J.W.; Moses, R.L.; Willsky, A.S. Nonparametric belief propagation for self-localization of sensor networks. IEEE J. Sel. Areas Commun. 2005,

Wu, Jiawei, Jinming Yu, Aijun Ou, Yiming Wu, and Wujun Xu. "RCDV-Hop Localization Algorithm for WSN." In 2012 8th International Conference on Wireless Communications, Networking and Mobile Computing, pp. 1-4. IEEE, 2012.

Chen, W.; Mei, T.; Meng, M. Q.; Liang, H.; Liu, Y.; Li, Y.; Li, S. Localization algorithm based on a spring model (LASM) for large scale wireless sensor networks. Sensors 2008, 3, 1797-1818.

Savvides, A.; Park, H.; Srivastava, M. The bits and flops of the N-hop multilateration primitive for node localization problems. In Proceedings of the First ACM International Workshop on Sensor Networks and Applications, Atlanta, GA, USA, September 2002; pp. 112-121.

Savvides, A.; Han, C.-C.; Srivastava, M. B. Dynamic fine-grained localization in ad-hoc networks of sensors. In Proceedings of the 7th annual international conference on Mobile computing and networking, Rome, Italy, 16-21 July 2001; pp. 166-179.



  • There are currently no refbacks.

Copyright (c) 2021 International Journal of Advanced Research in Computer Science