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Distributed self-deployment of mobile wireless 3D robotic sensor networks for complete sensing coverage and forming specific shapes

Published online by Cambridge University Press:  26 April 2017

Vali Nazarzehi and
Andrey V. Savkin
Vali Nazarzehi*
Affiliation:
School of Electrical Engineering and Telecommunications, The University of New South Wales, Sydney, NSW 2052, Australia. E-mail: a.savkin@unsw.edu.au
Andrey V. Savkin
Affiliation:
School of Electrical Engineering and Telecommunications, The University of New South Wales, Sydney, NSW 2052, Australia. E-mail: a.savkin@unsw.edu.au
*
*Corresponding author. E-mail: valinazarzei2004@gmail.com
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Summary

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This paper addresses a problem of complete sensing coverage in 3D environments. We propose a distributed random algorithm to drive mobile robotic sensors on the vertices of a truncated octahedral grid for complete sensing coverage of a bounded 3D area. Furthermore, we develop a distributed algorithm for the self-deployment of mobile sensors to form a desired 3D geometric shape on the vertices of the truncated octahedral grid. These algorithms are developed based on some consensus rules that only rely on local information. The proposed algorithms utilize 3D grids for the coverage task. Several simulations are conducted to illustrate the validity of the proposed distributed sensing coverage and formation building algorithms for a mobile robotic sensor network. Also, we give mathematically rigorous proof of the convergence with probability 1 of our proposed algorithms.

Keywords

Mobile robotic sensor networksWireless sensor networksDistributed self-deploymentMobile robotsSensing coverageCoverage control

Information

Type
Articles
Information
Robotica , Volume 36 , Issue 1 , January 2018, pp. 1 - 18
Copyright
Copyright © Cambridge University Press 2017 

Footnotes

†

This work was supported by the Australian Research Council.

References

1. Senouci, M. R., Mellouk, A., Assnoune, K. and Bouhidel, F., “Movement-assisted sensor deployment algorithms: A survey and taxonomy,” Commun. Surv. Tutorials 17 (4), 2493–2510 (2015).Google Scholar
2. Sangwan, A. and Singh, R. P., “Survey on coverage problems in wireless sensor networks,” Wireless Personal Commun. 80 (4), 1475–1500 (2015).CrossRefGoogle Scholar
3. Pompili, D., Melodia, T. and Akyildiz, I. F., “Three-dimensional and two-dimensional deployment analysis for underwater acoustic sensor networks,” Ad Hoc Netw. 7 (4), 778–790 (2009).CrossRefGoogle Scholar
4. Akkaya, K. and Newell, A., “Self-deployment of sensors for maximized coverage in underwater acoustic sensor networks,” Comput. Commun. 32 (7), 1233–1244 (2009).CrossRefGoogle Scholar
5. Jalalkamali, P., Distributed Tracking and Information-Driven Control for Mobile Sensor Networks (Dartmouth College, Hanover, USA, 2013).Google Scholar
6. Zhang, S., Yu, J., Zhang, A., Yang, L. and Shu, Y., “Marine vehicle sensor network architecture and protocol designs for ocean observation,” Sensors 12 (1), 373–390 (2012).Google Scholar
7. Khedo, K. K., Perseedoss, R., and Mungur, A. et al., “A wireless sensor network air pollution monitoring system,” preprint, arXiv:1005.1737, 2010.Google Scholar
8. Mansour, S., Nasser, N., Karim, L. and Ali, A., “Wireless Sensor Network-Based Air Quality Monitoring System,” Proceedings of the International Conference on Computing, Networking and Communications (ICNC), IEEE, Hawaii, USA (2014) pp. 545–550.Google Scholar
9. Liu, J., Wang, Z., Zuba, M., Peng, Z., Cui, J.-H. and Zhou, S., “Da-sync: A doppler-assisted time-synchronization scheme for mobile underwater sensor networks,” IEEE Trans. Mobile Comput. 13 (3), 582–595 (Mar. 2014).CrossRefGoogle Scholar
10. Partan, J., Kurose, J. and Levine, B. N., “A survey of practical issues in underwater networks,” ACM SIGMOBILE Mobile Comput. Commun. Rev. 11 (4), 23–33 (2007).CrossRefGoogle Scholar
11. Wu, F.-J., Kao, Y.-F. and Tseng, Y.-C., “From wireless sensor networks towards cyber physical systems,” Pervasive Mobile Comput. 7 (4), 397–413 (2011).CrossRefGoogle Scholar
12. Zhu, C., Shu, L., Hara, T., Wang, L., Nishio, S. and Yang, L. T., “A survey on communication and data management issues in mobile sensor networks,” Wireless Commun. Mobile Comput. 14 (1), 19–36 (2014).Google Scholar
13. Atkar, P. N., Greenfield, A., Conner, D. C., Choset, H. and Rizzi, A. A., “Uniform coverage of automotive surface patches,” Int. J. Robot. Res. 24 (11), 883–898 (2005).CrossRefGoogle Scholar
14. Cortes, J. and Martinez, S., Distributed Control of Robotic Networks (Princeton University Press, Princeton, USA, 2009).Google Scholar
15. Cheng, T. M., Savkin, A. V. and Javed, F., “Decentralized control of a group of mobile robots for deployment in sweep coverage,” Robot. Auton. Syst. 59 (7), 497–507 (2011).Google Scholar
16. Cortes, J., Martinez, S., Karatas, T. and Bullo, F., “Coverage Control for Mobile Sensing Networks,” Proceedings of the IEEE International Conference on Robotics and Automation, vol. 2., IEEE, Washington D.C, USA (2002) pp. 1327–1332.Google Scholar
17. Savkin, A. V., Cheng, T. M., Li, Z., Javed, F., Xi, Z., Matveev, A. S. and Nguyen, H., Decentralized Coverage Control Problems For Mobile Robotic Sensor and Actuator Networks (John Wiley & Sons, New Jersey, USA, 2015).Google Scholar
18. Bretl, T. and Hutchinson, S., “Robust Coverage by a Mobile Robot of a Planar Workspace,” Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), IEEE, Karlsruhe, Sweden (2013) pp. 4582–4587.Google Scholar
19. Cheng, T. M. and Savkin, A. V., “Self-deployment of mobile robotic sensor networks for multilevel barrier coverage,” Robotica 30 (04), 661–669 (2012).CrossRefGoogle Scholar
20. Savkin, A. V. and Javed, F., “A Method for Decentralized Self-Deployment of a Mobile Sensor Network with Given Regular Geometric Patterns,” Proceedings of the International Conference on Intelligent Sensors, Sensor Networks and Information Processing, IEEE, Adelaide, Australia (2011) pp. 371–376.Google Scholar
21. Savkin, A. V., Javed, F. and Matveev, A. S., “Optimal distributed blanket coverage self-deployment of mobile wireless sensor networks,” IEEE Commun. Lett. 16 (6), 949–951 (2012).Google Scholar
22. Cheng, T. M. and Savkin, A. V., “Decentralized control for mobile robotic sensor network self-deployment: Barrier and sweep coverage problems,” Robotica 29 (02), 283–294 (2011).Google Scholar
23. Dong, H., Zhang, K. and Zhu, L., “An Algorithm of 3d Directional Sensor Network Coverage Enhancing Based on Artificial Fish-Swarm Optimization,” Proceedings of the International Workshop on Microwave and Millimeter Wave Circuits and System Technology (MMWCST), IEEE, Chengdu, China (2012) pp. 1–4.Google Scholar
24. Huang, C.-F., Tseng, Y.-C. and Lo, L.-C., “The coverage problem in three-dimensional wireless sensor networks,” J. Interconnect. Netw. 8 (03), 209–227 (2007).Google Scholar
25. Liu, B., Dousse, O., Nain, P. and Towsley, D., “Dynamic coverage of mobile sensor networks,” IEEE Trans. Parallel Distrib. Syst. 24 (2), 301–311 (2013).Google Scholar
26. Watfa, M. K. and Commuri, S., “The 3-Dimensional Wireless Sensor Network Coverage Problem,” Proceedings of the 2006 IEEE International Conference on Networking, Sensing and Control, IEEE, Florida, USA (2006) pp. 856–861.Google Scholar
27. Pompili, D., Melodia, T. and Akyildiz, I. F., “Deployment Analysis in Underwater Acoustic Wireless Sensor Networks,” Proceedings of the 1st ACM International Workshop on Underwater Networks, ACM, Los Angeles, USA (2006) pp. 48–55.Google Scholar
28. Boufares, N., Khoufi, I., Minet, P., Saidane, L. and Ben Saied, Y., “Three Dimensional Mobile Wireless Sensor Networks Redeployment Based on Virtual Forces,” Proceedings of the International Wireless Communications and Mobile Computing Conference (IWCMC), IEEE, Dubrovnik, Croatia (2015) pp. 563–568.Google Scholar
29. Stirling, T., Wischmann, S. and Floreano, D., “Energy-efficient indoor search by swarms of simulated flying robots without global information,” Swarm Intell. 4 (2), 117–143 (2010).CrossRefGoogle Scholar
30. Feng, L., Qiu, T., Sun, Z., Xia, F. and Zhou, Y., “A coverage strategy for wireless sensor networks in a three–dimensional environment,” Int. J. Ad Hoc Ubiquitous Comput. 15 (1), 83–94 (2014).Google Scholar
31. Zhu, C., Zheng, C., Shu, L. and Han, G., “A survey on coverage and connectivity issues in wireless sensor networks,” J. Netw. Comput. Appl. 35 (2), 619–632 (2012).Google Scholar
32. Nazarzehi, V., Savkin, A. V. and Baranzadeh, A., “Distributed 3d dynamic search coverage for mobile wireless sensor networks,” IEEE Commun. Lett. 19 (4), 633–636 (Apr. 2015).CrossRefGoogle Scholar
33. Nazarzehi, V. and Savkin, A. V., “Decentralized Control of Mobile Three-Dimensional Sensor Networks for Complete Coverage Self-Deployment and Forming Specific Shapes,’ Proceedings of the IEEE Multi-Conference on Systems and Control (MSC), IEEE, Sydney, Australia (2015) pp. 301–306.Google Scholar
34. Jeremic, A. and Nehorai, A., “Design of chemical sensor arrays for monitoring disposal sites on the ocean floor,” IEEE J. Ocean. Eng. 23 (4), 334–343 (1998).Google Scholar
35. Borhaug, E., Pavlov, A. and Pettersen, K. Y., “Straight Line Path Following for Formations of Underactuated Underwater Vehicles,” Proceedings of the IEEE Conference on Decision and Control, New Orleans, USA (2007) pp. 2905–2912.Google Scholar
36. Heidemann, J., Stojanovic, M. and Zorzi, M., “Underwater sensor networks: Applications, advances and challenges,” Phil. Trans. R. Soc. A: Math. Phys. Eng. Sci. 370 (1958), 158–175 (2012).Google Scholar
37. Alam, S. N. and Haas, Z. J., “Coverage and connectivity in three-dimensional networks with random node deployment,” Ad Hoc Netw. 34, 157–169 (2014).Google Scholar
38. Al-Turjman, F. M., Hassanein, H. S. and Ibnkahla, M. A., “Efficient deployment of wireless sensor networks targeting environment monitoring applications,” Comput. Commun. 36 (2), 135–148 (2013).Google Scholar
39. Kottege, N. and Zimmer, U. R., “Underwater acoustic localization for small submersibles,” J. Field Robot. 28 (1), 40–69 (2011).CrossRefGoogle Scholar
40. Alam, S. and Haas, Z. J., “Coverage and Connectivity in Three-Dimensional Networks,” Proceedings of the 12th Annual International Conference on Mobile Computing and Networking, ACM, Los Angeles, USA (2006) pp. 346–357.Google Scholar
41. Zhao, Y., Li, B., Qin, J., Gao, H. and Karimi, H. R., “Consensus and synchronization of nonlinear systems based on a novel fuzzy model,” IEEE Trans. Cybern. 43 (6), 2157–2169 (2013).Google Scholar
42. Qing, X., Karimi, H. R., Niu, Y. and Wang, X., “Decentralized unscented {K}alman filter based on a consensus algorithm for multi-area dynamic state estimation in power systems,” Int. J. Electr. Power Energy Syst. 65, 26–33 (2015).Google Scholar
43. Karimi, H. R. and Gao, H., “New delay-dependent exponential synchronization for uncertain neural networks with mixed time delays,” IEEE Trans. Syst. Man Cybern. Part B: Cybern. 40 (1), 173–185 (2010).Google Scholar
44. Alam, S. and Haas, Z. J., “Coverage and connectivity in three-dimensional underwater sensor networks,” Wireless Commun. Mobile Comput. 8 (8), 995–1009 (2008).Google Scholar
45. Jadbabaie, A., Lin, J. and Morse, A. S., “Coordination of groups of mobile autonomous agents using nearest neighbor rules,” IEEE Trans. Autom. Control 48 (6), 988–1001 (2003).Google Scholar
46. Savkin, A. V., Matveev, A. S., Hoy, M. and Wang, C., Safe Robot Navigation Among Moving and Steady Obstacles (Elsevier, Butter worth-Heinemann, UK, 2015).Google Scholar
47. Hoy, M., Matveev, A. S. and Savkin, A. V., “Algorithms for collision-free navigation of mobile robots in complex cluttered environments: A survey,” Robotica 33 (03), 463–497 (2015).Google Scholar