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Optimization of Wireless Sensor Network and UAV Data Acquisition

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Abstract

This paper deals with selection of sensor network communication topology and the use of Unmanned Aerial Vehicles (UAVs) for data gathering. The topology consists of a set of cluster heads that communicate with the UAV. In conventional wireless sensor networks Low Energy Adaptive Clustering Hierarchy (LEACH) is commonly used to select cluster heads in order to conserve energy. Energy conservation is far more challenging for large scale deployments. Particle Swarm Optimization (PSO) is proposed as an optimization method to find the optimal topology in order to reduce the energy consumption, Bit Error Rate (BER), and UAV travel time. PSO is compared to LEACH using a simulation case and the results show that PSO outperforms LEACH in terms of energy consumption and BER, while the UAV travel time is similar. The numerical results further illustrate that the performance gap between them increases with the number of cluster head nodes. Because of reduced energy consumption, network life time can be significantly extended while increasing the amount of data received from the entire network. By considering the wind effect into the PSO scheme, it is shown that this has an impact on the traveling time for the UAV but BER and energy consumption are not significantly increased.

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References

  1. Purvis, K., Åström, K., Kammash, M.: Estimation and optimal configurations for localization using cooperative UAVs. IEEE Trans. on Control System Technology 16, 947–958 (2008)

    Article  Google Scholar 

  2. Frew, E.W., Brown, T.X.: Networking issues for small unmmaned aircraft systems. J. of Int. and Robotic Sys. 54, 21–37 (2008)

    Article  Google Scholar 

  3. Ruz, J.J., Arevalo, O., Pajares, G., De la Cruz, J.M.: UAV trajectory planning for static and dynamic environments. InTech, ch 27, 581–600 (2009)

    Google Scholar 

  4. Flushing, E.F., Gambardella, L., Di Caro, G.: Search and rescue using mixed swarms of heterogeneous agents: Modeling, simulation, and planning, IDSIA/USI-SUPSI, Tech. Rep. (2012)

  5. Brown, T., Argrow, B., Dixon, C., Doshi, S., Thekkekunnel, R., Henkel, D.: Ad hoc UAV-ground network (AUGNet). In: Proceedings of the AIAA 3rd Unmanned Unlimited Technical Conference, Workshop and Exhibit (2004)

  6. Ho, D.T., Grøtli, E.I., Shimamoto, S., Johansen, T.A.: Optimal relay path selection and cooperative communication protocol for a swarm of UAVs. In: Proceedings of the 3rd International Workshop on Wireless Networking & Control for Unmanned Autonomous Vehicles: Architectures, pp 1585–1590 (2012)

  7. Sujit, P.B., Lucani, D.E., Sousa, J.B.: Bridging cooperative sensing and route planning of autonomous vehicles. IEEE J. Sel. Areas Commun. 30, 912–922 (2012)

    Article  Google Scholar 

  8. Isaacs, J., Venkateswaran, S., Hespanha, J., Madhow, U., Burman, J., Pham, T.: Multiple event localization in a sparse acoustic sensor network using UAVs as data mules. In: Proceedings of the 3rd International Workshop on Wireless Networking & Control for Unmanned Autonomous Vehicles: Architectures, Protocols and Applications, pp 1562–1567 (2012)

  9. Wang, F., Liu, J.: Networked wireless sensor data collection: Issues, challenges, and approaches. IEEE Commun. Surv. Tutorials 13, 673–687 (2011)

    Article  Google Scholar 

  10. He, L., Tao, J., Pan, J., Xu, P.: Adaptive mobility-assisted data collection in wireless sensor networks. In: Proceedings of the International Conference on Wireless Communications and Signal Processing, pp 1–6 (2011)

  11. Liang, W., Luo, J., Xu, X.: Prolonging network lifetime via a controlled mobile sink in wireless sensor networks, in Proc. of the 2010 IEEE Global Telecommunication Conference (GLOBECOM), pp 1–6 (2010)

  12. Tong, L., Zhao, Q., Adireddy, S.: Sensor networks with mobile agents. In: Proceedings of the 2003 IEEE Military Communications Conference, Vol. 1, pp 688–693 (2003)

  13. Ho, D.T., Shimamoto, S.: Highly reliable communication protocol for WSN-UAV system employing TDMA and PFS scheme. In: Proceedings of the 2nd International Workshop on Wireless Networking & Control for Unmanned Autonomous Vehicles: Architectures, pp 1320–1324 (2011)

  14. Ho, D.T., Grøtli, E.I., Johansen, T.A.: Heuristic algorithm and cooperative relay for energy efficient data collection with a UAV and WSN. In: Proceedings of the IEEE International Conference on Computing, Management & Telecommunications, pp 346–351 (2013)

  15. Grøtli, E.I., Johansen, T.A.: Path planning for UAVs under communication constraints using SPLAT! and MILP. J. Intell. Robot. Syst. 65(1-4), 265–282 (2012)

    Article  Google Scholar 

  16. Grøtli, E.I., Johansen, T.A.: Path- and data transmission planning for cooperating UAVs in delay tolerant network. In: Proceedings of the 3rd International Workshop on Wireless Networking and Control for Unmanned Autonomous Vehicles: Architectures, Protocols and Applications, pp 1568–1573 (2012)

  17. Grancharova, A., Grøtli, E.I., Johansen, T.A.: Distributed MPC-based path planning for UAVs under radio communication path loss constraints. In: Proceedings of the IFAC Conference on Embedded Systems, Computational Intelligence and Telematics in Control, pp 254–259 (2012)

  18. Zhang, K., Collins Jr, E.G., Barbu, A.: An efficient stochastic clustering auction for heterogeneous robotic collaborative teams. J. Intell. Robot. Syst. 72(3-4), 541–558 (2013)

    Article  Google Scholar 

  19. Zhang, K., Collins Jr, E.G., Shi, D.: Centralized and distributed task allocation in multi-robot teams via a stochastic clustering auction. ACM Trans. Auton. Adapt. Syst. (TAAS) 7(2), 21 (2012)

    Google Scholar 

  20. Tsitsiklis, J.: Efficient algorithm for globally optimal trajectories. IEEE Trans. Autom. Contr. 40(9), 1528–1538 (1995)

    Article  MATH  MathSciNet  Google Scholar 

  21. Zhao, Q., Tong, L.: Quality of service specific information retrieval for densely deployed sensor network. In: Proceedings of the 2003 IEEE Military Communications Conference, Vol. 1, pp 591–596 (2003)

  22. Salari, M., Naji-Azimi, Z.: An integer programming-based local search for the covering salesman problem. J. Comput. Oper. Res., 2594–2602 (2012)

  23. Shi, S., X., L., Gu, X.: An energy-efficiency optimized LEACH-C for wireless sensor networks. In: Proceedings of the International ICST Conference on Communication and Networking in China (CHINACOM), pp 487–492 (2003)

  24. Heinzelman, W., Chandrakasan, A., Balakrishnan, H.: An application-specific protocol architecture for wireless microsensor networks. IEEE Trans. Wirel. Commun. 1, 660–670 (2002)

    Article  Google Scholar 

  25. Ghaffarkhah, A., Mostofi, Y.: Communication-aware motion planning in mobile networks. IEEE Trans. Autom. Control 56(10), 2478–2485 (2011)

    Article  MathSciNet  Google Scholar 

  26. Mostofi, Y.: Decentralized communication-aware motion planning in mobile networks: An information gain approach. J. of Int. Robot. Sys. 56, 233256 (2009)

    Google Scholar 

  27. Latiff, N.M.A., Tsimenidis, C.C., Sharif, B.S.: Energy-aware clustering for wireless sensor networks using particle swarm optimization. In: Proceedings of the IEEE International Symposium on Personal, Indoor and Mobile Radio Communications, pp 1–5 (2007)

  28. Singh, B., Lobiyal, D.: A novel energy-aware cluster head selection based on particle swarm optimization for wireless sensor networks. J. Human-centric Comput. Inform. Sci. 2, 1–18 (2012)

    Article  Google Scholar 

  29. Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proceedings of IEEE International Conference on Neural Networks, Vol. 4, pp 1942–1948 (1995)

  30. Parsopoulos, K.E., Vrahatis, M.N.: Recent approaches to global optimization problems through particle swarm optimization. Nat. Comput. 1(2-3), 235–306 (2002)

    Article  MATH  MathSciNet  Google Scholar 

  31. Trelea, I.C.: The particle swarm optimization algorithm: convergence analysis and parameter selection. Inf. Process. Lett. 85, 317–325 (2003)

    Article  MATH  MathSciNet  Google Scholar 

  32. Shi, Y., Eberhart, R.C.: Parameter selection in particle swarm optimization. In: Proceedings of the International Conference on Evolutionary Programming, pp 591–600 (1998)

  33. Pedersen, M.E.H., Chipperfield, A.J.: Simplifying particle swarm optimization. Appl. Soft Comput. 10, 618–628 (2010)

    Article  Google Scholar 

  34. Kuhn, H.W.: The Hungarian method for the assignment problem. Nav. Res. Logist. Quarterly 2, 83–97 (1955)

    Article  Google Scholar 

  35. Goldsmith, A. Cambridge University Press, Cambridge (2005)

  36. Ho, D.T., Park, J., Shimamoto, S., Kitaori, J.: Performance evaluation of multi hop relay network for oceanic air traffic control communication. IEICE Transactions on Communications E94-B(1), 86–96 (2011)

    Article  Google Scholar 

  37. Ho, D.T., Grøtli, E., Sujit, P., Johansen, T., Borges Sousa, J.: Cluster-based communication topology selection and UAV path planning in wireless sensor networks. In: Proceedings of International Conference on Unmanned Aircraft Systems (ICUAS), pp 59–68 (2013)

  38. Savla, K., Frazzoli, E., Bullo, F.: Traveling salesperson problems for the Dubins vehicle. IEEE Trans. Autom. Control. 53, 1378–1391 (2008)

    Article  MathSciNet  Google Scholar 

  39. Dubins, L.E.: On curves of minimal length with a constraint on average curvature, and with prescribed initial and terminal positions and tangents (1957)

  40. Sujit, P., Saripalli, S., Sousa, J.: Unmanned aerial vehicle path following: A survey and analysis of algorithms for fixed-wing unmanned aerial vehicless. Control. Syst., IEEE 34(1), 42–59 (2014)

    Article  MathSciNet  Google Scholar 

  41. Nelson, D.R., Barber, D.B., McLain, T.W., Beard, R.W.: Vector field path following for miniature air vehicles. IEEE Trans. Robot. 23(3), 519–529 (2007)

    Article  Google Scholar 

  42. Hovstein, V.E., Sægrov, A., Johansen, T.A.: Experiences with coastal and maritime UAS BLOS operation with phased-array payload data link. In: Proceedings of International Conference on Unmanned Aircraft Systems (ICUAS) (2014)

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Ho, DT., Grøtli, E.I., Sujit, P.B. et al. Optimization of Wireless Sensor Network and UAV Data Acquisition. J Intell Robot Syst 78, 159–179 (2015). https://doi.org/10.1007/s10846-015-0175-5

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  • DOI: https://doi.org/10.1007/s10846-015-0175-5

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