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Performance optimization of UAV-based IoT communications using a novel constrained gravitational search algorithm

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Abstract

In the recent years, unmanned aerial vehicles (UAVs) because of their ability to be used as aerial base stations for collecting data from IoT devices have attracted substantial interest in Internet of Things (IoT) systems. In this paper, a novel method has been proposed to increase the quality of the uplink IoT communications and to decrease the transmission power of IoT devices. For this purpose, to compute the UAVs’ trajectory, association of device-to-UAV and IoT transmission power, a novel objective function has been defined to optimize the link quality and energy consumption in the considered UAV-based IoT system. Then, for optimizing this objective function, a novel constrained version of gravitational search algorithm is proposed, which is an NP-hard problem. In this algorithm, to handle the constraints, a multiple constraint ranking method is used. Moreover, to calculate the value of the parameter of this method, a fuzzy logic controller is used to control the exploitation and exploration abilities and improve the performance of this algorithm. To evaluate the performance of the proposed method, simulations have been performed and the results were compared with those of the other methods. the experimental results show that an increase in system throughput and a decrease in the energy consumption of the considered UAV-based IoT system can be achieved simultaneously using the proposed optimization algorithm.

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References

  1. Gonzalez F, Tsourdos A (eds) (2018) UAV sensors for environmental monitoring. MDPI, Basel, Switzerland. https://doi.org/10.3390/books978-3-03842-754-4

  2. Everaerts J (2008) The use of unmanned aerial vehicles (UAVs) for remote sensing and mapping. Int Arch Photogramm Remote Sens Spat Inf Sci 2008(37):1187–1192

    Google Scholar 

  3. Angel Y et al. 2017 Uav-based hyperspectral remote sensing for precision agriculture: challenges and opportunities in AGU Fall Meeting Abstracts.

  4. Reinoso J et al (2018) Cartography for civil engineering projects: photogrammetry supported by unmanned aerial vehicles. Iran J Sci Technol Trans Civ Eng 42(1):91–96

    Article  Google Scholar 

  5. Pang Y et al. 2014 Efficient data collection for wireless rechargeable sensor clusters in harsh terrains using UAVs in global communications conference (GLOBECOM), IEEE.

  6. García CG et al (2019) A review of artificial intelligence in the Internet of Things. IJIMAI 5(4):9–20

    Article  Google Scholar 

  7. Xie L, Xu J, Zhang R 2018 Throughput maximization for UAV-enabled wireless powered communication networks-invited paper in 2018 IEEE 87th Vehicular Technology Conference (VTC Spring).

  8. Wu Q, Zeng Y, Zhang R (2018) Joint trajectory and communication design for multi-UAV enabled wireless networks. IEEE Trans Wireless Commun 17(3):2109–2121

    Article  Google Scholar 

  9. Fotouhi A, Ding M, Hassan M, 2017 Dynamic base station repositioning to improve spectral efficiency of drone small cells in a world of wireless, mobile and multimedia networks (WoWMoM), 2017 IEEE 18th International Symposium on. 2017. IEEE.

  10. Mozaffari M et al (2017) Mobile unmanned aerial vehicles (UAVs) for energy-efficient internet of things communications. IEEE Trans Wireless Commun 16(11):7574–7589

    Article  Google Scholar 

  11. Lobo FG, Goldberg DE, Pelikan M, 2000 Time complexity of genetic algorithms on exponentially scaled problems in proceedings of the 2nd annual conference on genetic and evolutionary computation. 2000.

  12. Nighot M, Ghatol A, Thakare V (2018) Self-organized hybrid wireless sensor network for finding randomly moving target in unknown environment. IJIMAI 5(1):16–28

    Article  Google Scholar 

  13. Li J et al. 2017 Path planning of UAV based on hierarchical genetic algorithm with optimized search region in control & automation (ICCA), 2017 13th IEEE International Conference on. 2017. IEEE.

  14. Ramirez-Atencia C et al (2017) Solving complex multi-UAV mission planning problems using multi-objective genetic algorithms. Soft Comput 21(17):4883–4900

    Article  Google Scholar 

  15. Shakhatreh H et al. 2017 Efficient 3d placement of a uav using particle swarm optimization in information and communication systems (ICICS), 2017 8th International Conference on. 2017. IEEE.

  16. Rashedi E, Nezamabadi-Pour H, Saryazdi S (2009) GSA: a gravitational search algorithm. Inf Sci 179(13):2232–2248

    Article  Google Scholar 

  17. Rashedi E, Nezamabadi-Pour H, Saryazdi S (2010) BGSA: binary gravitational search algorithm. Nat Comput 9(3):727–745

    Article  MathSciNet  Google Scholar 

  18. Han X, Xiong X, Duan F (2015) A new method for image segmentation based on BP neural network and gravitational search algorithm enhanced by cat chaotic mapping. Appl Intell 43(4):855–873

    Article  Google Scholar 

  19. Shirazi F, Rashedi E 2016 Detection of cancer tumors in mammography images using support vector machine and mixed gravitational search algorithm in Swarm intelligence and evolutionary computation (CSIEC), 2016 1st Conference on. 2016. IEEE.

  20. Das P et al (2017) An intelligent multi-robot path planning in a dynamic environment using improved gravitational search algorithm. Int J Autom Comput. https://doi.org/10.1007/s11633-016-1019-x

    Article  Google Scholar 

  21. Zahedi A, Parma F (2018) An energy-aware trust-based routing algorithm using gravitational search approach in wireless sensor networks. Peer-to-Peer Netw Appl 12(1):167–176

    Article  Google Scholar 

  22. Kherabadi HA, Mood SE, Javidi MM (2017) Mutation: a new operator in gravitational search algorithm using fuzzy controller. Cybern Inf Technol 17(1):72–86

    MathSciNet  Google Scholar 

  23. Ganesan T et al (2013) Swarm intelligence and gravitational search algorithm for multi-objective optimization of synthesis gas production. Appl Energy 103:368–374

    Article  Google Scholar 

  24. Al-Hourani A, Kandeepan S, Lardner S (2014) Optimal LAP altitude for maximum coverage. IEEE Wireless Commun Lett 3(6):569–572

    Article  Google Scholar 

  25. Rashedi E, Rashedi E, Nezamabadi-pour H (2018) A comprehensive survey on gravitational search algorithm. Swarm Evol Comput 41:141–158

    Article  Google Scholar 

  26. Rather SA, Shahid M, Bala PS 2019 A comprehensive survey on solving clustering and classification problems using gravitational search algorithm in 2019 IEEE 9th international conference on advanced computing (IACC). 2019. IEEE.

  27. Gu J et al (2020) Energy constrained completion time minimization in UAV-enabled Internet of Things. IEEE Int Things J 7(6):5491–5503

    Article  Google Scholar 

  28. Sun W et al (2020) A survey of using swarm intelligence algorithms in IoT. Sensors 20(5):1420

    Article  Google Scholar 

  29. Novlan TD, Dhillon HS, Andrews JG (2013) Analytical modeling of uplink cellular networks. IEEE Trans Wireless Commun 12(6):2669–2679

    Article  Google Scholar 

  30. Bor-Yaliniz RI, El-Keyi A, Yanikomeroglu H 2016 Efficient 3-D placement of an aerial base station in next generation cellular networks in communications (ICC), 2016 IEEE international conference on 2016. IEEE.

  31. Bertsekas DP (2014) Constrained optimization and Lagrange multiplier methods. Academic press, Cambridge

    MATH  Google Scholar 

  32. Mezura-Montes E, Coello CAC (2011) Constraint-handling in nature-inspired numerical optimization: past, present and future. Swarm Evol Comput 1(4):173–194

    Article  Google Scholar 

  33. Orvosh D, Davis L, 1994 Using a genetic algorithm to optimize problems with feasibility constraints in evolutionary computation, 1994. IEEE World Congress on Computational Intelligence., Proceedings of the First IEEE Conference on. 1994. IEEE.

  34. de Paula Garcia R et al (2017) A rank-based constraint handling technique for engineering design optimization problems solved by genetic algorithms. Comput Struct 187:77–87

    Article  Google Scholar 

  35. Sastry K, Goldberg D, Kendall G 2005 Genetic algorithms, in Search methodologies. 2005, Springer. p. 97-125.

  36. Castellanos CU et al. 2008 Performance of uplink fractional power control in UTRAN LTE in Vehicular Technology Conference, 2008. VTC Spring 2008. IEEE.

  37. Wan S et al (2019) Multi-dimensional data indexing and range query processing via Voronoi diagram for internet of things. Futur Gener Comput Syst 91:382–391

    Article  Google Scholar 

  38. Lin C-Y, Wu W-H (2004) Self-organizing adaptive penalty strategy in constrained genetic search. Struct Multidiscip Optim 26(6):417–428

    Article  Google Scholar 

  39. Friedman M (1937) The use of ranks to avoid the assumption of normality implicit in the analysis of variance. J Am Stat Assoc 32(200):675–701

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Computer Science, Yazd University, Yazd, Iran

    Sepehr Ebrahimi Mood

  2. Csiro, Sydney, Australia

    Ming Ding

  3. School of Electrical and Information Engineering, University of Sydney, Sydney, Australia

    Zihuai Lin

  4. Department of Computer Science, Shahid Bahonar University of Kerman, Kerman, Iran

    Mohammad Masoud Javidi

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  1. Sepehr Ebrahimi Mood
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  2. Ming Ding
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Correspondence to Mohammad Masoud Javidi.

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Ebrahimi Mood, S., Ding, M., Lin, Z. et al. Performance optimization of UAV-based IoT communications using a novel constrained gravitational search algorithm. Neural Comput & Applic 33, 15557–15568 (2021). https://doi.org/10.1007/s00521-021-06178-1

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  • DOI: https://doi.org/10.1007/s00521-021-06178-1

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