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Multi-objective NSGA-II optimization framework for UAV path planning in an UAV-assisted WSN

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Abstract

The recent technological advancements such as IoT-enabled sensor nodes, Global Positioning System, Wi-Fi transceivers, and lightweight lithium-ion batteries enable the use of Unmanned Aerial Vehicles (UAV) for data collection in wireless sensor networks. In a UAV-assisted wireless sensor network (UAV-WSN), the sensor nodes are installed at the ground and a UAV works as the sink node. The UAV-based sink flies over the sensed region and receives the data packets of surrounding ground nodes. A UAV-WSN offers improved data collection efficiency as the UAV-based sink avoids the ground obstacles and establishes line-of-sight communication with the ground sensor nodes. However, the UAV’s flight trajectory needs to be optimized to achieve minimized UAV energy consumption during flight operation and minimized node energy consumption in data transmission. This paper presents a hybrid data routing protocol for UAV-WSN that considers optimized planning of the UAV’s flight trajectory in parallel with energy-efficient data communication amid ground sensor nodes and the UAV. The presented scheme utilizes multi-objective NSGA-II optimization heuristics to optimize UAV’s flight trajectory. The developed NSGA-II model evolves into an optimal UAV flight trajectory that simultaneously achieves the objectives of minimized UAV energy consumption, minimized node energy consumption, and maximized average RSSI. A maximized RSSI further brings about a significant increase in network throughput rate. Simulation results depict that the proposed UAV-WSN scheme achieves improved network lifetime and network throughput rate compared to other state-of-the-art protocols.

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References

  1. Singh MK, Amin SI (2020) Energy efficient wireless sensor network using optimum hops and virtual MIMO technique. SN Appl Sci. https://doi.org/10.1007/s42452-020-03360-3

    Article  Google Scholar 

  2. Moussa N, Hamidi-Alaoui Z, El Belrhiti A, El Alaoui, (2020) ECRP: an energy-aware cluster-based routing protocol for wireless sensor networks. Wireless Netw 26(4):2915–2928. https://doi.org/10.1007/s11276-019-02247-5

    Article  Google Scholar 

  3. Heinzelman WR, Chandrakasan A, & Balakrishnan H (2000) Energy-efficient communication protocol for wireless microsensor networks, In: Proceedings of the 33rd annual Hawaii international Conference on system sciences, IEEE, pp 10

  4. Oubbati OS, Lakas A, Zhou F, Güneş M, Lagraa N, Yagoubi MB (2017) Intelligent UAV-Assisted routing protocol for urban VANETs. Comput Commun 107:93–111

    Article  Google Scholar 

  5. Barrado C, Meseguer-Pallarés R, Lopez J, Pastor E, Santamaria E, Royo P (2010) Wildfire monitoring using a mixed air-ground mobile network. IEEE Pervasive Comput 9:24–32

    Article  Google Scholar 

  6. Lottes P, Khanna R, Pfeifer J, Siegwart R, Stachniss C (2017) UAV-Based crop and weed classification for smart farming, In: Proceedings of the 2017 IEEE international Conference on robotics and automation (ICRA), Singapore, pp 3024–3031

  7. Choudhary A, Nizamuddin M, Singh MK et al (2019) Energy budget based multiple attribute decision making (EB-MADM) algorithm for cooperative clustering in wireless body area networks. J Electr Eng Technol 14:421–433. https://doi.org/10.1007/s42835-018-00006-8

    Article  Google Scholar 

  8. Smaragdakis G, Matta I, & Bestavros A (2004) SEP: A stable election protocol for clustered heterogeneous wireless sensor networks, In: Second international workshop on sensor and actor network protocols and applications (SANPA 2004), Vol 3, Boston, MA

  9. Choudhary A, Nizamuddin M, Sachan VK (2020) A hybrid fuzzy-genetic algorithm for performance optimization of cyber physical wireless body area networks. Int J Fuzzy Syst 22:548–569. https://doi.org/10.1007/s40815-019-00751-6

    Article  Google Scholar 

  10. Singh MK, Amin SI, Choudhary A (2021) Genetic algorithm based sink mobility for energy efficient data routing in wireless sensor networks. AEU-Int J Electron Commun 131:153605. https://doi.org/10.1016/j.aeue.2021.153605

    Article  Google Scholar 

  11. Han J (2020) Wireless communications by Theodore S. Rappaport Pearson, New York

    Google Scholar 

  12. Leu J-S, Chiang T-H, Min-Chieh Yu, Kuan-Wu Su (2015) Energy efficient clustering scheme for prolonging the lifetime of wireless sensor network with isolated nodes. IEEE Commun Lett 19(2):259–262. https://doi.org/10.1109/LCOMM.2014.2379715

    Article  Google Scholar 

  13. Heinzelman WB, Chandrakasan AP, Balakrishnan H (2002) An application-specific protocol architecture for wireless microsensor networks. IEEE Trans Wirel Commun 1(4):660–670. https://doi.org/10.1109/TWC.2002.804190

    Article  Google Scholar 

  14. Wang J, Gao Y, Liu W, Sangaiah AK, Kim HJ (2019) Energy efficient routing algorithm with mobile sink support for wireless sensor networks. Sensors 19(7):1494

    Article  Google Scholar 

  15. Mozaffari M, Saad W, Bennis M, Nam YH, Debbah M (2019) A tutorial on UAVs for wireless networks: applications, challenges, and open problems. IEEE Commun Surv Tutorials 21(3):2334–2360. https://doi.org/10.1109/COMST.2019.2902862

    Article  Google Scholar 

  16. Luo C, Chen W, Li D, Wang Y, Hongwei Du, Lidong Wu, Weili Wu (2021) “Optimizing flight trajectory of UAV for efficient data collection in wireless sensor networks. Theoret Comput Sci 853:25–42

    Article  MathSciNet  MATH  Google Scholar 

  17. Wu P, Xiao F, Huang H, Sha C, Yu S (2020) Adaptive and extensible energy supply mechanism for UAVs-aided wireless-powered internet of things. IEEE Internet Things J 7(9):9201–9213. https://doi.org/10.1109/JIOT.2020.3005133

    Article  Google Scholar 

  18. Xiang H, Tian L (2011) Development of a low-cost agricultural remote sensing system based on an autonomous unmanned aerial vehicle (UAV). Biosyst Eng 108:174–190

    Article  Google Scholar 

  19. Sun P, Boukerche A (2018) Performance modeling and analysis of a UAV path planning and target detection in a UAV-based wireless sensor network. Comput Netw 146:217–231. https://doi.org/10.1016/j.comnet.2018.09.022

    Article  Google Scholar 

  20. Arafat MY, Habib MA, Moh S (2020) Routing protocols for UAV- aided wireless sensor networks. Appl Sci 10:4077. https://doi.org/10.3390/app10124077

    Article  Google Scholar 

  21. Liu J, Tong P, Wang X, Bai B, Dai H (2021) UAV-Aided data collection for information freshness in wireless sensor networks. IEEE Trans Wirel Commun 20(4):2368–2382. https://doi.org/10.1109/TWC.2020.3041750

    Article  Google Scholar 

  22. Mao C, Liu J, & Xie L (2020) Multi-UAV aided data collection for age minimization in wireless sensor networks, In: 2020 International Conference on wireless communications and signal processing (WCSP), IEEE, pp 80-85

  23. Mazayev A, Correia N, Schütz G (2016) Data gathering in wireless sensor networks using unmanned aerial vehicles. Int J Wirel Inf Netw 23:297–309. https://doi.org/10.1007/s10776-016-0319-y

    Article  Google Scholar 

  24. Jawhar I, Mohamed N, Al-Jaroodi J, Zhang S (2013) A framework for using unmanned aerial vehicles for data collection in linear wireless sensor networks. J Intell Robot Syst 74:437–453

    Article  Google Scholar 

  25. Martinez-de Dios JR, Lferd K, de San Bernabé A, Núnez G, Torres-González A, Ollero A (2013) Cooperation between UAS and wireless sensor networks for efficient data collection in large environments. J Intell Robot Syst 70(1):491–508

    Google Scholar 

  26. Zanjie H, Hiroki N, Nei K, Fumie O, Ryu M, Baohua Z (2014) Resource allocation for data gathering in UAV-Aided wireless sensor networks, In: Proceedings of the 2014 4th IEEE international Conference on network infrastructure and digital content, Karlskrona, Sweden, 19–21 pp 11–16

  27. Okcu H, Soyturk M (2014) Distributed clustering approach for UAV integrated wireless sensor networks. Int J Ad Hoc Ubiquitous Comput 15:106

    Article  Google Scholar 

  28. Uddin M, Mansour A, Le Jeune D, Ammad-Uddin M, Aggoune E (2018) UAV-Assisted dynamic clustering of wireless sensor networks for crop health monitoring. Sensors 18:555

    Article  Google Scholar 

  29. Caillouet C, Giroire F, Razafindralambo T (2019) Efficient data collection and tracking with flying drones. Ad Hoc Netw 89:35–46

    Article  Google Scholar 

  30. Ebrahimi D, Sharafeddine S, Ho P-H, Assi C (2018) UAV-Aided projection-based compressive data gathering in wireless sensor networks. IEEE Internet Things J 6:1893–1905

    Article  Google Scholar 

  31. Poudel S, Moh S (2021) Hybrid path planning for efficient data collection in uav-aided wsns for emergency applications. Sensors 21:2839. https://doi.org/10.3390/s21082839

    Article  Google Scholar 

  32. Nazib RA, Moh S (2021) Energy-efficient and fast data collection in uav-aided wireless sensor networks for hilly terrains. IEEE Access 9:23168–23190. https://doi.org/10.1109/ACCESS.2021.3056701

    Article  Google Scholar 

  33. Ebrahimi D, Sharafeddine S, Ho PH, & Assi C (2018) Data collection in wireless sensor networks using UAV and compressive data gathering, In: 2018 IEEE global communications Conference (GLOBECOM), IEEE, pp 1-7

  34. Gupta GP, Chawra VK, & Dewangan S (2019) Optimal path planning for UAV using NSGA-II based metaheuristic for sensor data gathering application in wireless sensor networks, In: 2019 IEEE international Conference on advanced networks and telecommunications systems (ANTS), IEEE, GPpp 1-5

  35. Amit Choudhary M, Nizamuddin MZ, Sachan VK (2020) Multi-objective optimization framework complying IEEE 802.15.6 communication standards for wireless body area networks. Wireless Netw 26(6):4339–4362. https://doi.org/10.1007/s11276-020-02342-y

    Article  Google Scholar 

  36. Popescu D, Dragana C, Stoican F, Ichim L, Stamatescu G (2018) A collaborative UAV-WSN network for monitoring large areas. Sensors 18:4202

    Article  Google Scholar 

  37. Deb K, Pratap A, Agarwal S, Meyarivan TAMT (2002) A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans Evolut Comput 6(2):182–197. https://doi.org/10.1109/4235.996017

    Article  Google Scholar 

  38. Abeywickrama HV, Jayawickrama BA, He Y, Dutkiewicz E (2018) Comprehensive energy consumption model for unmanned aerial vehicles, based on empirical studies of battery performance. IEEE Access 6:58383–58394. https://doi.org/10.1109/ACCESS.2018.2875040

    Article  Google Scholar 

  39. Singh SP, Sharma SC (2017) Genetic-algorithm-based energy-efficient clustering (gaeec) for homogenous wireless sensor networks. IETE J Res 64(5):648–659. https://doi.org/10.1080/03772063.2017.1364981

    Article  Google Scholar 

  40. Singh S, Malik A, Kumar R, Singh PK (2021) A proficient data gathering technique for unmanned aerial vehicle-enabled heterogeneous wireless sensor networks. Int J Commun Syst 34(16):e4956. https://doi.org/10.1002/dac.4956

    Article  Google Scholar 

  41. Ammad-Udin M, Mansour A, Le Jeune D, Aggoune EHM, & Ayaz M (2016) UAV routing protocol for crop health management, In: 2016 24th European signal processing Conference (EUSIPCO), IEEE, pp 1818-1822

  42. Amit Choudhary M, Nizamuddin MZ (2022) Body node coordinator placement algorithm for wban using multi-objective swarm optimization. IEEE Sens J 22(3):2858–2867. https://doi.org/10.1109/JSEN.2021.3135269

    Article  Google Scholar 

  43. Intel Australia, (2017). Specifications for the Intel Aero Ready to Fly Drone [Online]. Available: https://www.intel.com.au/content/www/au/en/support/articles/ 000023272/drones/development-drones.html

  44. DUALSKY Advanced Power Systems, “Dualsky Xpower ECO-S V2 Series 25–30C” [Online]. Available: http://www.dualsky.com/ECO_Series/Xpower_ECO-S.shtml

  45. Singh MK, Amin SI, Choudhary A (2021) A survey on the characterization parameters and lifetime improvement techniques of wireless sensor network. Frequenz 75(9–10):431–448. https://doi.org/10.1515/freq-2020-0163

    Article  Google Scholar 

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Acknowledgements

We sincerely thank department of electronics & communication engineering, KIET Group of Institutions, Ghaziabad, India, for providing the opportunity and guidance for research work.

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Authors and Affiliations

  1. Department of Electronics and Communication Engineering, KIET Group of Institutions, Ghaziabad, India

    Manish Kumar Singh

  2. Department of Electronics and Communication Engineering, Jamia Millia Islamia- A Central University, New Delhi, India

    Amit Choudhary

  3. Department of Electronics and Communication Engineering, School of Engineering & Technology, Sushant University, Gurugram, India

    Sandeep Gulia

  4. Department of Electrical Engineering, Government Polytechnic Gorakhpur, Gorakhpur, India

    Anurag Verma

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Singh, M.K., Choudhary, A., Gulia, S. et al. Multi-objective NSGA-II optimization framework for UAV path planning in an UAV-assisted WSN. J Supercomput 79, 832–866 (2023). https://doi.org/10.1007/s11227-022-04701-2

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