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UAV-Assisted IoT Data Collection Optimization Using Gaining-Sharing Knowledge Algorithm

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Handbook of Nature-Inspired Optimization Algorithms: The State of the Art

Part of the book series: Studies in Systems, Decision and Control ((SSDC,volume 213))

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Abstract

Unmanned aerial vehicles (UAVs) provide an energy-efficient and robust solution for data collection from the internet of things (IoT) devices. However, the UAV’s deployment optimization, including locations of the UAV’s stop points, is necessary to save the overall energy consumption and conduct the data collection efficiently. Thus, the objective is to minimize the energy consumption of the UAV and the IoT devices while collecting the data efficiently. This chapter proposes gaining-sharing knowledge (GSK) algorithm for optimizing the UAV’s deployment. In GSK, the number of UAV’s stop points in the three-dimensional space is encapsulated into a single individual with a fixed-length representing an entire deployment. The validity of using GSK is verified by simulation in five IoT device distribution scenarios. It provides significant results in all five scenarios compared to the other three optimization algorithms.

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Notes

  1. 1.

    In case of population size adaption the number of candidate solutions will change according to the search space and the algorithm state.

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Acknowledgements

This paper is based upon work supported by Science, Technology, and Innovation Funding Authority (STIFA) of Egypt, under grant no. 34876.

Author information

Authors and Affiliations

  1. Wireless Intelligent Networks Center (WINC) School of Engineering and Applied Sciences, Nile University, Giza, 12677, Egypt

    Rania M. Tawfik & M. Saeed Darweesh

  2. School of Information Technology and Computer Science (ITCS), Nile University, Giza, 12677, Egypt

    Hazem A. A. Nomer

  3. Faculty of Computer Science and Engineering, Al Alamein International University, Al Alamein, Egypt

    Hazem A. A. Nomer

  4. Operations Research Department, Faculty of Graduate Studies for Statistical Research, Cairo University, Giza, 12613, Egypt

    Ali Wagdy Mohamed

  5. Department of Mathematics and Actuarial Science, School of Science and Engineering, The American University in Cairo, Cairo, Egypt

    Ali Wagdy Mohamed

  6. Electronics and Communications Engineering Department, Cairo University, Cairo, Egypt

    Hassan Mostafa

  7. University of Science and Technology, Nanotechnology and Nanoelectronics Program, Zewail City of Science and Technology, October Gardens 6th of October, Giza, 12578, Egypt

    Hassan Mostafa

Authors
  1. Rania M. Tawfik
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  2. Hazem A. A. Nomer
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  3. M. Saeed Darweesh
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  4. Ali Wagdy Mohamed
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  5. Hassan Mostafa
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Corresponding author

Correspondence to Rania M. Tawfik .

Editor information

Editors and Affiliations

  1. Operations Research Department, Faculty of Graduate Studies for Statistical Research, Cairo University, Giza, Egypt

    Ali Wagdy Mohamed

  2. Department of Computer Sciences, University of Guadalajara, Guadalajara, Jalisco, Mexico

    Diego Oliva

  3. School of EEE, Nanyang Technological University, Singapore, Singapore

    Ponnuthurai Nagaratnam Suganthan

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Tawfik, R.M., Nomer, H.A.A., Saeed Darweesh, M., Mohamed, A.W., Mostafa, H. (2022). UAV-Assisted IoT Data Collection Optimization Using Gaining-Sharing Knowledge Algorithm. In: Mohamed, A.W., Oliva, D., Suganthan, P.N. (eds) Handbook of Nature-Inspired Optimization Algorithms: The State of the Art. Studies in Systems, Decision and Control, vol 213. Springer, Cham. https://doi.org/10.1007/978-3-031-07516-2_7

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