Towards optimal positioning and energy-efficient UAV path scheduling in IoT applications

The Unmanned Aerial Vehicle (UAV) based communication has been emerged as a feasible solution for remote applications such as disaster management, and search and rescue due to its mobility and cost efficiency. The prior researches in this domain focused on positioning and path planning of UAVs; however, these approaches faced several limitations due to adverse weather conditions of the environment. In this paper, the impact of weather-based positioning and path planning of UAVs (IWPOP-UAV) is carried out to achieve increased QoS, reliability and energy efficiency in UAV communications. Initially, the prediction of weather conditions in the emergency situations is performed by utilizing the Cerebral Long Short-Term Memory (C-LSTM) which possesses negligible training loss and increased accuracy. The cell-based partitioning of emergency area is carried out in order to determine the target UEs. The decision upon number and position of the UAVs in each cell is provided by the A3C algorithm based on weather conditions and other significant factors thereby achieving increased coverage ratio and minimal requirement of UAV transmit power. The path planning of the UAV in order to perform effective collection of data is considered as a multi-objective optimization problem and executed by using Mayfly Optimization Algorithm (MOA). By doing so, the proposed approach is able to achieve increased QoS, reliability and energy efficiency in UAV based communication. The proposed IWPOP-UAV approach is experimented in NS 3.26 and evaluated in terms of performance metrics such as coverage ratio, cell coverage, delay, path gain, number of collected packets, UAV transmit power, and energy consumption. The results obtained are summarized and concluded to demonstrate the efficacy of the proposed approach. © 2022 Elsevier B.V.

Muthanna M.S.A.1 , Muthanna A. 2, 7 , Nguyen T.N. 3 , Alshahrani A.4 , Abd El-Latif A.A.
Elsevier B.V.
  • 1 Institute of Computer Technologies and Information Security, Southern Federal University, Taganrog, 347922, Russian Federation
  • 2 Peoples’ Friendship University of Russia (RUDN University) 6 Miklukho-Maklaya, Moscow, 117198, Russian Federation
  • 3 Department of Computer Science, Kennesaw State University, Marietta, GA 30060, United States
  • 4 Department of Computer Science and Artificial Intelligence, College of Computer Science and Engineering, University of Jeddah, Jeddah, 21493, Saudi Arabia
  • 5 EIAS Data Science Lab, College of Computer and Information Sciences, Prince Sultan University, Riyadh, 11586, Saudi Arabia
  • 6 Mathematics and Computer Science Department, Faculty of Science, Menoufia University, Shebin El-Koom, 32511, Egypt
  • 7 Department of Telecommunication Networks and Data Transmission, The Bonch-Bruevich Saint-PetersburgState University of Telecommunications, Saint Petersburg, 193232, Russian Federation
Ключевые слова
Emergency situations; Energy efficiency; Path planning; Positioning; QoS; Reliability; UAV communication; Weather conditions
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