Energy-Efficient Data Gathering Framework-Based Clustering via Multiple UAVs in Deadline-Based WSN Applications
Author(s) -
Alaa Taima Albu-Salih,
Seyed Amin Hosseini Seno
Publication year - 2018
Publication title -
ieee access
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.587
H-Index - 127
ISSN - 2169-3536
DOI - 10.1109/access.2018.2882161
Subject(s) - aerospace , bioengineering , communication, networking and broadcast technologies , components, circuits, devices and systems , computing and processing , engineered materials, dielectrics and plasmas , engineering profession , fields, waves and electromagnetics , general topics for engineers , geoscience , nuclear engineering , photonics and electrooptics , power, energy and industry applications , robotics and control systems , signal processing and analysis , transportation
This paper proposes a new method for energy-efficient data gathering using multiple unmanned aerial vehicles (UAVs) in deadline-based wireless sensor networks (WSNs). This method collects WSN node data in minimum energy by providing the optimal position and trajectory of UAVs, the minimum travel time, and the minimum number of UAVs in a determined deadline. First, in order to minimize the energy consumption of WSN nodes and determine the positions of where to place UAVs for receiving network nodes data, this paper clusters the nodes in a distributed form and considers the centers of these clusters as a place to meet the UAVs. Then, beginning and ending virtual nodes are used for controlling the minimum number of UAVs. This paper attempts to complete the proposed solution and obtain the minimum travel time of UAV required for collecting data from the network. In order to find the optimal solution for this problem, a mixed integer linear programming mathematical model is presented, followed by normalizing and putting weights on each part of an objective function. Results obtained in the simulation step show that the presented model has an optimal performance in providing the position and optimal trajectory of UAVs, energy consumption, minimum travel time, and the number of UAVs used.
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