Power Control and Trajectory Planning Based Interference Management for UAV-Assisted Wireless Sensor Networks
Wireless sensor networks are generally used to assist in collecting and transmitting data where humans cannot directly explore. But in a scenario with complex terrestrial environment, the ground communication links between sensors become so weak to provide reliable and high-speed services. Unmanned...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2020-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/8943430/ |
| _version_ | 1818444487194574848 |
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| author | Shuo Zhang Shuo Shi Shushi Gu Xuemai Gu |
| author_facet | Shuo Zhang Shuo Shi Shushi Gu Xuemai Gu |
| author_sort | Shuo Zhang |
| collection | DOAJ |
| description | Wireless sensor networks are generally used to assist in collecting and transmitting data where humans cannot directly explore. But in a scenario with complex terrestrial environment, the ground communication links between sensors become so weak to provide reliable and high-speed services. Unmanned aerial vehicles (UAVs) can be used as flying relays to enhance connective reliability of terrestrial wireless sensor networks. However, in a UAV-assisted wireless senor network, if the UAV shares the same spectrum with sensors, the interference degrades the quality of communication links when sensors exist in pairs under co-channel conditions. Motivated thereby, we manage the interference by optimizing the transmit power of all communication nodes and planning the trajectory of UAV to achieve the goal of maximizing the sum throughput of the target sensor. Due to the nonconvexity of the optimization problems, we utilize difference of two convex functions (D.C.) programming and successive convex approximation to obtain the suboptimal solutions. Simulation results prove that the minimum signal-to-interference-plus-noise ratio (SINR) required by sensor pairs, flight altitude and maximum transmit power of the UAV can be carefully selected to maximize the sum throughput of target sensor, when the UAV's trajectory is pre-planned. The successive trajectory planning algorithm is also employed to significantly improve the sum throughput. |
| first_indexed | 2024-12-14T19:16:43Z |
| format | Article |
| id | doaj.art-1bcca055c2f548fb873e235e7ae94a46 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-14T19:16:43Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-1bcca055c2f548fb873e235e7ae94a462022-12-21T22:50:34ZengIEEEIEEE Access2169-35362020-01-0183453346410.1109/ACCESS.2019.29625478943430Power Control and Trajectory Planning Based Interference Management for UAV-Assisted Wireless Sensor NetworksShuo Zhang0https://orcid.org/0000-0001-6342-8547Shuo Shi1Shushi Gu2https://orcid.org/0000-0002-3897-5407Xuemai Gu3School of Electronic and Information Engineering, Harbin Institute of Technology, Harbin, ChinaSchool of Electronic and Information Engineering, Harbin Institute of Technology, Harbin, ChinaSchool of Electronic and Information Engineering, Harbin Institute of Technology, Shenzhen, ChinaSchool of Electronic and Information Engineering, Harbin Institute of Technology, Harbin, ChinaWireless sensor networks are generally used to assist in collecting and transmitting data where humans cannot directly explore. But in a scenario with complex terrestrial environment, the ground communication links between sensors become so weak to provide reliable and high-speed services. Unmanned aerial vehicles (UAVs) can be used as flying relays to enhance connective reliability of terrestrial wireless sensor networks. However, in a UAV-assisted wireless senor network, if the UAV shares the same spectrum with sensors, the interference degrades the quality of communication links when sensors exist in pairs under co-channel conditions. Motivated thereby, we manage the interference by optimizing the transmit power of all communication nodes and planning the trajectory of UAV to achieve the goal of maximizing the sum throughput of the target sensor. Due to the nonconvexity of the optimization problems, we utilize difference of two convex functions (D.C.) programming and successive convex approximation to obtain the suboptimal solutions. Simulation results prove that the minimum signal-to-interference-plus-noise ratio (SINR) required by sensor pairs, flight altitude and maximum transmit power of the UAV can be carefully selected to maximize the sum throughput of target sensor, when the UAV's trajectory is pre-planned. The successive trajectory planning algorithm is also employed to significantly improve the sum throughput.https://ieeexplore.ieee.org/document/8943430/Wireless sensor networkUAV communicationthroughput maximizationpower controltrajectory planning |
| spellingShingle | Shuo Zhang Shuo Shi Shushi Gu Xuemai Gu Power Control and Trajectory Planning Based Interference Management for UAV-Assisted Wireless Sensor Networks IEEE Access Wireless sensor network UAV communication throughput maximization power control trajectory planning |
| title | Power Control and Trajectory Planning Based Interference Management for UAV-Assisted Wireless Sensor Networks |
| title_full | Power Control and Trajectory Planning Based Interference Management for UAV-Assisted Wireless Sensor Networks |
| title_fullStr | Power Control and Trajectory Planning Based Interference Management for UAV-Assisted Wireless Sensor Networks |
| title_full_unstemmed | Power Control and Trajectory Planning Based Interference Management for UAV-Assisted Wireless Sensor Networks |
| title_short | Power Control and Trajectory Planning Based Interference Management for UAV-Assisted Wireless Sensor Networks |
| title_sort | power control and trajectory planning based interference management for uav assisted wireless sensor networks |
| topic | Wireless sensor network UAV communication throughput maximization power control trajectory planning |
| url | https://ieeexplore.ieee.org/document/8943430/ |
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