Performance Analysis On Uav Assisted Data Collection In Wsn
DOI:
https://doi.org/10.29284/7aj6xb53Keywords:
Wireless sensor network, environmental conditions, data collection, data loss, classification and optimizationAbstract
Wireless Sensor Network (WSN) has no communications in huge number of wireless sensors which observe the abiotic or biotic conditions. Data collection is a basic problem in WSNs. During the data collection, the in SN is fed into sensed data from each sensor node and the base station (BS). The Unmanned Aerial Vehicle (UAV) collect the sensed data is transmitting to the BS. In WSNs, the aim of data collection is to gather the different number of data to improves the flexibility and reduce the data loss due to few amount of memory capacity of sensor nodes. The several researchers introduced to diverse data collection technique for gathering data. But, the WSN using UAV aids to data collection method overcome many obstacles because of energy boundaries and sensors. The current research provides comprehensive overview of data collection methods in WSN. Therefore, classification and optimization methods are used for performing resource efficient data collection in WSN with higher accuracy and minimum energy consumption.
References
[1] A. A. Amer, R. Ahmed, I. S. Fahim and T. Ismail, “Energy Optimization and Trajectory Planning for Constrained Multi-UAV Data Collection in WSNs”, IEEE Access, Volume 12, January 2024, Pages 9047 – 9061
[2] C. Luo, J. Zhang, J. Qian, Yi Hong, Z. Chen, Y. Hou, X. Zhang and Y. Zhu, “Data collection of wireless sensor network based on trajectory optimization of laser-charged UAV”, High-Confidence Computing, Elsevier, Volume 4, Issue 2, June 2024, Pages 1-13
[3] V. A. Memos and K. E. Psannis, “Optimized UAV-based data collection from MWSNs”, ICT Express, Elsevier, Volume 9, Issue 1, February 2023, Pages 29-33
[4] V. Pandiyaraju, S. Ganapathy, N. Mohith and A. Kannan, “An optimal energy utilization model for precision agriculture in WSNs using multi-objective clustering and deep learning”, Journal of King Saud University - Computer and Information Sciences, Elsevier, Volume 35, Issue 10, December 2023, Pages 1-13
[5] I. Yoon and D. Kun Noh, “Adaptive Data Collection using UAV with Wireless Power Transfer for Wireless Rechargeable Sensor Networks”, IEEE Access, Volume 10, January 2022, Pages 9729 – 9743
[6] I. Surenther, K.P. Sridhar and M. Kingston Roberts, “Maximizing energy efficiency in wireless sensor networks for data transmission: A Deep Learning-Based Grouping Model approach”, Alexandria Engineering Journal, Elsevier, Volume 83, 15 November 2023, Pages 53-65
[7] J. Chen and J. Tang, “UAV-assisted data collection for wireless sensor networks with dynamic working modes”, Digital Communications and Networks, Elsevier, 2022, Pages 1-12
[8] Z. Zhanga, F. Xua, Z. Qin and Y. Xie, “Resource allocation in UAV assisted air ground intelligent inspection system”, Cognitive Robotics, Elsevier, Volume 2, 2022, Pages 1–12
[9] Y. Hu, Y. Liu, A. Kaushik, C. Masouros and J. S. Thompson, “Timely Data Collection for UAV-based IoT networks: A Deep Reinforcement Learning Approach”, IEEE Sensors Journal, Volume 23, Issue 11, 01 June 2023, Pages 12295 – 12308
[10] K. Li, W. Ni and F. Dressler, “Continuous Maneuver Control and Data Capture Scheduling of Autonomous Drone in Wireless Sensor Networks”, IEEE Transactions on Mobile Computing, Volume 21, Issue 8, 01 August 2022, Pages 2732 – 2744
[11] T. Wang, X. Pang, J. Tang, N. Zhao, X. Zhang and X. Wang, “Time and energy efficient data collection via UAV”, Science China Information Sciences, Springer, Volume 65, Issue 182302, 2022, Pages 1-15
[12] K. Khac Nguyen, T. Q. Duong, T. Do-Duy, H. Claussen and L. Hanzo, “3D UAV Trajectory and Data Collection Optimisation via Deep Reinforcement Learning”, IEEE Transactions on Communications, Volume 70, Issue 4, April 2022, Pages 2358 – 2371
[13] K. Li, W. Ni, E. Tovar and M. Guizani, “Joint Flight Cruise Control and Data Collection in UAV-Aided Internet of Things: An Onboard Deep Reinforcement Learning Approach”, IEEE Internet of Things Journal, Volume 8, Issue 12, 15 June 2021, Pages 9787 – 9799
[14] C. Lin, G. Han; X. Qi; J. Du; T. Xu and M. Martínez-García, “Energy-Optimal Data Collection for Unmanned Aerial Vehicle-Aided Industrial Wireless Sensor Network-Based Agricultural Monitoring System: A Clustering Compressed Sampling Approach”, IEEE Transactions on Industrial Informatics, Volume 17, Issue 6, June 2021, Pages 4411 – 4420
[15] H. Zhang, L. Dou, B. Xin, J. Chen, M. Gan and Y. Ding and P. Cheng, “Data Collection Task Planning of a Fixed-Wing Unmanned Aerial Vehicle in Forest Fire Monitoring”, IEEE Access, Volume 9, August 2021, Pages 109847 – 109864
[16] H. Liu, R. Chen, S. Ding, Z. Jiang, F. Liu and J. Zhang, “An energy efficiency routing protocol for UAV-aided WSNs data collection”, Ad Hoc Networks, Elsevier, Volume 154, March 2024, Pages 1-15
[17] K. Bani-Hani, K. F. Hayajneh, A. Jaradat and H. Shakhatreh, “Energy-efficient UAV-wireless networks for data collection”, Physical Communication, Elsevier, Volume 60, October 2023, Pages 1-15
[18] X. Zhu, L. Wang, Y. Li, S. Song, S. Ma, F. Yang and L. Zhai, “Path planning of multi-UAVs based on deep Q-network for energy-efficient data collection in UAVs-assisted IoT”, Vehicular Communications, Elsevier, Volume 36, August 2022, Pages 1-18
[19] M. Rusyadi Ramli, J.M Lee and D.S. Kim, “Hybrid MAC Protocol for UAV-Assisted Data Gathering in a Wireless Sensor Network”, Internet of Things, Elsevier, Volume 14, June 2021, Pages 1-18
[20] Z. Xu., “UAV surveying and mapping information collection method based on Internet of Things”,Internet of Things and Cyber-Physical Systems, Elsevier, Volume 2, 2022, Pages 138-144
[21] Q. Wu, P. Sun and A. Boukerche, “Unmanned aerial vehicle-assisted energy-efficient data collection scheme for sustainable wireless sensor networks”, Computer Networks, Elsevier, Volume 165, 24 December 2019, Pages 1-15
[22] N. Mazumdar, S. Roy, A. Nag and J. Prakash Singh., “A buffer-aware dynamic UAV trajectory design for data collection in resource-constrained IoT frameworks”, Computers and Electrical Engineering, Elsevier, Volume 100, May 2022, Pages 1-15
[23] W. Chen, S. Zhao, R. Zhang, Y. Chen and L. Yang, “UAV-Assisted Data Collection with Nonorthogonal Multiple Access”, IEEE Internet of Things Journal, Volume 8, Issue 1, 01 January 2021, Pages 501 – 511
[24]M. Zhu, Z. Wei, C. Qiu, W. Jiang, H. Wu and Z. Feng “Joint Data Collection and Sensor Positioning in Multi-UAV-Assisted Wireless Sensor Network”, IEEE Sensors Journal, Volume 23, Issue 19, 01 October 2023, Pages 23664 – 23675
[25] X. Wang, X. Liu, C.T. Cheng, L. Deng, X. Chen and F. Xiao, “A Joint User Scheduling and Trajectory Planning Data Collection Strategy for the UAV-Assisted WSN”,IEEE Communications Letters, Volume 25, Issue 7, July 2021, Pages 2333 – 2337
[26] G. Li, B. He, Z. Wang, X. Cheng and J. Chen, “Blockchain-Enhanced Spatiotemporal Data Aggregation for UAV-Assisted Wireless Sensor Networks”, IEEE Transactions on Industrial Informatics, Volume 18, Issue 7, July 2022, Pages 4520 – 4530
[27] J. Chen and J. Tang, “UAV-Assisted Data Collection for Dynamic and Heterogeneous Wireless Sensor Networks”,IEEE Wireless Communications Letters, Volume 11, Issue 6, June 2022, Pages 1288 – 1292
[28] K.V Nguyen, C.H Nguyen, T. Van Do and C. Rotter “Efficient Multi-UAV Assisted Data Gathering Schemes for Maximizing the Operation Time of Wireless Sensor Networks in Precision Farming”, IEEE Transactions on Industrial Informatics, Volume 19, Issue 12, December 2023, Pages 11664 – 11674
[29]H. Xiaoge, W. Lingzhi, H. Yong and C. Qianbin, “Actor-Critic-based UAV-assisted data collection in the wireless sensor network”, China Communications, Volume 21, Issue 4, April 2024, Pages 163 - 177
[30] P. A. Karegar, D. Zuhair Al-Hamid and P. Han Joo Chong, “UAV-enabled software defined data collection from an adaptive WSN”, Wireless Networks, Springer, 2024, Pages 1-15
Downloads
Published
Issue
Section
License
This work is licensed under a Creative Commons Attribution 4.0 International License.
This work is licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
