International Journal of Computer Networks and Applications

Open Access Open Access  Restricted Access Subscription Access

Recursive Perceptron Long Short Term Memory for Wireless Data Transmission in Unmanned Aerial Vehicles


Affiliations
1 Department of Computer Science and Engineering, Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Avadi, Chennai, Tamil Nadu, India
 

In Wireless Sensor Networks, diverse nodes are associated with each other for monitoring definite circumstances. So, sensors are considerably utilized in distinct real-time utilizations namely remote operated unmanned vehicle, atmospheric surveillance, disaster management, and so on. Transmitting data from a remote operated unmanned vehicle to server via Long Term Evolution (LTE) with the harmony of Bluetooth Low Energy (BLE) relaying remains the core of significant data transmission in wireless networks. The utilization of Unmanned Aerial Vehicles (UAVs) for wireless networks is swiftly heightening as the driving force of new applications due to their distinctive resources for improving coverage and energy efficiency of wireless network UAVs act as base stations. In other condition, data-driven Deep Learning-assisted (DL) strategies using multilayer perceptron are acquiring an increasing interest for not utilizing huge frequency of generated data, however ensuring network procedure in an optimal manner and hence providing QoS requirements of wireless networks. But, UAVs is resource-constrained devices specifically in power resources and data transmission. With traditional DL scheme being cloud-centric necessitate UAVs' data are stored in centralized server, therefore generating huge communication overhead and thus result in network bandwidth and energy inefficiency of UAV devices. To address these issues in this work, a Fully Recursive Long Short Term Memory (FR-LSTM) for improving data transmission rates and quality of service in wireless networks is proposed. Initially, Deep Learning-based model was designed in Long Term Evolution (LTE) Dominant Influencing Criterions (DIC) estimation. The applications of power resources and bandwidth allocation (PRBA) in self-organizing LTE small cell network, therefore minimizing RMSE and average end-to-end delay involved in transmission. Next, a Fully Recursive Perceptron Network (FRPC) and LSTM model was utilized and applied for DIC to resolve the UAV position which reduces overall system performance and user throughput. Hence, for classification regression tasks, when is there no LTE signal, data can be transmitted to another device through BLE (Bluetooth Low Energy), therefore ensuring throughput and ensuring minimum latency. The effectiveness of FR-LSTM is yet to be validated using four kinds of evaluation metrics with diverse number of nodes, namely, RMSE, throughput, average end-to-end delay, and latency.

Keywords

Wireless Sensor Network, Long Term Evolution, Long Short Term Memory, Dominant Influencing Criterion, Root Mean Square Error, Power Resources and Bandwidth Allocation.
User
Notifications
Font Size

  • Visalakshi Annepu, A. Rajesh, “An unmanned aerial vehicle-aided node localization using an efficient multilayer perceptron neural network in wireless sensor networks”, Neural Computing and Applications, Springer, 32, 11651–11663, Dec 2019 [multilayer perceptron neural network].

  • Saptarshi Chaudhuri, Irfan Baig, Debabrata Das, “A Novel QoS aware Medium Access Control Scheduler for LTE-Advanced Network”, Computer Networks, Elsevier, 135, 1-14, Jan 2018 [Multi Objective QoS aware LTE-A Downlink-MAC Scheduler (MOQDS) method].

  • Maher Aljehani, Masahiro Inoue, Akira Watanbe, Taketoshi Yokemura, Fumiya Ogyu, Hidemasa Iida, “UAV communication system integrated into network traversal with mobility”, Springer Nature, 2, 1057, May 2020.

  • Bouziane Brik, Adlen Ksentini, Maha Bouaziz “Federated Learning for UAVs-Enabled Wireless Networks: Use Cases, Challenges, and Open Problems”, IEEE Access, 8, 53841– 53849, Mar 2020.

  • Mohammad N. Patwary, Syed Junaid Nawaz, Abdur Rahman, Shree Krishna Sharma, Mamunur Rashid, Stuart J. Barnes, “The Potential Short- and Long-Term Disruptions and Transformative Impacts of 5G and Beyond Wireless Networks: Lessons Learnt From the Development of a 5G Testbed Environment”, IEEE Access, 8, 11352– 11379, Jan 2020.

  • Hanif Ullah, Mamun Abu-Tair, Sally McClean, Paddy Nixon, Gerard Parr, Chunbo Luo, “Connecting Disjoint Nodes Through a UAV-Based Wireless Network for Bridging Communication Using IEEE 802.11 Protocols”, EURASIP Journal on Wireless Communications and Networking, 142, Mar 2020.

  • Wen Zhan, Lin Dai,” Massive Random Access of Machine-to-Machine Communications in LTE Networks: Throughput Optimization with a Finite Data Transmission Rate”, IEEE Transactions on Wireless Communications, 18, 12, 5749-5763, Jul 2019.

  • Sung-Woong Jo, Woo-Seong Shim, “LTE-Maritime: High-Speed Maritime Wireless Communication Based on LTE Technology”, IEEE Access, 7, 53172-53181, Mar 2019.

  • Jun Zheng, Jie Xiao, Qilei Ren, and Yuan Zhang, “Performance Modeling of an LTE LAA and WiFi Coexistence System using the LAA Category-4 LBT Procedure and 802.11e EDCA Mechanism”, IEEE Transactions on Vehicular Technology, 69, 6, 6603– 6618, Jun 2020.

  • Aiqing Zhang, Jianxin Chen, Rose Qingyang Hu, Yi Qian, “SeDS: Secure Data Sharing Strategy for D2D Communication in LTE-Advanced Networks”, IEEE Transactions on Vehicular Technology, 65, 4, 2659– 2672, Mar 2015.

  • Haonan Hu, Yuan Gao, Jiliang Zhang, Xiaoli Chu, Qianbin Chen, Jie Zhang,” On the Fairness of the Coexisting LTE-U and WiFi Networks Sharing Multiple Unlicensed Channels”, IEEE Transactions on Vehicular Technology, 69, 11, 13890– 13904, Jul 2020.

  • Sarvjit Singh, Amit Gupta, J. S. Sohal, “Transmission of Audio over LTE Packet Based Wireless Networks Using Wavelets”, Wireless Personal Communications, Springer, 112, 541–553, Jan 2020.

  • Younghwan Jung, Daehee Kim, Sunshin An, “Scalable group-based machine-to-machine communications in LTE-advanced networks”, Wireless Network, Springer, 25, 63–74, Jun 2017.

  • Khalid M. Hosny, Marwa M. Khashaba, Walid I. Khedr, Fathy A. Amer, “New vertical handover prediction schemes forLTE-WLAN heterogeneous networks”, PLOS ONE https://doi.org/10.1371/journal.pone.0215334 April 17, 2019.

  • Welton Araujo, Rafael Fogarolli, Marcos Seruffo, Diego Cardoso, “Deployment of small cells and a transport infrastructure concurrently for next generation mobile access networks “, PLOS ONE https://doi.org/10.1371/journal.pone.0207330 November 26, 2020.

  • Khong-Lim Yap, Yung-Wey Chong, Weixia Liu, “Enhanced handover mechanism using mobility prediction in wireless networks”, PLOS ONE https://doi.org/10.1371/journal.pone.0227982 January 24, 2020.

  • Mun-Suk Kim, Yena Kim, SeungSeob Lee, SuKyoung Lee, Nada Golmie, “A user application-based access point selection algorithm for dense WLANs”, PLOS ONE https://doi.org/10.1371/journal.pone.0210738 January 16, 2019.

  • S. Schwarz, B. Ramos Elbal, E. Zöchmann, L. Marijanovic, S. Pratschner, “Dependable wireless connectivity: insights and methods for 5G and beyond”, Elsevier, 135, 449–455, Oct 2018.

  • Kaveh Pahlavan, Prashant Krishnamurthy, “Evolution and Impact of Wi‑Fi Technology and Applications: A Historical Perspective”, International Journal of Wireless Information Networks, Springer, 28, 3–19, Nov 2020.

  • Zeeshan Hameed Mir, Fethi Filali, “LTE and IEEE 802.11p for vehicular networking: a performance evaluation”, EURASIP Journal on Wireless Communications and Networking, 89, Aug 2014.

  • X. Wang, M. Jia, Q. Guo, I. W. Ho and J. Wu, "Joint Power, Original Bandwidth, and Detected Hole Bandwidth Allocation for Multi-Homing Heterogeneous Networks Based on Cognitive Radio," in IEEE Transactions on Vehicular Technology, 68, 3, 2777-2790, March 2019, doi: 10.1109/TVT.2019.2892184.

  • Yue Wang, Xuhui Luo, Xiaojie Wu, Long-term evolution and lifetime analysis of geostationary transfer orbits with solar radiation pressure, Acta Astronautica, 175, 2020, 405-420,ISSN 0094-5765,https://doi.org/10.1016/j.actaastro.2020.06.007.

  • Y. Yu, W. Lu, Y. Liu and H. Zhu, "Neural-Network-Based Root Mean Delay Spread Model for Ubiquitous Indoor Internet-of-Things Scenarios," in IEEE Internet of Things Journal, 7, 6, 5580-5589, June 2020, doi: 10.1109/JIOT.2020.2979766.

  • Jimenez, AF., Ortiz, B.V., Bondesan, L. et al. Long Short-Term Memory Neural Network for irrigation management: a case study from Southern Alabama, USA. Precision Agric 22, 475–492 (2021). https://doi.org/10.1007/s11119-020-09753-z

  • J. Liu, Z. Zhao, J. Ji and M. Hu, "Research and application of wireless sensor network technology in power transmission and distribution system," in Intelligent and Converged Networks, 1, 2, 199-220, Sept. 2020, doi: 10.23919/ICN.2020.0016.


Abstract Views: 413

PDF Views: 2




  • Recursive Perceptron Long Short Term Memory for Wireless Data Transmission in Unmanned Aerial Vehicles

Abstract Views: 413  |  PDF Views: 2

Authors

Uma S.
Department of Computer Science and Engineering, Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Avadi, Chennai, Tamil Nadu, India
M. J. Carmel Mary Belinda
Department of Computer Science and Engineering, Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Avadi, Chennai, Tamil Nadu, India

Abstract


In Wireless Sensor Networks, diverse nodes are associated with each other for monitoring definite circumstances. So, sensors are considerably utilized in distinct real-time utilizations namely remote operated unmanned vehicle, atmospheric surveillance, disaster management, and so on. Transmitting data from a remote operated unmanned vehicle to server via Long Term Evolution (LTE) with the harmony of Bluetooth Low Energy (BLE) relaying remains the core of significant data transmission in wireless networks. The utilization of Unmanned Aerial Vehicles (UAVs) for wireless networks is swiftly heightening as the driving force of new applications due to their distinctive resources for improving coverage and energy efficiency of wireless network UAVs act as base stations. In other condition, data-driven Deep Learning-assisted (DL) strategies using multilayer perceptron are acquiring an increasing interest for not utilizing huge frequency of generated data, however ensuring network procedure in an optimal manner and hence providing QoS requirements of wireless networks. But, UAVs is resource-constrained devices specifically in power resources and data transmission. With traditional DL scheme being cloud-centric necessitate UAVs' data are stored in centralized server, therefore generating huge communication overhead and thus result in network bandwidth and energy inefficiency of UAV devices. To address these issues in this work, a Fully Recursive Long Short Term Memory (FR-LSTM) for improving data transmission rates and quality of service in wireless networks is proposed. Initially, Deep Learning-based model was designed in Long Term Evolution (LTE) Dominant Influencing Criterions (DIC) estimation. The applications of power resources and bandwidth allocation (PRBA) in self-organizing LTE small cell network, therefore minimizing RMSE and average end-to-end delay involved in transmission. Next, a Fully Recursive Perceptron Network (FRPC) and LSTM model was utilized and applied for DIC to resolve the UAV position which reduces overall system performance and user throughput. Hence, for classification regression tasks, when is there no LTE signal, data can be transmitted to another device through BLE (Bluetooth Low Energy), therefore ensuring throughput and ensuring minimum latency. The effectiveness of FR-LSTM is yet to be validated using four kinds of evaluation metrics with diverse number of nodes, namely, RMSE, throughput, average end-to-end delay, and latency.

Keywords


Wireless Sensor Network, Long Term Evolution, Long Short Term Memory, Dominant Influencing Criterion, Root Mean Square Error, Power Resources and Bandwidth Allocation.

References





DOI: https://doi.org/10.22247/ijcna%2F2021%2F209696