International Journal of Computer Networks and Applications (IJCNA) DOI: 10.22247/ijcna/2021/209696 Volume 8, Issue 4, July – August (2021) ISSN: 2395-0455 ©EverScience Publications 277 RESEARCH ARTICLE Recursive Perceptron Long Short Term Memory for Wireless Data Transmission in Unmanned Aerial Vehicles 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. uma_priyadarshini@yahoo.com 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. carmelbelinda@veltech.edu.in Received: 30 May 2021 / Revised: 03 July 2021 / Accepted: 10 July 2021 / Published: 28 August 2021 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. Index Terms – Wireless Sensor Network, Long Term Evolution, Long Short Term Memory, Dominant Influencing Criterion, Root Mean Square Error, Power Resources and Bandwidth Allocation. 1. INTRODUCTION Long Term Evolution identification necessitates Medium Access Control (MAC) scheduler entity in bestows full proof QoS in downlink and uplink direction. However, LTE-MAC usually takes into consideration only single impediment like, radio resource availability, user throughput and channel conditions so on. However, in reality not taking into consideration all the constraints in a synchronous manner would affect the QoS requirements. A multilayer perceptron neural network was proposed in [1] based on UAV localization was proposed with the objective of enhancing the localization accuracy. Here, the UAV height plays the dominant role on accuracy aspect, the flying height was initially optimized and followed by which the localization was said to be performed. Moreover, nonlinear MLP model with nonlinear activation functions was employed that in turn serves enhanced as localizing node in WSNs by UAV, therefore not only improving localization accuracy but also minimizing the deployment cost. However, MLP-based