Novel Hybrid Deep Optimized Machine Learning Model for Energy Efficient Cluster Selection in Unmanned Aerial Vehicle Networks
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Abstract
Unmanned Aerial Vehicles (UAVs) have grown into a more powerful type of data transmission due to this rapid progress of evolution of wireless communication technology. In addition, UAVs have been proven to be effective in a variety of applications, including intelligent transport, disaster risk management, surveillance, and environmental monitoring. When UAVs are deployed randomly, however, they can effectively accomplish challenging tasks because of the UAVs’ has low battery capacity, quick mobility, and dynamic in nature orientation. Due to this reason, a new technique must be designed for an optimal energy efficient UAV clustering as well as data routing protocols. In this work proposes a new hybrid model of Emperor penguin-based Generalized Approximate Reasoning Based Intelligent Control (EP-GARIC) cluster-based network topology. Furthermore, the optimal routing function is achieved by the proposed Artificial Jellyfish Optimization (AJO). The implementation of this research is carried out using Network Simulator (NS2). The simulation results displays the effective performance of the suggested approach in terms of reduced energy consumption, improved packet delivery ratio, reduced loss, and so on over compared to the conventional approaches
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References
Renduchintala, Ankit, et al. "A comprehensive micro unmanned aerial vehicle (UAV/Drone) forensic framework." Digital Investigation 30 (2019): 52-72.
Torres-Sánchez, Jorge, et al. "Assessing UAV-collected image overlap influence on computation time and digital surface model accuracy in olive orchards." Precision Agriculture 19.1 (2018): 115-133.
Outay, Fatma, Hanan Abdullah Mengash, and Muhammad Adnan. "Applications of unmanned aerial vehicle (UAV) in road safety, traffic and highway infrastructure management: Recent advances and challenges." Transportation research part A: policy and practice 141 (2020): 116-129.
Ahmed, Syed Affan, MujahidMohsin, and Syed Muhammad Zubair Ali. "Survey and technological analysis of laser and its defense applications." Defence Technology 17.2 (2021): 583-592.
Altmann, Jürgen. "New Military Technologies: Dangers for International Security and Peace." S&F Sicherheit und Frieden 38.1 (2020): 36-42.
Bisio, Igor, et al. "Unauthorized amateur UAV detection based on WiFi statistical fingerprint analysis." IEEE Communications Magazine 56.4 (2018): 106-111.
Um, Jung-Sup. "Drone Flight Ready." Drones as Cyber-Physical Systems. Springer, Singapore, 2019. 21-57.
Hassanalian, Mostafa, and AbdessattarAbdelkefi. "Classifications, applications, and design challenges of drones: A review." Progress in Aerospace Sciences 91 (2017): 99-131.
Mohamed, Nader, et al. "Unmanned aerial vehicles applications in future smart cities." Technological Forecasting and Social Change 153 (2020): 119293.
Ullah, Zaib, et al. "Applications of artificial intelligence and machine learning in smart cities." Computer Communications 154 (2020): 313-323.
Alsamhi, Saeed H., et al. "Survey on collaborative smart drones and internet of things for improving smartness of smart cities." Ieee Access 7 (2019): 128125-128152.
Mohamed, N., Al-Jaroodi, J., Jawhar, I., Idries, A., & Mohammed, F. (2020). Unmanned aerial vehicles applications in future smart cities. Technological Forecasting and Social Change, 153, 119293.
Billings, Charles E. Aviation automation: The search for a human-centered approach. CRC Press, 2018.
Alsamhi, Saeed H., et al. "Survey on collaborative smart drones and internet of things for improving smartness of smart cities." Ieee Access 7 (2019): 128125-128152.
Sungheetha, Akey, and Rajesh Sharma. "Real time monitoring and fire detection using internet of things and cloud based drones." Journal of Soft Computing Paradigm (JSCP) 2.03 (2020): 168-174.
Suzuki, Koji AO, Paulo Kemper Filho, and James R. Morrison. "Automatic battery replacement system for UAVs: Analysis and design." Journal of Intelligent & Robotic Systems 65.1 (2012): 563-586.
Lin, Na, et al. "A novel multimodal collaborative drone-assisted VANET networking model." IEEE Transactions on Wireless Communications 19.7 (2020): 4919-4933.
Pustokhina, I. V., Pustokhin, D. A., Kumar Pareek, P., Gupta, D., Khanna, A., & Shankar, K. (2021). Energy‐efficient cluster‐based unmanned aerial vehicle networks with deep learning‐based scene classification model. International Journal of Communication Systems, 34(8), e4786.
Torres-Sánchez, Jorge, et al. "Assessing UAV-collected image overlap influence on computation time and digital surface model accuracy in olive orchards." Precision Agriculture 19.1 (2018): 115-133.
Li, Yabo, et al. "Joint resource allocation and trajectory optimization with QoS in UAV-based NOMA wireless networks." IEEE Transactions on Wireless Communications (2021).
Wu, Jiehong, et al. "A multi-UAV clustering strategy for reducing insecure communication range." Computer Networks 158 (2019): 132-142.
Valadares, Dalton, et al. "Performance Evaluation of an IEEE 802.11 g Network in an Industrial Environment." IEEE Latin America Transactions 18.05 (2020): 947-953.
Azari, M. Mahdi, et al. "UAV-to-UAV communications in cellular networks." IEEE Transactions on Wireless Communications 19.9 (2020): 6130-6144.
Saif, Abdu, et al. "Distributed Clustering for User Devices Under Unmanned Aerial Vehicle Coverage Area during Disaster Recovery." arXiv preprint arXiv:2103.07931 (2021).
Pustokhina, Irina V., et al. "Energy‐efficient cluster‐based unmanned aerial vehicle networks with deep learning‐based scene classification model." International Journal of Communication Systems 34.8 (2021): e4786.
Annepu, Visalakshi, and Anbazhagan Rajesh. "An unmanned aerial vehicle-aided node localization using an efficient multilayer perceptron neural network in wireless sensor networks." Neural Computing and Applications 32.15 (2020): 11651-11663.
Miranda, Julien, et al. "Hybridization of deep and prototypical neural network for rare defect classification on aircraft fuselage images acquired by an unmanned aerial vehicle." Journal of Electronic Imaging 29.4 (2020): 041010.
Bensalem, Amina, and DjallelEddineBoubiche. "EBEESU: ElectriBio-inspired Energy-Efficient Self-organization model for Unmanned Aerial Ad-hoc Network." Ad Hoc Networks 107 (2020): 102236.
Arafat, Muhammad Yeasir, and SangmanMoh. "Bio-inspired approaches for energy-efficient localization and clustering in UAV networks for monitoring wildfires in remote areas." IEEE Access 9 (2021): 18649-18669.
Nazib, Rezoan Ahmed, and SangmanMoh. "Energy-Efficient Data Gathering Schemes in UAV-Based Wireless Sensor Networks." (2020).
Baek, Jaeuk, Sang Ik Han, and Youngnam Han. "Energy-efficient UAV routing for wireless sensor networks." IEEE Transactions on Vehicular Technology 69.2 (2019): 1741-1750.
Humadi, Khaled, et al. "Energy-Efficient Cluster Sizing for User-Centric Air-to-Air Networks." IEEE Communications Letters 25.4 (2020): 1308-1312.
Deng, Jianing, Zhiguo Shi, and Cheng Zhuo. "Energy-efficient real-time uav object detection on embedded platforms." IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems 39.10 (2019): 3123-3127.
Zhu, Botao, et al. "Improved soft-K-means clustering algorithm for balancing energy consumption in wireless sensor networks." IEEE Internet of Things Journal 8.6 (2020): 4868-4881.
Nazib, Rezoan Ahmed, and SangmanMoh. "Energy-efficient and fast data collection in UAV-aided wireless sensor networks for hilly terrains." IEEE Access 9 (2021): 23168-23190.