Article type: Research Article
Authors: Komala, C.R.a; * | Velmurugan, V.b | Maheswari, K.c | Deena, S.d | Kavitha, M.e | Rajaram, A.f
Affiliations:
[a] Department of Information Science and Engineering, HKBK College of Engineering, Bangalore, Karnataka, India
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[b] Department of Electronics and Communication Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Chennai, Tamil Nadu, India
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[c] Department of CSE, CMR Technical Campus Kandlakoya, Hyderabad
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[d] Department of Computer Science Engineering, School of Computing, Amrita Vishwa Vidyapeetham, Chennai, Tamil Nadu, India
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[e] Department of Electrical and Electronics Engineering, Sathyabama Institute of Science and Technology, Chennai, Tamil Nadu, India
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[f] Department of Electronics and Communication Engineering, EGS Pillay Engineering College, Nagapattinam
Correspondence:
[*]
Corresponding author. C.R. Komala, Department of Information Science and Engineering, HKBK College of Engineering, Bangalore 560045, Karnataka, India. E-mail: [email protected].
Abstract: Internet of Things (IoT) technologies increasingly integrate unmanned aerial vehicles (UAVs). IoT devices that are becoming more networked produce massive data. The process and memory of this enormous volume of data at local nodes, particularly when utilizing artificial intelligence (AI) algorithms to collect and utilize useful information, have been declared vital issues. In this paper, we introduce UAV computing to solve greater energy consumption, delay difficulties using task offload and clustered approaches, and make cloud computing operations accessible to IoT devices. First, we present a clustering technique to group IoT devices for data transmission. After that, we apply the Q-learning approach to accomplish task offloading and allocate the difficult tasks to UAVs that are not yet fully loaded. The sensor readings from the CHs are then collected using UAV path planning. Furthermore, We use a convolutional neural network (CNN) to achieve UAV route planning. In terms of coverage ratio, clustering efficiency, UAV motion, energy consumption, and the number of collected packets, the effectiveness of the current study is finally compared with the existing techniques using UAVs. The results showed that the suggested strategy outperformed the current approaches in terms of coverage ratio, clustering efficiency, UAV motion, energy consumption, and the number of collected packets. Additionally, the proposed technique consumed less energy due to CNN-based route planning and dynamic positioning, which reduced UAV transmits power. Overall, the study concluded that the suggested approach is effective for improving energy-efficient and responsive data transmission in crises.
Keywords: UAV computing, Internet of Things, clustering, energy reduction, task offloading, and UAV path planning
DOI: 10.3233/JIFS-231242
Journal: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 1717-1730, 2023
