Satellite Imaging Based Risk Management in Cloud IoT Network Using Machine Learning Techniques

 

 

 

Jyotsnarani Tripathy1, T. Krishna Murthy2, S. Manjula3, Sukanya Ledalla4, Alla Rajendra5,
P. Lakshmi Harika6, K Boopathy7

 

1Assistant Professor, Department of AIML & IoT, VNR Vignana Jyothi Institution of Engineering and Technology, Hyderabad, Telangana, India

 

2Assistant Professor, Department of Computer Science and Engineering, Mallareddy University, Hyderabad, India

 

3Assistant Professor (Sr Gr), Department of Computer Science and Business Systems, Nehru Institute of Engineering and Technology, Coimbatore, India

 

4Assistant Professor, Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Bowrampet, Hyderabad-500043, Telangana, India

 

5Assistant Professor, Department of Computer Science and Engineering, Aditya University, Surampalem, India

 

6Assistant Professor, Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Hyderabad, Telangana, India

 

7Department of Electrical and Electronics Engineering, Aarupadai veedu Institute of Technology,Vinayaka Missions Research Foundation(DU), Chennai Campus, Paiyanoor:603104, India

 

Text Box: Abstract The consistent improvement of remote sensing (RS) technology has resulted in an easy access to a large volume of satellite imagery. There is a need for effective and scalable solutions for widening the application of RS in different fields and making it work efficiently in practical situations. This research propose novel technique in satellite image gathering and cloud IoT network risk management using machine-learning model. Here the cloud IoT network has been used in satellite image collection and this network security analysis has been carried out using secure trust based cryptographic blockchain model. Then this collected image has been classified using convolutional bayes fuzzy markov perceptron basis function model. Experimental analysis has been carried out in terms of accuracy, QoS, recall, latency, scalability. Proposed model attained accuracy of 97%, QoS of 94%, LATENCY of 96%, Scalability of 95%, RECALL of 93%. These results assist decision-makers, planners, and scientists studying remote sensing select an appropriate image classification system for tracking a dynamic, fragmented, and varied landscape. Emails: jtjyotsna@gmail.com; thaticherla.krishnamurthy@mallareddyuniversity.ac.in; manjulapmu@gmail.com; ledalla.sukanya@gmail.com; rajendracivil127@gmail.com; pothuri.harika99@gmail.com; boopathyk@avit.ac.in

 

Received: February 19, 2025 Revised: June 02, 2025 Accepted: July 03, 2025

 

Keywords: Cloud IoT network; Risk management; Machine learning model; Satellite image; Cryptographic blockchain