The Department of Computer Science and Engineering is pleased to announce an extraordinary research paper titled “A Deep Transfer Learning Model for Green Environment Security Analysis in Smart City“, authored by Dr Sambit Kumar Mishra, Assistant Professor, was published in the Journal of King Saud University – Computer and Information Sciences that falls within the Q1 quartile with an Impact Factor (IF) of 6.9. The study introduces a model to automatically classify high-resolution scene images for environmental conservation in smart cities. By enhancing the training dataset with spatial patterns, the model improves green resource management and personalised services. It also demonstrates the effectiveness of LULC classification in smart city environments using transfer learning. Data augmentation techniques improve model performance, and optimisation methods enhance efficiency, contributing to better environmental management.

Abstract

The research addresses the importance of green environmental security in smart cities and proposes a morphologically augmented fine-tuned DenseNet121 (MAFDN) model for Land Use and Land Cover (LULC) classification. This model aims to automate the categorisation of high spatial resolution scene images to facilitate green resource management and personalised services.

Dr Mishra collaborated with Dr Rasmita Dash and Madhusmita Sahu from SoA Deemed to be University, India, as well as Mamoona Humayun, Majed Alfayad, and Mohammed Assiri from universities in Saudi Arabia.

His plans include optimising the model using pruning methods to create lightweight scene classification models for resolving challenges in LULC datasets.

Link to the article

The Department of Computer Science and Engineering is thrilled to share that the paper titled, “A Total Randomized SLP Preserving Technique with Improved Privacy and Lifetime in WSNs for IoT and the Impact of Radio Range on SLP” has been published by Dr Manjula R, Assistant Professor, Department of CSE, and BTech-CSE Student Mr Tejodbhav Koduru in “Sensors“, a Q2 journal, having an Impact Factor of 3.9. Their research addresses the critical need for improved source location privacy and extended network longevity, presenting a pioneering solution known as Source Location Privacy with Enhanced Privacy and Network Lifetime (SLP-E).

Abstract

SLP-E utilises a unique combination of techniques, including a reverse random walk, a walk on annular rings, and min-hop routing, to diversify routing pathways within the network. Unlike existing SLP techniques that either prioritize privacy over network lifetime or vice versa, this approach aims to simultaneously enhance safety period, network lifetime, and privacy uniformly. Notably, this research also explores the impact of sensor radio range on Network Lifetime metrics and privacy strength within the context of SLP in WSN.

Practical Implementation/Social Implications of the research

This research holds real-world significance, especially in scenarios like protecting a lone white giraffe in Kenya fitted with a GPS tracker. Poachers pose a serious threat to such animals, hacking GPS devices to locate and harm them. This solution offers a viable approach to mitigate these threats, providing practical implications for the conservation of endangered species.

Collaborations

• Mr Tejodbhav Koduru from SRM University-AP
• Prof. Raja Datta from IIT Kharagpur
• Ms Florence Mukamanzi, Dr Damien Hanyurwimfura and Prof. Mukanyiligira Didacienne from the African Center of Excellence in the Internet of Things, University of Rwanda