Lithungo Murry

About

Dr. Lithungo K. Murry is an Assistant Professor in the Department of Computer Science and Engineering at the National Institute of Technology Nagaland, India. His research focuses on the application of Artificial Intelligence (AI), wireless communication, Internet of Things (IoT), Geospatial Information Systems (GIS), and UAV-based technologies for disaster communication, landslide monitoring, and resilient infrastructure.

Dr. Murry has been involved in research projects funded by ANRF, NMHS related to landslide monitoring and early warning, disaster-resilient infrastructure, GIS-based disaster management, environmental monitoring, and intelligent sensing systems. His broader research goal is to develop low-cost, intelligent, and field-deployable technologies that support disaster preparedness, early warning, and sustainable environmental management.

Employment

• National Institute of Technology Nagaland Assistant Professor
  2013 - Present

Education

• National Institute of Technology Nagaland Ph.D.
  2014 - 2021
• Indian Institute of Technology Kharagpur M.Tech
  2009 - 2011
• National Institute of Technology Patna B.Tech
  2005 - 2009

Projects & Funding

Projects & funding information is unavailable.

Publications (17)

• LoS-aware multi-attention CSI localization for Wi-Fi-assisted UAV-based disaster scenarios
  Measurement 2026 DOI: 10.1016/j.measurement.2026.121541
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• QPSO-driven deep hybrid network for robust CSI localization in disaster scenarios
  Ad Hoc Networks 2026 DOI: 10.1016/j.adhoc.2026.104157
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• Real-Time Pineapple Bud Counting for Yield Estimation with YOLO11 and StrongSORT
  2026 IEEE International Students' Conference on Electrical, Electronics and Computer Science (SCEECS) 2026 DOI: 10.1109/sceecs68810.2026.11429802
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• A Low-Cost Adaptive Head Movement-Based User Interface for Quadriplegic Accessibility Using RNN-LSTM
  2026 DOI: 10.1007/978-981-96-7473-2_28
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• Vehicular Assistance Communication System for Road Blockage Using Multi-hop V2X Architecture in Hilly Terrain
  2026 DOI: 10.1007/978-981-96-4933-4_38
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• UAV-Assisted Victim Localization for Disaster Scenarios Using Channel-Aware Deep Learning and Swarm Optimization
  Wireless Personal Communications 2026 DOI: 10.1007/s11277-026-12131-z
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• Localization of Victims in Disaster Area Using RSSI with Machine Learning Techniques
  SN Computer Science 2025 DOI: 10.1007/s42979-025-03917-2
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• Disaster Resilience Communication Through Optimized-UAV Enabled 5G Networks with D2D Communication
  Advances in Science, Technology & Innovation 2025 DOI: 10.1007/978-3-031-82733-4_8
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• Evolutionary and Nature-Inspired Algorithms for Disaster-Resilient Networks
  Beyond Signals - Exploring Revolutionary Fourier Transform Applications 2025 DOI: 10.5772/intechopen.1006175
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• Wi-Fi-Assisted UAV-Based 3D Localization for Emergency Scenarios: Enhancing Accuracy and Coverage
  2025 IEEE International Conference on Next-Gen Technologies of Artificial Intelligence and Geoscience Remote Sensing (EarthSense) 2025 DOI: 10.1109/earthsense66084.2025.11297343
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