Keynote Speaker I
Prof. (Dr.) Sanjay Kumar Singh
Professor and Programme Director, AIITL, Amity University Uttar Pradesh, India
Bio: Dr Sanjay Kumar Singh is M.Sc., MCA, M. Tech IT and Ph.D. and is currently working as Professor and Program Director at Amity Institute of Information Technology, Amity University Uttar Pradesh India. He has a Teaching and Research experience of more than 28 Years at Post Graduate Level and Industry experience of More than 03 years including at All India Council for Technical Education (AICTE), Govt. of India. His Teaching & Research Area includes Big Data Analytics, Algorithm Analysis and Design, Advanced Numerical Techniques, and Computational Model Development. Prof. Singh has 16 Ph.D. scholars under his supervision. (Awarded 08, Ongoing 08).
He has published more than 70 Research Papers in Scopus and other Indexed Journals/UGC Approved Journals of repute/Conference proceedings. He is also Editor and Reviewer of 12 International Journals. Prof. Singh is an active member of IET (UK) and CSI, etc. He has delivered talks at various International and National forums and have been Session chairs, technical committee member for various IEEE and Elsevier conferences, and member of Board of Studies (BOS) of various government and private Universities. He is also Credited with State Scholarship Award for performance in State Board Examinations.
Speech Title: Improved RSSI-Based WSN Localization Algorithm for Coal Mine Underground Miners
Abstract: Due to the high frequency of subterranean accidents, real-time capture of underground workers’ location information is critical for the safe escape and rescue of miners. A better localization strategy is needed for this. As a result, studying the node positioning algorithm in wireless sensor networks is critical for the safe production of coal mines. We present an improved entropy-based received signal strength indicator (RSSI) localization technique for finding miners in this research. To acquire a more accurate distance, a novel RSSI value correcting model (entropy-weighted model) is proposed first, and then, genetic algorithm-based localization technique is presented to determine the location of the node of interest. MATLAB® is used to simulate the suggested technique. The simulation results confirm that the proposed algorithm can reduce the impact of bad environment factors like diffraction and multipath on the positioning process and can provide higher positioning accuracy than the conventional methods, meeting the requirements of personnel location precision in underground mining networks.
CFDSP Past Speakers
Prof. Qinmin Yang