Rajashekhara

Work place: Venus Technologies, Bangalore, India

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Research Interests: Computer systems and computational processes, Computational Learning Theory, Pattern Recognition, Image Processing, Data Structures and Algorithms

Biography

Dr. Rajashekhara received Ph.D. from IIT Bombay and M.Tech from University of Mysore. He has more than eighteen years of experience in Industry, research and teaching at reputed
organisations. His research and product development experience includes Communications,
Digital signal and image processing, Machine learning, Pattern recognition, Biometric
authentication and analysis, System modelling and design, Currently he is heading R&D division in Venus Technologies, Bangalore. Karnataka, India.

Author Articles
Exploring an Effectiveness & Pitfalls of Correlational-based Data Aggregation Approaches in Sensor Network

By Anand Gudnavar Rajashekhara

DOI: https://doi.org/10.5815/ijwmt.2017.02.05, Pub. Date: 8 Mar. 2017

Data aggregation is one of the core processing in wireless sensor network which ensures that environmental data being captured reaches the user via base station. In order to ensure proper data aggregation, there are many underlying principles that need more attention as compared to more frequently visited routing and energy problems. We reviewed existing data aggregation schemes with special focus on data correlation scheme and found that there is still a large scope of investigation in this area. We find that there are only less number of research publications towards existing techniques of data aggregation using correlational-based approach. It was also explored that such techniques still does not focus much on data quality, computational complexity, inappropriate benchmarking, etc. This paper elaborates about all the unsolved issues which require dedicate focus of investigation towards enhancing the data reliability and data quality in aggregation process in wireless sensor network.

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