Work place: Department of Electrical and Electronic Engineering, Faculty of Engineering, University of Peradeniya, Peradeniya, Sri Lanka
E-mail: maheshid@eng.pdn.ac.lk
Website:
Research Interests: Machine Learning
Biography
Maheshi B. Dissanayake received the B.Sc. Engineering degree with First Class Honors in electrical and electronic engineering from the University of Peradeniya, Sri Lanka, in 2006, and the Ph.D. in electronic engineering from the University of Surrey, U.K., in 2010. She has been a senior lecturer from 2013 -2021 and a professor from 2021 to date with the Department of Electrical and Electronic Engineering, Faculty of Engineering, University of Peradeniya. She has been a visiting research fellow at King's College London from 2015-2017. Her research interests include error correction codes, robust video communication, molecular communication, machine learning, and biomedical image analysis. She has co-authored nearly 75 conference and journal articles and has a citation record of more than 300.
By Sehan Samarakoon Maheshi B Dissanayake Kithsiri M Liyanage Sudheera Navaratne Chirantha Jayasinghe Prabhath Illangakoon
DOI: https://doi.org/10.5815/ijcnis.2024.05.07, Pub. Date: 8 Oct. 2024
A fundamental and vital aspect of Smart Metering infrastructure is the communication technologies and techniques associated with it, especially between the Smart Meters and the Data Concentrator Unit. Among many available communication technologies, ZigBee provides a low-cost, low-power, and easy-to-deploy network solution for a Smart Meter network. There exists limited literature that discusses ZigBee as a potential communication technology for long-range networks. Hence thorough analysis is demanded on the suitability of ZigBee for smart meter deployment under different types of environmental conditions, coverage ranges, and obstacles. This work evaluates the performance of an extended ZigBee module in outdoor as well as indoor conditions in the presence of different types of obstacles. Parameters are obtained for path loss exponent and the standard deviation of the Gaussian Random variable to validate the Log Normal Shadowing model for modeling long-range ZigBee communication. The impact of obstacles on path loss is also considered. The results show that the Log Normal Shadowing model is a good approximation for the behavior of ZigBee path loss. Accordingly, the suitability of ZigBee for a Smart Meter network spanned as a Neighborhood Area Network is also assessed based on the approximated model.
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