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Chinmaya Mahapatra
Researcher at University of British Columbia
Publications - 13
Citations - 1209
Chinmaya Mahapatra is an academic researcher from University of British Columbia. The author has contributed to research in topics: Wireless sensor network & Efficient energy use. The author has an hindex of 7, co-authored 13 publications receiving 988 citations.
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Journal ArticleDOI
Wireless energy harvesting for the Internet of Things
Pouya Kamalinejad,Chinmaya Mahapatra,Zhengguo Sheng,Shahriar Mirabbasi,Victor C. M. Leung,Yong Liang Guan +5 more
TL;DR: This article presents an overview of enabling technologies for efficient WEH, analyzes the lifetime of WEH-enabled IoT devices, and briefly study the future trends in the design of efficientWEH systems and research challenges that lie ahead.
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Recent Advances in Industrial Wireless Sensor Networks Toward Efficient Management in IoT
TL;DR: The key approach to enable efficient and reliable management of WSN within an infrastructure supporting various WSN applications and services is a cross-layer design of lightweight and cloud-based RESTful Web service.
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Energy Efficient Cooperative Computing in Mobile Wireless Sensor Networks
TL;DR: A novel approach to minimize energy consumption of processing an application in MWSN while satisfying a certain completion time requirement is proposed by introducing the concept of cooperation and shows the significant energy saving of the proposed solution.
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Energy Management in Smart Cities Based on Internet of Things: Peak Demand Reduction and Energy Savings.
TL;DR: The proposed method provides an agile, flexible and energy efficient decision making system for home energy management which is self-learning and adaptive and helps reducing the carbon footprint of residential dwellings.
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Optimal Power Control in Green Wireless Sensor Networks With Wireless Energy Harvesting, Wake-Up Radio and Transmission Control
TL;DR: A utility-lifetime maximization problem incorporating WEH, WUR, and ECC is formulated and solved using distributed dual subgradient algorithm based on the Lagrange multiplier method to improve network utility, prolong the lifetime, and pave the way for a greener WSN by reducing its carbon footprint.