N
Naveed Ul Hassan
Researcher at Lahore University of Management Sciences
Publications - 111
Citations - 2847
Naveed Ul Hassan is an academic researcher from Lahore University of Management Sciences. The author has contributed to research in topics: Smart grid & Renewable energy. The author has an hindex of 22, co-authored 97 publications receiving 2211 citations. Previous affiliations of Naveed Ul Hassan include Singapore University of Technology and Design & Supélec.
Papers
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A Survey on Radio Resource Allocation in Cognitive Radio Sensor Networks
TL;DR: A survey of the recent advances in radio resource allocation in CR sensor networks (CRSNs) is presented and an insight into the related issues and challenges is provided, and future research directions are clearly identified.
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Peak-to-Average Ratio Constrained Demand-Side Management With Consumer's Preference in Residential Smart Grid
TL;DR: Simulation results show that the proposed demand-side energy consumption schedule can provide an effective approach to reducing total energy costs while simultaneously considering PAR constraints and consumers' preferences.
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Energy Efficiency Tradeoff Mechanism Towards Wireless Green Communication: A Survey
TL;DR: A survey of different tradeoff mechanisms proposed in the literature for EE tradeoffs based on each protocol layer and its affect in the network energy efficiency is provided.
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Design of a Scalable Hybrid MAC Protocol for Heterogeneous M2M Networks
TL;DR: A scalable hybrid MAC protocol, which consists of a contention period and a transmission period, is designed for heterogeneous M2M networks and analytical and simulation results demonstrate the effectiveness of the proposed hybridMAC protocol.
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A survey of data fusion in smart city applications
Billy Pik Lik Lau,Sumudu Hasala Marakkalage,Yuren Zhou,Naveed Ul Hassan,Naveed Ul Hassan,Chau Yuen,Meng Zhang,U-Xuan Tan +7 more
TL;DR: In this article, a multi-perspectives classification of the data fusion to evaluate the smart city applications is presented, where the proposed classification is applied to evaluate selected applications in each domain of smart city and the potential future direction and challenges of data fusion integration are discussed.