M
Md. Zakirul Alam Bhuiyan
Researcher at Fordham University
Publications - 32
Citations - 890
Md. Zakirul Alam Bhuiyan is an academic researcher from Fordham University. The author has contributed to research in topics: Wireless sensor network & Key distribution in wireless sensor networks. The author has an hindex of 14, co-authored 32 publications receiving 565 citations. Previous affiliations of Md. Zakirul Alam Bhuiyan include Hong Kong Polytechnic University & Central South University.
Papers
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Journal ArticleDOI
Data-driven dynamic clustering framework for mitigating the adverse economic impact of Covid-19 lockdown practices
Arafatur Rahman,Arafatur Rahman,Nafees Zaman,A. Taufiq Asyhari,Fadi Al-Turjman,Md. Zakirul Alam Bhuiyan,Mohamad Fadli Zolkipli +6 more
TL;DR: A data-driven dynamic clustering framework for moderating the adverse economic impact of COVID-19 flare-up is proposed and the idea can be exploited for potentially the next waves of corona virus-related diseases and other upcoming viral life-threatening calamities.
Journal ArticleDOI
Adaptive Computation Offloading With Edge for 5G-Envisioned Internet of Connected Vehicles
TL;DR: An adaptive computation offloading method, named ACOM, is devised for edge computing in 5G-envisioned IoCV to optimize the task offloading delay and resource utilization of the edge system.
Journal ArticleDOI
An intelligent/cognitive model of task scheduling for IoT applications in cloud computing environment
Sayantani Basu,Marimuthu Karuppiah,K. Selvakumar,Kuan-Ching Li,SK Hafizul Islam,Mohammad Mehedi Hassan,Md. Zakirul Alam Bhuiyan +6 more
TL;DR: A cognitive or intelligent model of bio-inspired approach is used to find the optimal solution of task scheduling for IoT applications in a heterogeneous multiprocessor cloud environment.
Proceedings ArticleDOI
Deploying Wireless Sensor Networks with Fault Tolerance for Structural Health Monitoring
TL;DR: This paper presents an approach, called FTSHM (fault tolerance in SHM), to repairing the network to guarantee a specified degree of fault tolerance and includes a SHM algorithm suitable for decentralized computing in energy-constrained WSNs, with the objective of prolonging the WSN lifetime under connectivity and data delivery constraints.
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A Deep Learning-Based Mobile Crowdsensing Scheme by Predicting Vehicle Mobility
TL;DR: This article studies how mobile vehicles can be optimally selected in order to collect maximum data from the urban environment in a future period of tens of minutes and proposes a greedy online algorithm to recruit a subset of vehicles with a limited budget for the NP-Complete problem.