Hybrid Elliptic Curve Cryptographic Approach for Data Privacy and Authentication in Secured Map Reduce Layer (SMR) for Optimized CPU Utilization

Xavier, Arogya Presskila; Kesavan, Ramesh

doi:10.1080/03772063.2022.2027285

Cited by 1 publication

(1 citation statement)

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“…the computational flow of the Reduce function is shown in Figure 3.3. HDFS is a database on the Hadoop platform in which all parameters are stored [19]. According to the above, improving QGA first requires setting a MapReduce parameter.…”

Section: Vs Model Based On Improved Qgamentioning

confidence: 99%

Design and Implementation of Vehicle Scheduling Optimization for Smart Logistics Platform Powered by Hadoop Big Data

Yu,

2023

SCPE

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E-commerce has become the mainstream consumption mode for people, and with the continuous increase of online shopping business volume, controlling logistics costs has become an urgent problem to be solved. In order to solve the challenges in vehicle scheduling and achieve a scientific and reasonable vehicle scheduling scheme, this research is based on Hadoop Big data platform, introduces the concept of time axis, and builds a vehicle scheduling model based on Hadoop intelligent platform. Based on quantum genetic algorithm and combined with MapReduce model, a quantum improved genetic algorithm is constructed to solve vehicle scheduling optimization problems. The results show that the traditional quantum genetic algorithm converges after 20 iterations, achieving an optimal value of 575 and taking 360 seconds. The improved quantum genetic algorithm converged after 10 iterations and achieved an optimal value of 675, taking 200 seconds. Compared with quantum genetic algorithm, the improved quantum genetic algorithm reduces the time spent by 44.4%. Selecting customer data from a certain logistics company for testing, the improved algorithm shortens the delivery time and achieves the design of the optimal path scheduling plan. This study optimized transportation routes and resource scheduling, reduced transportation costs, and played an important role in optimizing vehicle scheduling in the logistics industry.

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Section: Vs Model Based On Improved Qgamentioning

confidence: 99%

Design and Implementation of Vehicle Scheduling Optimization for Smart Logistics Platform Powered by Hadoop Big Data

Yu,

2023

SCPE

View full text Add to dashboard Cite

show abstract

Hybrid Elliptic Curve Cryptographic Approach for Data Privacy and Authentication in Secured Map Reduce Layer (SMR) for Optimized CPU Utilization

Cited by 1 publication

References 24 publications

Design and Implementation of Vehicle Scheduling Optimization for Smart Logistics Platform Powered by Hadoop Big Data

Design and Implementation of Vehicle Scheduling Optimization for Smart Logistics Platform Powered by Hadoop Big Data

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