Evolutionary Computing Assisted K-Means Clustering based MapReduce Distributed Computing Environment for IoT-Driven Smart City

In the last few years, the exponential rise in urban population and allied demands have alarmed governing agencies as well as industries to achieve more quality-of-service (QoS) oriented solutions to meet up-surging demands, especially towards real-time decision making, information exchange and know...

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Bibliographic Details
Published in2021 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS) pp. 192 - 200
Main Authors Srinivas, Kunal G, Hosahalli, Doreswamy
Format Conference Proceeding
LanguageEnglish
Published IEEE 19.02.2021
Subjects
BigData Analytics
Clustering
Clustering algorithms
Computational modeling
Data models
Distributed Computing
Estimation
Genetic Algorithm
Genetic algorithms
Hadoop
MapReduce
Quality of service
Smart cities
Smart City
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Summary:In the last few years, the exponential rise in urban population and allied demands have alarmed governing agencies as well as industries to achieve more quality-of-service (QoS) oriented solutions to meet up-surging demands, especially towards real-time decision making, information exchange and knowledge-driven decisions. To achieve it, smart city concept which employs Internet- of-Things (IoT), distributed software computing, and BigData analytics has gained widespread attention. Though, inclusion of QoS-sensitive routing has helped enabling better and efficient sensory or node's data collection and dissemination; however, ensuring optimal query-driven knowledge mining and information exchange has remained a challenge. Considering it as motivation, in this paper an evolutionary computing assisted K-Means clustering algorithm is developed for MapReduce computation in Hadoop distributed framework. The proposed method employs genetic algorithm to enhance centroid estimation as well as clustering, which as a result helped in achieving better clustering to support MapReduce. The proposed GA based K-Means clustering has been applied over Hadoop-MapReduce, where to achieve aforesaid centroid estimation and clustering enhancement Silhouette coefficient was used as the objective function. Here, GA-K Means was applied in such manner that it estimates optimized centroid and clusters simultaneously over Mapper and Reducer, which makes overall computation faster and more accurate.
DOI:10.1109/ICCCIS51004.2021.9397217