An Evolutionary GA-Based Approach for Community Detection in IoT
Identifying traffic congestion and solving them by using predictive models has been ongoing research in intelligent transportation scenarios. However, it is improper that such scenarios can be judged on the basis of mean traffic intensity and mean traffic speed. This paper works on this aspect and u...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2019-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/8741012/ |
| _version_ | 1818642233408094208 |
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| author | Sanket Mishra Chinmay Hota Lov Kumar Abhaya Nayak |
| author_facet | Sanket Mishra Chinmay Hota Lov Kumar Abhaya Nayak |
| author_sort | Sanket Mishra |
| collection | DOAJ |
| description | Identifying traffic congestion and solving them by using predictive models has been ongoing research in intelligent transportation scenarios. However, it is improper that such scenarios can be judged on the basis of mean traffic intensity and mean traffic speed. This paper works on this aspect and uses data mining approaches to derive the aggregation metrics of traffic intensity data from the city of Madrid. This work uses a novel similarity measure by utilizing the results of the Wilcoxon Signed Rank test across 2018 locations to discover similarities. We propose a Genetic Algorithm on the results of the Wilcoxon test for forming communities based on the aggregation metrics. This work also compares and evaluates the performance of the proposed algorithm against standard distance measures and other state-of-the-art approaches. For finding the optimal number of possible communities in the data, we have taken the help of Davies - Bouldin Test. Our experimental results show the effectiveness of the Genetic Algorithm using various parameters, such as number of dissimilar points within a cluster, minimum number of dissimilar data points between clusters and overall based on Modified Silhouette coefficient. Furthermore, we find that our method is able to distribute the data points in a more uniform manner across formed communities in comparison to other approaches considered in this work. |
| first_indexed | 2024-12-16T23:39:48Z |
| format | Article |
| id | doaj.art-a7e838f7b05b45b4a2469f8274a855a5 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-16T23:39:48Z |
| publishDate | 2019-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-a7e838f7b05b45b4a2469f8274a855a52022-12-21T22:11:38ZengIEEEIEEE Access2169-35362019-01-01710051210053410.1109/ACCESS.2019.29239658741012An Evolutionary GA-Based Approach for Community Detection in IoTSanket Mishra0https://orcid.org/0000-0002-3193-8160Chinmay Hota1Lov Kumar2Abhaya Nayak3Department of Computer Science, Birla Institute of Technology and Science, Hyderabad, IndiaDepartment of Computer Science, Birla Institute of Technology and Science, Hyderabad, IndiaDepartment of Computer Science, Birla Institute of Technology and Science, Hyderabad, IndiaDepartment of Computing, Macquarie University, Sydney, NSW, AustraliaIdentifying traffic congestion and solving them by using predictive models has been ongoing research in intelligent transportation scenarios. However, it is improper that such scenarios can be judged on the basis of mean traffic intensity and mean traffic speed. This paper works on this aspect and uses data mining approaches to derive the aggregation metrics of traffic intensity data from the city of Madrid. This work uses a novel similarity measure by utilizing the results of the Wilcoxon Signed Rank test across 2018 locations to discover similarities. We propose a Genetic Algorithm on the results of the Wilcoxon test for forming communities based on the aggregation metrics. This work also compares and evaluates the performance of the proposed algorithm against standard distance measures and other state-of-the-art approaches. For finding the optimal number of possible communities in the data, we have taken the help of Davies - Bouldin Test. Our experimental results show the effectiveness of the Genetic Algorithm using various parameters, such as number of dissimilar points within a cluster, minimum number of dissimilar data points between clusters and overall based on Modified Silhouette coefficient. Furthermore, we find that our method is able to distribute the data points in a more uniform manner across formed communities in comparison to other approaches considered in this work.https://ieeexplore.ieee.org/document/8741012/Clusteringevolutionary clusteringfuzzy c-means clusteringgenetic algorithmsintelligent transportation systemsk-means clustering |
| spellingShingle | Sanket Mishra Chinmay Hota Lov Kumar Abhaya Nayak An Evolutionary GA-Based Approach for Community Detection in IoT IEEE Access Clustering evolutionary clustering fuzzy c-means clustering genetic algorithms intelligent transportation systems k-means clustering |
| title | An Evolutionary GA-Based Approach for Community Detection in IoT |
| title_full | An Evolutionary GA-Based Approach for Community Detection in IoT |
| title_fullStr | An Evolutionary GA-Based Approach for Community Detection in IoT |
| title_full_unstemmed | An Evolutionary GA-Based Approach for Community Detection in IoT |
| title_short | An Evolutionary GA-Based Approach for Community Detection in IoT |
| title_sort | evolutionary ga based approach for community detection in iot |
| topic | Clustering evolutionary clustering fuzzy c-means clustering genetic algorithms intelligent transportation systems k-means clustering |
| url | https://ieeexplore.ieee.org/document/8741012/ |
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