An Evolutionary GA-Based Approach for Community Detection in IoT

Abstract

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.

NALPA: A Node Ability Based Label Propagation Algorithm for Community Detection

“…We also can find that NALPA has similar efficiency with LPA, which is better than other methods. Specifically, the running time of COPRA rises more than other methods, which increases when O m ∈ [2,5] and declines or keeps steady when O m ∈ [5,8], thus, COPRA's running time is sensitive to network scale. When µ = 0.3, we can find that the running time of COPRA falls at O m = 6, which shows it converges fast under O m = 6.…”

“…Analyzing the structural feature and organizational function of complex networks is an important research area. Among different features of networks, community has received widespread attention, which is the division of a network into the groups of nodes having dense intra-connections and sparse inter-connections [2]. Overlapping nodes are shared among different communities in networks [3].…”

Improved Initialization Method for Metaheuristic Algorithms: A Novel Search Space View

“…These new successful optimizers can effectively deal with complex problems that are difficult to be solved by traditional optimization. In fact, metaheuristic optimizers have been successfully applied in engineering design [3]- [5], decision management [6], the Internet of things [7], complex network [8], job scheduling [9]- [11], genetic engineering [12], [13], biomedical [14], resource allocation and other fields.…”