Adaptive k-means clustering for Flying Ad-hoc Networks

Raza, Ali; Khan, Muhammad Fahad; Maqsood, Muazzam; Haider, Bilal; Aadil, Farhan

doi:10.3837/tiis.2020.06.019

Cited by 8 publications

(3 citation statements)

References 26 publications

(36 reference statements)

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“…Furthermore, the work in [30] formulated a Maximum Stable Set Problem (MSSP) using the vehicles' similarity scores and solving it with the Continuous Hopfield Network (CHN) to estimate the K number and select the initial medoids. For single-hop clustering, the authors in reference [31] stated that the covered geography and transmission range are inversely proportional to the cluster size. Therefore, they proposed a brute force method to learn the K number by randomly selecting a CH, assigning the in-range vehicles to it, and then repeating the process for the remaining unassigned vehicles.…”

Section: Background and Related Workmentioning

confidence: 99%

Examining the Use of Scott’s Formula and Link Expiration Time Metric for Vehicular Clustering

Samann

2024

CMES

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Implementing machine learning algorithms in the non-conducive environment of the vehicular network requires some adaptations due to the high computational complexity of these algorithms. K-clustering algorithms are simplistic, with fast performance and relative accuracy. However, their implementation depends on the initial selection of clusters number (K), the initial clusters' centers, and the clustering metric. This paper investigated using Scott's histogram formula to estimate the K number and the Link Expiration Time (LET) as a clustering metric. Realistic traffic flows were considered for three maps, namely Highway, Traffic Light junction, and Roundabout junction, to study the effect of road layout on estimating the K number. A fast version of the PAM algorithm was used for clustering with a modification to reduce time complexity. The Affinity propagation algorithm sets the baseline for the estimated K number, and the Medoid Silhouette method is used to quantify the clustering. OMNET++, Veins, and SUMO were used to simulate the traffic, while the related algorithms were implemented in Python. The Scott's formula estimation of the K number only matched the baseline when the road layout was simple. Moreover, the clustering algorithm required one iteration on average to converge when used with LET.

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Section: Background and Related Workmentioning

confidence: 99%

Examining the Use of Scott’s Formula and Link Expiration Time Metric for Vehicular Clustering

Samann

2024

CMES

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“…This research proposes a mechanism whose performance is assessed by contrasting it with three topology construction methods, K-means [13] , DCM [14] and MMF [15] .…”

Section: Performance Analysismentioning

confidence: 99%

Distributed topology control based on reinforcement learning in unmanned aerial vehicles networks

Fang,

Kuang,

Zhang

et al. 2024

International Conference on Frontiers of Applied Optics and Computer Engineering (AOCE 2024)

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The fast development and restricted energy qualities of Unmanned Aerial Vehicles (UAVs) lead to visit changes in the topology of UAV networks, to adapt to this present circumstance, this paper proposes a distributed topology control based on reinforcement learning in unmanned aerial vehicles networks. Neighbor discovery, Election of cluster heads, establishment of clusters, and cluster maintenance are carried out based on parameters such as UAV survival time, connectivity and link stability time. Reinforcement learning method is used to determine the optimal cluster reconfiguration strategy. For every parameter used in CH election, a set of appropriate weights is determined by the maintenance approach to preserve a stable network topology and extend the network lifetime. The strategies are rewarded based on how well they perform on the network. The mechanism proposed in this research was assessed by contrasting it with three topology construction methods, K-means, DCM and MMF. The results show that the method in this paper is applicable to UAV networks with different degrees of improvement in topology maintenance time, packet delivery ratio, end-to-end delay, network throughput, and average node survival time.

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“…And we cluster services with no reduce the dimension of user side. K-means algorithm is interpretable and easy to implement, so we use K-means algorithm to cluster users and services [25]. However, it is well known that the clustering results of K-means will be different each time [26].…”

Section: Modeling User Groups and Service Groupsmentioning

confidence: 99%

An expanded Matrix Factorization model for real-time Web service QoS prediction

2021

KSII TIIS

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Real-time prediction of Web service of quality (QoS) provides more convenience for web services in cloud environment, but real-time QoS prediction faces severe challenges, especially under the cold-start situation. Existing literatures of real-time QoS predicting ignore that the QoS of a user/service is related to the QoS of other users/services. For example, users/services belonging to the same group of category will have similar QoS values. All of the methods ignore the group relationship because of the complexity of the model. Based on this, we propose a real-time Matrix Factorization based Clustering model (MFC), which uses category information as a new regularization term of the loss function. Specifically, in order to meet the real-time characteristic of the real-time prediction model, and to minimize the complexity of the model, we first map the QoS values of a large number of users/services to a lower-dimensional space by the PCA method, and then use the K-means algorithm calculates user/service category information, and use the average result to obtain a stable final clustering result. Extensive experiments on real-word datasets demonstrate that MFC outperforms other state-of-the-art prediction algorithms.

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Adaptive k-means clustering for Flying Ad-hoc Networks

Cited by 8 publications

References 26 publications

Examining the Use of Scott’s Formula and Link Expiration Time Metric for Vehicular Clustering

Examining the Use of Scott’s Formula and Link Expiration Time Metric for Vehicular Clustering

Distributed topology control based on reinforcement learning in unmanned aerial vehicles networks

An expanded Matrix Factorization model for real-time Web service QoS prediction

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