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

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.

Target Detection Improvement in Distance Measurement System Using Two Rotatable Cameras for FPSO

This paper describes target detection improvement in a distance measurement system using two rotatable cameras for floating production, storage and offloading （FPSO） facilities. The authors have developed a distance measurement system that consists of two rotatable cameras on a ship and a target on a wharf for the automatic berthing of ships. This system measures a distance by detecting and tracking the target on the wharf using the two rotatable cameras on the ship. Our goal is to apply this distance measurement system to an automatic relative positioning system for a ship at an FPSO facility. In this application, the shape of the target in the images captured by the cameras is deformed by their relative positions and attitudes, which increases the measurement errors. To solve this problem, we propose a target detection method that improves the target deformations. The proposed target detection method is able to detect the deformed targets using a target database that is created by image conversion with the perspective projection of a reference target. By using the proposed target detection method, the distance measurement error is decreased. Experimental results on a miniature scale and in an indoor environment confirmed that the measurement error of the relative distance is decreased by using the proposed target detection method.

Flag-Based Vehicular Clustering Scheme for Vehicular Ad-Hoc Networks

Clustering schemes in vehicular networks organize vehicles into logical groups.They are vital for improving network performance,accessing the medium,and enabling efficient data dissemination.Most schemes rely on periodically broadcast hello messages to provide up-to-date information about the vehicles.However,the periodic exchange of messages overwhelms the system and reduces efficiency.This paper proposes the Flag-based Vehicular Clustering(FVC)scheme.The scheme leverages a combination of Fitness Score(FS),Link Expiration Time(LET),and clustering status flags to enable efficient cluster formation in a hybrid manner.The FVC relies on the periodic broadcast of the basic safety message in the Dedicated Short-Range Communications(DSRC)standard for exchanging the vehicle’s status,FS,and joining request.Piggybacking extra information onto the existing periodic beacon reduces the overhead of exchanging additional control messages,which is the main contribution of this work.The scheme is implemented in a hybrid manner by utilizing a Road Side Unit(RSU)to implement a clustering algorithm.This work considered the FastPAM algorithm,a fast version of the Partitioning Around Medoids(PAM)clustering algorithm,to generate a list of potential cluster heads.The FVC scheme uses the LET as the clustering metric with the FastPAM algorithm.Moreover,the Lightweight FastPAM Vehicular Clustering(LFPVC)algorithm is considered by selecting the initial cluster heads based on the FS instead of the greedy FastPAM’s build stage.In the absence of the RSU,the vehicles utilize the FS with proper back-off time to self-elect the cluster head.The hybrid FVC scheme increased the cluster lifetime by 32%and reduced the control-message overhead by 63%compared to the related work.Moreover,the LFPVC algorithm achieved similar results to the FastPAM algorithm.

An Effective Classifier Model for Imbalanced Network Attack Data

Recently,machine learning algorithms have been used in the detection and classification of network attacks.The performance of the algorithms has been evaluated by using benchmark network intrusion datasets such as DARPA98,KDD’99,NSL-KDD,UNSW-NB15,and Caida DDoS.However,these datasets have two major challenges:imbalanced data and highdimensional data.Obtaining high accuracy for all attack types in the dataset allows for high accuracy in imbalanced datasets.On the other hand,having a large number of features increases the runtime load on the algorithms.A novel model is proposed in this paper to overcome these two concerns.The number of features in the model,which has been tested at CICIDS2017,is initially optimized by using genetic algorithms.This optimum feature set has been used to classify network attacks with six well-known classifiers according to high f1-score and g-mean value in minimumtime.Afterwards,amulti-layer perceptron based ensemble learning approach has been applied to improve the models’overall performance.The experimental results showthat the suggested model is acceptable for feature selection as well as classifying network attacks in an imbalanced dataset,with a high f1-score(0.91)and g-mean(0.99)value.Furthermore,it has outperformed base classifier models and voting procedures.