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 perio...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.展开更多
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 sim...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.展开更多
Weed management is a crucial aspect of modern agriculture as invasive plants can negatively impact crop yields and profitability.Long-established methods of weed control,such as manual labor and synthetic herbicides,h...Weed management is a crucial aspect of modern agriculture as invasive plants can negatively impact crop yields and profitability.Long-established methods of weed control,such as manual labor and synthetic herbicides,have been widely used but come with their own set of challenges.These methods are often time-consuming,labor-intensive,and pose environmental risks.Herbicides have been the primary method of weed control due to their efficiency and cost-effectiveness.However,over-reliance on herbicides has led to environmental contamination,weed resistance,and potential health hazards.To address these issues,researchers and industry experts are now exploring the integration of machine learning into chemical weed management strategies.As technology advances,there is a growing interest in exploring innovative and sustainable weed management approaches.This review examines the potential of machine learning in chemical weed management.Machine learning offers innovative and sustainable approaches by analyzing large data sets,recognizing patterns,and making accurate predictions.Machine learning models can classify weed species and optimize herbicide usage.Real-time monitoring enables timely intervention,preventing invasive species spread.Integrating machine learning into chemical weed management holds promise for enhancing agricultural practices,reducing herbicide usage and minimizing environmental impact.Validation and refinement of these algorithms are needed for practical application.展开更多
文摘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.
文摘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.
文摘Weed management is a crucial aspect of modern agriculture as invasive plants can negatively impact crop yields and profitability.Long-established methods of weed control,such as manual labor and synthetic herbicides,have been widely used but come with their own set of challenges.These methods are often time-consuming,labor-intensive,and pose environmental risks.Herbicides have been the primary method of weed control due to their efficiency and cost-effectiveness.However,over-reliance on herbicides has led to environmental contamination,weed resistance,and potential health hazards.To address these issues,researchers and industry experts are now exploring the integration of machine learning into chemical weed management strategies.As technology advances,there is a growing interest in exploring innovative and sustainable weed management approaches.This review examines the potential of machine learning in chemical weed management.Machine learning offers innovative and sustainable approaches by analyzing large data sets,recognizing patterns,and making accurate predictions.Machine learning models can classify weed species and optimize herbicide usage.Real-time monitoring enables timely intervention,preventing invasive species spread.Integrating machine learning into chemical weed management holds promise for enhancing agricultural practices,reducing herbicide usage and minimizing environmental impact.Validation and refinement of these algorithms are needed for practical application.
