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 Energy Efficient Clustering Scheme for Data Aggregation in Wireless Sensor Networks

In wireless sensor networks, a clustering scheme is helpful in reducing the energy consumption by aggregating data at intermediate sensors. This paper discusses the important issue of energy optimization in hierarchically-clustered wireless sensor networks to minimize the total energy consumption required to collect data. We propose a comprehensive energy consumption model for multi-tier clustered sensor networks, in which all the energy consumptions not only in the phase of data transmissions but also in the phase of cluster head rotations are taken into account. By using this new model, we are able to obtain the solutions of optimal tier number and the resulted optimal clustering scheme on how to group all the sensors into tiers by the suggested numerical method. This then enables us to propose an energy-efficiency optimized distributed multi-tier clustering algorithm for wireless sensor networks. This algorithm is theoretically analyzed in terms of time complexity. Simulation results are provided to show that, the theoretically calculated energy consumption by the new model matches very well with the simulation results, and the energy consumption is indeed minimized at the optimal number of tiers in the multi-tier clustered wireless sensor networks.

Unstructured Oncological Image Cluster Identification Using Improved Unsupervised Clustering Techniques

This paper presents,a new approach of Medical Image Pixels Clustering(MIPC),aims to trace the dissimilar patterns over the Magnetic Resonance(MR)image through the process of automatically identify the appropriate number of distinct clusters based on different improved unsupervised clustering schemes for enrichment,pattern predication and deeper investigation.The proposed MIPC consists of two stages:clustering and validation.In the clustering stage,the MIPC automatically identifies the distinct number of dissimilar clusters over the gray scale MR image based on three different improved unsupervised clustering schemes likely improved Limited Agglomerative Clustering(iLIAC),Dynamic Automatic Agglomerative Clustering(DAAC)and Optimum N-Means(ONM).In the second stage,the performance of MIPC approach is estimated by measuring Intra intimacy and Intra contrast of each individual cluster in the result of MR image based on proposed validation method namely Shreekum Intra Cluster Measure(SICM).Experimental results showthat the MIPC approach is better suited for automatic identification of highly relative dissimilar clusters over the MR cancer images with higher Intra closeness and lower Intra contrast based on improved unsupervised clustering schemes.

Clustered trajectories anonymity in wireless sensor networks

This paper proposes a clustered trajectories anonymity scheme （CTA） that enhances the kano nymity scheme to provide the intended level of source location privacy in mobile event monitoring when a global attacker is assumed. CTA applies isomorphic property of rotation to create traces of the fake sources distributions which are similar to those of the real sources. Thus anonymity of each trajectory and that of the clustered is achieved. In addition, location kdiversity is achieved by dis tributing fake sources around the base station. To reduce the time delay, tree rooted at the base sta tion is constructed to overlap part of the beacon interval of the nodes in the hierarchy. Both the ana lytical analysis and the simulation results prove that proved energy overhead and time delay. our scheme provides perfect anonymity with improved energy overhead and time delay.

Routing strategy of reducing energy consumption for underwater data collection

Underwater Wireless Sensor Networks(UWSNs)are widely used in many fields,such as regular marine monitoring and disaster warning.However,UWSNs are still subject to various limitations and challenges:ocean interferences and noises are high,bandwidths are narrow,and propagation delays are high.Sensor batteries have limited energy and are difficult to be replaced or recharged.Accordingly,the design of routing protocols is one of the solutions to these problems.Aiming at reducing and balancing network energy consumption and effectively extending the life cycle of UWSNs,this paper proposes a Hierarchical Adaptive Energy-efficient Clustering Routing(HAECR)strategy.First,this strategy divides hierarchical regions based on the depth of the sensor node in a three-dimensional(3D)space.Second,sensor nodes form different competition radii based on their own relevant attributes and remaining energy.Nodes in the same layer compete freely to form clusters of different sizes.Finally,the transmission path between clusters is determined according to comprehensive factors,such as link quality,and then the optimal route is planned.The simulation experiment is conducted in the monitoring range of the 3D space.The simulation results prove that the HAECR clustering strategy is superior to LEACH and UCUBB in terms of balancing and reducing energy consumption,extending the network lifetime,and increasing the number of data transmissions.