Data Dissemination in VANETs Using Clustering and Probabilistic Forwarding Based on Adaptive Jumping Multi-Objective Firefly Optimization


The dissemination of data within a VANETs network calls for a process that is extremely detailed in order to guarantee a high level of service and get rid of potentially dangerous conditions caused by congestion or a broadcast storm. Taking into account the multi-metric approaches and the inherent conflicting nature of those approaches, it is necessary to find efficient multi-objective optimization algorithms in order to handle this situation. A metaheuristic approach that takes into account a significant number of possible interactions between solutions can be used to manage an efficient optimization. For this particular objective, the meta-heuristic search algorithm known as firefly was chosen as the best option. Objective decomposition, archive management, and controlled mutation for exploration and exploitation balance are some of the developments that have been added to the firefly optimization in order to increase its capacity to find more dominant solutions. The name given to this newly developed method of multi-objective optimization is the adaptive jumping multi-objective firefly algorithm (AJ-MOFA). After that, AJ-MOFA was incorporated with a mechanism for clustering and forwarding Communications (CFM). There are three primary parts that make up this mechanism. The first is known as clustering, and it employs arbitration that is determined by the score of the cluster head. The second is a forwarding mechanism that makes use of probabilistic forwarding, and the third is known as AJ-MOFA. CFM's approach to the design of the solution space combined two variables: the first is the probability of forwarding, and the second is the maximum number of nodes that can be contained within a single cluster. The packet delivery ratio (PDR), the end-to-end delay (E2E-delay), and the number of dropped packets are the metrics that will be incorporated into the multi-objective optimizations. The results of a comparison between AJ-MOFA and CFM with benchmarks based on multi-objective optimization and NetWorking metrics reveal that both algorithms are superior in the majority of evaluation measures, which makes them promising candidates for the dissemination of data in VANETs. The results showed an accomplished PDR of 60% and an E2E delay of 6.6 seconds, while the number of dropped packets was almost nine for the entire running time of the experiment. This result compares favorably to a performance that is comparable to or lower than the benchmarks for these metrics.

Did you like this research project?

To get this research project Guidelines, Training and Code... Click Here

PROJECT TITLE : Multi-View Clustering with the Cooperation of Visible and Hidden Views ABSTRACT: The use of multi-view data in real-world applications is becoming increasingly common, and as a result, numerous multi-view clustering
PROJECT TITLE : SCHAIN-IRAM: An Efficient and Effective Semi-Supervised Clustering Algorithm for Attributed Heterogeneous Information Networks ABSTRACT: A heterogeneous information network, also known as an HIN, is a network in
PROJECT TITLE : RDMN: A Relative Density Measure Based on MST Neighborhood for Clustering Multi-Scale Datasets ABSTRACT: Techniques for discovering intrinsic clusters that are based on density do so by classifying the regions
PROJECT TITLE : Fully Dynamic kk-Center Clustering With Improved Memory Efficiency ABSTRACT: Any machine learning library worth its salt will include both static and dynamic clustering algorithms as core components. The sliding
PROJECT TITLE : ESA-Stream: Efficient Self-Adaptive Online Data Stream Clustering ABSTRACT: A wide variety of big data applications generate an enormous amount of streaming data that is high-dimensional, real-time, and constantly

Ready to Complete Your Academic MTech Project Work In Affordable Price ?

Project Enquiry