Novel Fuzzy Logic Scheme for Push-Based Critical Data Broadcast Mitigation in VNDN

Khan, Saad Ahmad; Lim, Huhnkuk

doi:10.3390/s22208078

Cited by 5 publications

(4 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The authors in [75] proposed a novel fuzzy logic scheme for push-based critical data broadcast mitigation in VNDN (FLPDF) to reduce the broadcast storm in the network. This work organized vehicles into clusters via K-means clustering, and fuzzy logic is adopted for Cluster Heads (CHs) selection where each CH is responsible for sharing critical data with its cluster members.…”

Section: ) Broadcast Control Based Mobility Management Schemesmentioning

confidence: 99%

See 1 more Smart Citation

Information-Centric Mobile Networks: A Survey, Discussion, and Future Research Directions

et al. 2023

View full text Add to dashboard Cite

Information-centric networking (ICN) and its fruition, the named data networking (NDN) is a paradigm shift from host-centric address-based communication architecture to the content-centric namebased one. ICN intends to resolve various major issues faced by today's internet architecture such as privacy, security, consistent routing, and mobility, to name a few. With the massive increase of mobile data traffic in today's era, mobility is one of the major concerns in networking. On the one hand, ICN realization i.e., the NDN follows a pull-based communication model and natively supports the consumer (end-user) mobility in wired networks by maintaining the forwarding states on intermediate nodes. Nevertheless, the mobile consumer nodes confront issues in wireless networking environments such as excessive energy consumption as a result of request flooding, content retrieval delays due to intermittent connectivity, and bandwidth consumption due to the broadcasting nature of the wireless medium, among others. The producer (contentgenerator) mobility, on the other hand, was not initially supported in the original architectural design of NDN for both wired and wireless networks. Therefore, to efficiently address the degradation issues incurred by mobile consumer/producer nodes, a plethora of mobility management schemes have been proposed over the recent few years. In this paper, we provided a detailed survey on the existing research efforts-in the context of producer as well as consumer mobility, that have been proposed in the literature. Moreover, we outlined various research directions considering the role of mobility in futuristic technologies such as artificial intelligence-enabled smart networks, edge computing, software-defined networking, vehicular-fog computing, autonomous driving, and resource-constrained Internet of Things.INDEX TERMS Named data networking, mobility management, producer mobility, wireless networking, artificial intelligence, edge computing, vehicular fog computing, autonomous driving. I. INTRODUCTIONT HE 21st century is experiencing immense growth in the production of data 1 as the result of the adaptation of the Internet of Things (IoT) vision in almost every walk of life [1]- [4]. The produced data is shared by enabling the communication between two parties: 1) the producer node, which could be a resource-rich server device on the Internet or resource-constrained sensor node in wireless sensor 1 We use Data with a uppercase "D" to refer to ICN/NDN Data packet whereas data with lowercase "d" refer to general data. networks (WSNs), among others, and 2) the consumer node, which could be an end-user using a mobile application on a smartphone, browsing a website on a laptop, or a base station in the WSNs environment. Due to the heavy data production in real-time scenarios, the current internet architecture and other existing wireless networking environments such as mobile ad hoc networks (MANETs), vehicular ad hoc networks (VANETs), and WSNs, are facing significant challenges specifically when the ...

show abstract

Section: ) Broadcast Control Based Mobility Management Schemesmentioning

confidence: 99%

“…• Additional delays owing to fuzzy logic computations [75] • Novel broadcast control mechanism to control the redundant packets dissemination…”

Section: Broadcast Control Mechanismsmentioning

confidence: 99%

Information-Centric Mobile Networks: A Survey, Discussion, and Future Research Directions

et al. 2023

View full text Add to dashboard Cite

show abstract

“…The incorporation of artificial intelligence (AI) technology is causing the automotive sector to evolve quickly [1,2]. Through a yearly average growth rate of 36.15%, worldwide demand for AI in cars is predicted to increase significantly, with a projected USD 6.6 billion in sales of AI technology for cars by 2025 [3,4].…”

Section: Introductionmentioning

confidence: 99%

Object Detection for Self-Driving Car in Complex Traffic Scenarios

Das,

Agrawal

2024

MATEC Web Conf.

View full text Add to dashboard Cite

The application of convolutional neural networks (CNNs) in particular has greatly enhanced the object detection capabilities of self-driving cars, because of recent advancements in artificial intelligence (AI). However, striking a balance in vehicular settings between high precision and fast processing continues to be a persistent challenge. Developing nations such as India, possessing the second-largest global population, introduce unique intricacies to road scenarios. Numerous challenges arise on Indian roads, such as unique vehicle kinds and a variety of traffic patterns, such as auto-rickshaws, which are only seen in India. This study presents the outcomes of evaluating the YOLOv8 models, which have demonstrated superior performance in Indian traffic conditions when compared to other existing YOLO models. The examination utilized the dataset, compiled from data collected in the cities of Bangalore and Hyderabad, as well as their surrounding areas. The investigation's findings demonstrate how well the YOLOv8 models work to address the unique problems that Indian road conditions present. This study advances the development of autonomous vehicles designed for intricate traffic situations such as those found on Indian Roads.

show abstract

“…The automotive industry is undergoing a rapid transformation with the integration of artificial intelligence (AI) technologies [1,2]. The global market for AI in vehicles is projected to experience significant growth, with AI software for automobiles expected to reach USD 6.6 billion by 2025, expanding at an average annual rate of 36.15% [3,4].…”

Section: Introductionmentioning

confidence: 99%

Enhancing Object Detection in Self-Driving Cars Using a Hybrid Approach

Khan

Lee²,

Lim

2023

Electronics

Self Cite

View full text Add to dashboard Cite

Recent advancements in artificial intelligence (AI) have greatly improved the object detection capabilities of autonomous vehicles, especially using convolutional neural networks (CNNs). However, achieving high levels of accuracy and speed simultaneously in vehicular environments remains a challenge. Therefore, this paper proposes a hybrid approach that incorporates the features of two state-of-the-art object detection models: You Only Look Once (YOLO) and Faster Region CNN (Faster R-CNN). The proposed hybrid approach combines the detection and boundary box selection capabilities of YOLO with the region of interest (RoI) pooling from Faster R-CNN, resulting in improved segmentation and classification accuracy. Furthermore, we skip the Region Proposal Network (RPN) from the Faster R-CNN architecture to optimize processing time. The hybrid model is trained on a local dataset of 10,000 labeled traffic images collected during driving scenarios, further enhancing its accuracy. The results demonstrate that our proposed hybrid approach outperforms existing state-of-the-art models, providing both high accuracy and practical real-time object detection for autonomous vehicles. It is observed that the proposed hybrid model achieves a significant increase in accuracy, with improvements ranging from 5 to 7 percent compared to the standalone YOLO models. The findings of this research have practical implications for the integration of AI technologies in autonomous driving systems.

show abstract

Novel Fuzzy Logic Scheme for Push-Based Critical Data Broadcast Mitigation in VNDN

Cited by 5 publications

References 29 publications

Information-Centric Mobile Networks: A Survey, Discussion, and Future Research Directions

Information-Centric Mobile Networks: A Survey, Discussion, and Future Research Directions

Object Detection for Self-Driving Car in Complex Traffic Scenarios

Enhancing Object Detection in Self-Driving Cars Using a Hybrid Approach

Contact Info

Product

Resources

About