Self-Adaptive Clustering and Load-Bandwidth Management for Uplink Enhancement in Heterogeneous Vehicular Networks

Wang, Tianyu; Cao, Xun

doi:10.1109/jiot.2019.2904036

Cited by 17 publications

(7 citation statements)

References 21 publications

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“…That is, the number of home points will affect the spatial distribution of mobile nodes. Motivated by this fact, clustering behavior has been found in [32,33,38] based on the long-term observations. In our work, we introduce the clustered model combined with the distribution of home points.…”

Section: Mobility Modelmentioning

confidence: 94%

Scaling Performance Analysis and Optimization Based on the Node Spatial Distribution in Mobile Content‐Centric Networks

Ren

Zhang

et al. 2021

Wireless Communications and Mobile Computing

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Content-centric networks (CCNs) have become a promising technology for relieving the increasing wireless traffic demands. In this paper, we explore the scaling performance of mobile content-centric networks based on the nonuniform spatial distribution of nodes, where each node moves around its own home point and requests the desired content according to a Zipf distribution. We assume each mobile node is equipped with a finite local cache, which is applied to cache contents following a static cache allocation scheme. According to the nonuniform spatial distribution of cache-enabled nodes, we introduce two kinds of clustered models, i.e., the clustered grid model and the clustered random model. In each clustered model, we analyze throughput and delay performance when the number of nodes goes infinity by means of the proposed cell-partition scheduling scheme and the distributed multihop routing scheme. We show that the node mobility degree and the clustering behavior play the fundamental roles in the aforementioned asymptotic performance. Finally, we study the optimal cache allocation problem in the two kinds of clustered models. Our findings provide a guidance for developing the optimal caching scheme. We further perform the numerical simulations to validate the theoretical scaling laws.

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Section: Mobility Modelmentioning

confidence: 94%

Scaling Performance Analysis and Optimization Based on the Node Spatial Distribution in Mobile Content‐Centric Networks

Ren

Zhang

et al. 2021

Wireless Communications and Mobile Computing

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“…Ahmad et al (2020) presented a good example of a heterogeneous vehicular network, in which a fullyfledged game-theoretic mechanism was proposed for cooperation among vehicles within the cluster. Wang TY et al (2019) proposed heterogeneous vehicular networks, in which DSRC, LTE, and vehicle-toeverything were integrated. A clustering approach was adopted, named self-adopting clustering, based on an iterative self-organizing data analysis algorithm, targeting at multiple clusters at the same time in a wide coverage area.…”

Section: Related Workmentioning

confidence: 99%

A cooperative heterogeneous vehicular clustering framework for efficiency improvement

Ahmad

Noor

Ahmed³

et al. 2021

Front Inform Technol Electron Eng

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Heterogeneous vehicular clustering integrates multiple types of communication networks to work efficiently for various vehicular applications. One popular form of heterogeneous network is the integration of long-term evolution (LTE) and dedicated short-range communication. The heterogeneity of such a network infrastructure and the non-cooperation involved in sharing cost/data are potential problems to solve. A vehicular clustering framework is one solution to these problems, but the framework should be formally verified and validated before being deployed in the real world. To solve these issues, first, we present a heterogeneous framework, named destination and interest-aware clustering, for vehicular clustering that integrates vehicular ad hoc networks with the LTE network for improving road traffic efficiency. Then, we specify a model system of the proposed framework. The model is formally verified to evaluate its performance at the functional level using a model checking technique. To evaluate the performance of the proposed framework at the micro-level, a heterogeneous simulation environment is created by integrating state-of-the-art tools. The comparison of the simulation results with those of other known approaches shows that our proposed framework performs better.

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“…Meeting the communication requirements of the ITS Proposing a heterogeneous vehicular network with V2I and V2V communications Network clustering and switching [24] Data collection Guaranteeing QoS and reduce cellular bandwidth cost Proposing a one-hop clustering and data delivery optimization approach [25] Vehicular communication Providing high reliability, low latency, and wide area coverage Proposing a self-adaptive clustering method to adjust clusters [26] Vehicular networks Lin et al [19] proposed a software-defined networking (SDN) enabled vehicular network architecture which divided the networks into three layers to improve network management capacity. In [20], the authors proposed a cognitive radio (CR)-based architecture for in-vehicle networks to employ CR technology to alleviate spectrum resource shortages in the presence of highly dynamic typologies and time-varying spectrum utilization.…”

Section: Improving Reliability Performance Of Heterogeneous Vehicular Communicationsmentioning

confidence: 99%

“…Qi et al [24] proposed a traffic differentiated clustering routing mechanism for vehicular data collection to reduce cellular bandwidth cost in a heterogeneous network, which was compose of DSRC and cellular vehicle-to-everything. To achieve high reliability, low latency, and wide-area coverage communication, the authors in [25] proposed an adaptive clustering method, which utilized DSRC and the cellular network to provide intra-cluster V2V communication and V2I communication outside the cluster, respectively. In [26], the authors proposed a section-based cluster mechanism, which clustered vehicles based on road sections and selected the vehicle closest to the cluster center as the CH, ignoring the driving stability of vehicles.…”

Section: Network Clustering and Switching In Iovmentioning

confidence: 99%

An Intelligent Multimode Clustering Mechanism Using Driving Pattern Recognition in Cognitive Internet of Vehicles

Chang

Ning

2021

Sensors

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Connected autonomous vehicles can leverage communication and artificial intelligence technologies to effectively overcome the perceived limitations of individuals and enhance driving safety and stability. However, due to the high dynamics of the vehicular network and frequent interruptions and handovers, it is still challenging to provide stable communication connections between vehicles, which is likely to cause disasters. To address this issue, in this paper, we propose an intelligent clustering mechanism based on driving patterns in heterogeneous Cognitive Internet of Vehicles (CIoVs). In the proposed approach, we analyze the driving mode containing multiple feature parameters to accurately capture the driving characteristics. To ensure the accuracy of pattern recognition, a genetic algorithm-based neural network pattern recognition algorithm is proposed to support the reliable clustering of connected autonomous vehicles. The cognitive engines recognize the driving modes to group vehicles with a similar driving mode into a relatively stable cluster. In addition, we formulate the stability and survival time of clusters and analyze the communication performance of the clustering mechanism. Simulation results show that the proposed mechanism improves the reliable communication throughput and average cluster lifetime by approximately 14.4% and 11.5% respectively compared to the state-of-the-art approaches.

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Self-Adaptive Clustering and Load-Bandwidth Management for Uplink Enhancement in Heterogeneous Vehicular Networks

Cited by 17 publications

References 21 publications

Scaling Performance Analysis and Optimization Based on the Node Spatial Distribution in Mobile Content‐Centric Networks

Scaling Performance Analysis and Optimization Based on the Node Spatial Distribution in Mobile Content‐Centric Networks

A cooperative heterogeneous vehicular clustering framework for efficiency improvement

An Intelligent Multimode Clustering Mechanism Using Driving Pattern Recognition in Cognitive Internet of Vehicles

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