Markov Chain based Predictive Model for Efficient handover Management in Vehicle-to-Infrastructure Communications

Aboud, Ahmed; Touati, Haifa; Hnich, Brahim

doi:10.1109/iwcmc51323.2021.9498927

Cited by 4 publications

(3 citation statements)

References 9 publications

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“…To that end, the VC-NDN framework incorporates a new handover prediction algorithm that successfully reduces the number of unnecessary handovers by maintaining the vehicle connectivity to the base station as long as possible without degrading the network performance. Due to the lack of space, we omit the details of the proposed handover strategy, but they can be found in Aboud et al 33,34 the network. However, since vehicles and BSs can not initiate communication, meaning that data can not be sent without receiving a prior interest packet, a wide range of applications such as traffic jam notification, ads, and accident notification on the road cannot be natively supported by NDN, and additional logic should be introduced in the NDN forwarding strategy to enable these applications.…”

Section: Cluster Maintenancementioning

confidence: 99%

“…To that end, the VC‐NDN framework incorporates a new handover prediction algorithm that successfully reduces the number of unnecessary handovers by maintaining the vehicle connectivity to the base station as long as possible without degrading the network performance. Due to the lack of space, we omit the details of the proposed handover strategy, but they can be found in Aboud et al 33,34 …”

Section: The Clustering Modulementioning

confidence: 99%

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Hybrid 802.11p‐cellular architecture for NDN‐based VANET

Aboud

Touati

Hnich

2022

Int J Communication

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Vehicular network communications (VANET) face multiple challenges due to their intermittent connections and the rapid changes in their topologies. In recent years, several research efforts have explored the use of content-centric approaches to alleviate some of these challenges. One of these promising network designs is Named Data Networking (NDN), which has become a valid solution to support VANET applications. However, in the NDN architecture, the main forwarding mechanism for the interest packets is flooding. This forwarding mechanism will result in excessive collisions, which will lead to the broadcast storm problem. In this paper, we propose VC-NDN: a hybrid and hierarchical Named Data Networking architecture for VANETs. VC-NDN improves content retrieval efficiency through an adapted NDN-based communication model. VC-NDN includes a new interest forwarding scheme to reduce packet collision in the network and an efficient mechanism to support pushbased traffic. Furthermore, to reduce communication costs, VC-NDN uses two communication technologies in parallel, namely, IEEE 802.11p and cellular communications, while keeping the usage of the cellular network at a minimum level. Finally, to reduce the impact on vehicle mobility, VC-NDN follows a hierarchical clustering architecture. Specifically, a density-based clustering algorithm is designed to create and maintain stable clusters with multihop communication capability. Our performance study shows that VC-NDN outperforms the basic VNDN solutions in terms of data retrieval delay and packet delivery ratio while minimizing the usage of the cellular network.

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Section: Cluster Maintenancementioning

confidence: 99%

Section: The Clustering Modulementioning

confidence: 99%

Hybrid 802.11p‐cellular architecture for NDN‐based VANET

Aboud

Touati

Hnich

2022

Int J Communication

Self Cite

View full text Add to dashboard Cite

show abstract

“…Consequently, selecting the most suitable network for multi-RAT terminals in a vehicular scenario is essentially a complex optimization problem. So far, there have been works that focus on the network selection process by relying on fuzzy logic [15][16][17] multiple-attribute decision-making [18][19][20][21][22][23], markov chain [24], machine learning and game theory [25][26][27]13,28] techniques, taking into account many parameters obtained from the different information sources, i.e. network, mobile devices, and user preferences.…”

Section: Introductionmentioning

confidence: 99%