Toward Offloading Internet of Vehicles Applications in 5G Networks

Wan, Shaohua; Gu, Renhao; Umer, Tariq; Salah, Khaled; Xu, Xiaolong

doi:10.1109/tits.2020.3017596

Cited by 64 publications

(25 citation statements)

References 39 publications

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“…In order to effectively solve the channel interference problem caused by exposed nodes in DANET, the channel allocation control algorithm with power control can be used to dynamically negotiate the channel to carry out adaptive channel allocation. It can realize multiple communications of different channels in the same area, reduce the negative effect caused by the exposed node problem, and improve the capacity of the network [70,71].…”

Section: Wireless Communications and Mobile Computingmentioning

confidence: 99%

Dispersed Computing for Tactical Edge in Future Wars: Vision, Architecture, and Challenges

Yang

Sun

et al. 2021

Wireless Communications and Mobile Computing

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In the future, the tactical edge is far away from the command center, the resources of communication and computing are limited, and the battlefield situation is changing rapidly, which leads to the weak connection and fast changes of network topology in a harsh and complex battlefield environment. Thus, to meet the needs of communication and computing to build a new generation of computing architecture for real-time sharing and service collaboration of tactical edge resources to win the future war, the dispersed computing (DCOMP) seeks a new solution to satisfy the requirements of fast and efficient sensing, transmission, integrating, scheduling, and processing of various information in the tactical edge. Through the research of a traditional computing paradigm of mobile cloud computing (MCC), fog computing (FC), mobile edge computing (MEC), mobile ad hoc network (MANET), etc., it can be found that these computations have difficulty in meeting the high changing and complex battlefield environment and we propose a novel architecture of DCOMP to build a scalable, extensible, and robust decision-making system, to realize powerful and secure communication, computing, storage, and information processing capabilities for the tactical edge. We illustrate the fundamental principles of building a network model, channel allocation, and forwarding control mechanism of the network architecture for DCOMP called DANET and then design a new architecture, programming model, task awareness, and computing scheduling for DCOMP. Finally, we discuss the main requirements and challenges of DCOMP in future wars.

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Section: Wireless Communications and Mobile Computingmentioning

confidence: 99%

Dispersed Computing for Tactical Edge in Future Wars: Vision, Architecture, and Challenges

Yang

Sun

et al. 2021

Wireless Communications and Mobile Computing

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“…Finally, we present results where MADCaDPAL is in use. Figures 9,10,11,12 show the average energy consumption of all significant nodes. This is presented alongside the maximum energy consumption of significant nodes, this being the single significant node to consume the most energy.…”

Section: Simulation Environment and Parametersmentioning

confidence: 99%

“…The IoT has moved into mobile environments in recent times [8][9][10], however, when considering a WSN as a static implementation, another issue arises. Due to the multi-hop to sink basis of WSNs they are prone to nodes closest to the sink taking on greater load and subsequently running out of energy faster.…”

Section: Introductionmentioning

confidence: 99%

A mobility aware duty cycling and preambling solution for wireless sensor network with mobile sink node

et al. 2021

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Utilising the mobilisation of a sink node in a wireless sensor network to combat the energy hole, or hotspot issue, is well referenced. However, another issue, that of energy spikes may remain. With the mobile sink node potentially communicating with some nodes more than others. In this study we propose the Mobility Aware Duty Cycling and Dynamic Preambling Algorithm (MADCaDPAL). This algorithm utilises an existing solution where a communication threshold is built between a mobile sink node using predictable mobility and static nodes on its path. MADCaDPAL bases decisions relating to node sleep function, moving to clear channel assessment and the subsequent sending of preambles on the relation between the threshold built by the static node and the position of the mobile sink node. MADCaDPAL achieves a reduction in average energy consumption of up to 80%, this when used in conjunction with a lightweight carrier-sense multiple access based MAC implementation. Maximum energy consumption amongst individual nodes is also brought closer to the average, reducing energy spikes and subsequently improving network lifetime. Additionally, frame delivery to the sink is improved overall.

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“…Therefore, this paper used PCA to reduce the dimension of the feature parameters. On the basis of retaining the original feature parameter information, reduce the dimension of the parameters [31]. The PCA algorithm is used to reduce the dimensionality of five feature parameters of crossing intention, and the correlation between the variables and the principal components is shown in Table 1.…”

Section: Principal Component Analysis (Pca)mentioning

confidence: 99%

An Online Semisupervised Learning Model for Pedestrians’ Crossing Intention Recognition of Connected Autonomous Vehicle Based on Mobile Edge Computing Applications

Peng

Zhang

et al. 2021

Wireless Communications and Mobile Computing

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One of the major challenges that connected autonomous vehicles (CAVs) are facing today is driving in urban environments. To achieve this goal, CAVs need to have the ability to understand the crossing intention of pedestrians. However, for autonomous vehicles, it is quite challenging to understand pedestrians’ crossing intentions. Because the pedestrian is a very complex individual, their intention to cross the street is affected by the weather, the surrounding traffic environment, and even his own emotions. If the established street crossing intention recognition model cannot be updated in real time according to the diversity of samples, the efficiency of human-machine interaction and the interaction safety will be greatly affected. Based on the above problems, this paper established a pedestrian crossing intention model based on the online semisupervised support vector machine algorithm (OS3VM). In order to verify the effectiveness of the model, this paper collects a large amount of pedestrian crossing data and vehicle movement data based on laser scanner, and determines the main feature components of the model input through feature extraction and principal component analysis (PCA). The comparison results of recognition accuracy of SVM, S3VM, and OS3VM indicate that the proposed OS3VM model exhibits a better ability to recognize pedestrian crossing intentions than the SVM and S3VM models, and the accuracy achieves 94.83%. Therefore, the OS3VM model can reduce the number of labeled samples for training the classifier and improve the recognition accuracy.

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Toward Offloading Internet of Vehicles Applications in 5G Networks

Cited by 64 publications

References 39 publications

Dispersed Computing for Tactical Edge in Future Wars: Vision, Architecture, and Challenges

Dispersed Computing for Tactical Edge in Future Wars: Vision, Architecture, and Challenges

A mobility aware duty cycling and preambling solution for wireless sensor network with mobile sink node

An Online Semisupervised Learning Model for Pedestrians’ Crossing Intention Recognition of Connected Autonomous Vehicle Based on Mobile Edge Computing Applications

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