Fog-assisted Congestion Avoidance Scheme for Internet of Vehicles

Yaqoob, Shumayla; Ullah, Ata; Akbar, Muhammad Sajjad; Imran, Muhammad; Guizani, Mohsen

doi:10.1109/iwcmc.2018.8450402

Cited by 6 publications

(10 citation statements)

References 16 publications

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“…In vehicular networks, data management refers to the application of methods to manage data of interest to drivers, including suitable mechanisms for retrieving and transferring data, specific data filtering, the handling of data queries, data collection, and efficient data exploitation while attempting to avoid possible risks. [39][40][41][42][43][44][45][46][47][48][49][51][52][53][54][55][58][59][60][65][66][67][68] Researchers in congestion control-enabled data management domain in VANETs and IoV networks focus more on solving challenges such as improving network performance 48,60,65 , better RT 42,66 , strengthening PDR 67,68 , simplifying throughput 51,58,67,68 , better E2E delay 39,40,43,48,67,68 , increasing channel utilization 44,67 , reducing communication costs 39,40 , reducing the average WT 41,…”

Section: Data Managementmentioning

confidence: 99%

A systematic review of congestion control in internet of vehicles and vehicular ad hoc networks: Techniques, challenges, and open issues

Hemmati,

Zarei,

Rahmani

2023

Int J Communication

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SummaryThe Internet of Vehicles (IoV) integrates various technologies to enable real‐time data exchange between vehicles, pedestrians, roadside units, unmanned aerial vehicles, road‐added things, and so forth. IoV is an improved internet‐enabled vehicular ad hoc network (VANET), which is similarly affected by data traffic aggravated by time‐variant network density and the network's topological and informational changes. A high density of simultaneous vehicular communication leads to channel saturation and data congestion, which leads to rapid increases in data loss, packet latency, and lower service quality. Due to the importance of real‐time data communications in high dynamic networks, we used the systematic literature review (SLR) approach to identify 54 articles up to May 2023 to discuss the ways to deal with data traffic congestion in VANETs and IoV networks as well as existing and upcoming issues in this area.

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Section: Data Managementmentioning

confidence: 99%

A systematic review of congestion control in internet of vehicles and vehicular ad hoc networks: Techniques, challenges, and open issues

Hemmati,

Zarei,

Rahmani

2023

Int J Communication

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“…Propose a robust and distributed incentive mechanism for content caching and dissemination in a collaborative method Performance Optimization [105] Bandwidth optimization A vehicle flow model is introduced into the optimization framework [106] Congestion avoidance Present a fog-based congestion avoidance strategy [107] Data scheduling Design two dynamic scheduling algorithms to schedule data [108] Data processing Propose a cooperative fog computing mechanism for big data processing [109] Energy-efficient Integrate big data analytical into VEC nodes, there is no need to access the remote cloud for content acquiring, shortening the delivery time. The work of [100] provides a theoretical architecture to enable content delivery by balancing computation, caching and communication resources.…”

Section: Themementioning

confidence: 99%

“…The framework takes into consideration the edge resource constraints and integrates the vehicle flow model simultaneously. A fog-based congestion avoidance scheme is proposed in [106], aimed to alleviating the congestion and reducing the delay. In this scheme, a fog server is employed to conduct services in vehicular networks.…”

Section: Performance Optimizationmentioning

confidence: 99%

Vehicular Edge Computing and Networking: A Survey

et al. 2020

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As one key enabler of Intelligent Transportation System (ITS), Vehicular Ad Hoc Network (VANET) has received remarkable interest from academia and industry. The emerging vehicular applications and the exponential growing data have naturally led to the increased needs of communication, computation and storage resources, and also to strict performance requirements on response time and network bandwidth. In order to deal with these challenges, Mobile Edge Computing (MEC) is regarded as a promising solution. MEC pushes powerful computational and storage capacities from the remote cloud to the edge of networks in close proximity of vehicular users, which enables low latency and reduced bandwidth consumption. Driven by the benefits of MEC, many efforts have been devoted to integrating vehicular networks into MEC, thereby forming a novel paradigm named as Vehicular Edge Computing (VEC). In this paper, we provide a comprehensive survey of stateof-art research on VEC. First of all, we provide an overview of VEC, including the introduction, architecture, key enablers, advantages, challenges as well as several attractive application scenarios. Then, we describe several typical research topics where VEC is applied. After that, we present a careful literature review on existing research work in VEC by classification. Finally, we identify open research issues and discuss future research directions.

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“…Fog nodes act as local servers where it computes given tasks. Fog servers are also capable to process data at local servers and help to take timely actions [15], [18]. Furthermore, Fog computing reduces computation processes and user-related services and reduces the burden of traditional cloud computing data centers [19].…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Based Anomaly Detection for Fog-Assisted IoVs Network

et al. 2023

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Internet of vehicles (IoVs) allows millions of vehicles to be connected and share information for various purposes. The main applications of IoVs are traffic management, emergency messages delivery, E-health, traffic, and temperature monitoring. On the other hand, IoVs lack in location awareness and geographic distribution, which is critical for some IoVs applications such as smart traffic lights and information sharing in vehicles. To support these topographies, fog computing was proposed as an appealing and novel term, which was integrated with IoVs to extend storage, computation, and networking. Unfortunately, it is also challenged with various security and privacy hazards, which is a serious concern of smart cities. Therefore, we can formulate that Fog-assisted IoVs (Fa-IoVs), are challenged by security threats during information dissemination among mobile nodes. These security threats of Fa-IoVs are considered as anomalies which is a serious concern that needs to be addressed for smooth Fa-IoVs network communication.Here, smooth communication refers to less risk of important data loss, delay, communication overhead, etc. This research work aims to identify research gaps in the Fa-IoVs network and present a deep learningbased dynamic scheme named CAaDet (Convolutional autoencoder Aided anomaly detection) to detect anomalies. CAaDet exploits convolutional layers with a customized autoencoder for useful feature extraction and anomaly detection. Performance evaluation of the proposed scheme is done by using the F1-score metric where experiments are carried out by exploiting a benchmark dataset named NSL-KDD. CAaDet also observes the behavior of fog nodes and hidden neurons and selects the best match to reduce false alarms and improve F1-score. The proposed scheme achieved significant improvement over existing schemes for anomaly detection. Identified research gaps in Fa-IoVs can give future directions to researchers and attract more attention to this new era.

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Fog-assisted Congestion Avoidance Scheme for Internet of Vehicles

Cited by 6 publications

References 16 publications

A systematic review of congestion control in internet of vehicles and vehicular ad hoc networks: Techniques, challenges, and open issues

A systematic review of congestion control in internet of vehicles and vehicular ad hoc networks: Techniques, challenges, and open issues

Vehicular Edge Computing and Networking: A Survey

Deep Learning Based Anomaly Detection for Fog-Assisted IoVs Network

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