Research and Application of Intelligent Internet of Vehicles Model Based on Fog Computing

Qin, Baoling; Cai, Jiejian; Luo, Yunshi; Zheng, Fenglin; Zhang, Jianwei; Luo, Qiao

doi:10.1109/itnec.2019.8729045

Cited by 7 publications

(2 citation statements)

References 2 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The network architecture based on fog computing is a scattered network, which can process data at the fog node to reduce the computing and storage burden of cloud computing. 32 However, the fog server has limited computing and storage capabilities. 33 Therefore, when a large amount of data needs to be calculated and stored, the data to be processed will be uploaded from the fog layer to the cloud layer and processed by the cloud server.…”

Section: Cloud Computing and Fog Computingmentioning

confidence: 99%

Privacy‐preserving cloud‐fog–based traceable road condition monitoring in VANET

Wang

et al. 2020

Int J Network Mgmt

View full text Add to dashboard Cite

With the development of intelligent vehicles, the research on road condition monitoring has attracted much attention in the vehicular ad hoc network (VANET). The combination of VANET, cloud computing, and fog computing provides on-demand computational resources while creating a lot of new challenges. In this paper, we propose an efficient privacy-preserving cloud-fog-based traceable road surface condition monitoring scheme. We use certificateless aggregate signcryption technology to implement multiple messages aggregation verification, which greatly saves computing resources and bandwidth. Moreover, we also use a traceable vehicle pseudo identity generated by a trace authority (TRA) to achieve identity privacy protection. To ensure the privacy of the fog and cloud server, the road condition information is reported in ciphertext format. In addition, the cloud server can perform ciphertext equivalence test operation to distinguish different road condition information of the same area without compromising the confidentiality. The ciphertext, which exceeds the set threshold, is uploaded to the blockchain. It can be stored permanently and never be tampered with or deleted. Finally, we demonstrate the correctness of the proposed scheme and show that the proposed scheme has higher efficiency.

show abstract

Section: Cloud Computing and Fog Computingmentioning

confidence: 99%

Privacy‐preserving cloud‐fog–based traceable road condition monitoring in VANET

Wang

et al. 2020

Int J Network Mgmt

View full text Add to dashboard Cite

show abstract

“…Massive amount of data will be produced by the exponential growth of IoT (edge) devices located nearby from users. As a result of that and the remote location of cloud servers from edge devices, several challenges appear in IoT systems, including network congestion, data loss, and higher latency [6]. Thus, a computing paradigm called Fog Computing (FC) was proposed to process data generated from IoT (edge) devices in real-time [5].…”

Section: Introductionmentioning

confidence: 99%

Dynamic Fault Tolerance Aware Scheduling for Healthcare System on Fog Computing

Rajab

Younis

2021

eijs

View full text Add to dashboard Cite

Internet of Things (IoT) contributes to improve the quality of life as it supports many applications, especially healthcare systems. Data generated from IoT devices is sent to the Cloud Computing (CC) for processing and storage, despite the latency caused by the distance. Because of the revolution in IoT devices, data sent to CC has been increasing. As a result, another problem added to the latency was increasing congestion on the cloud network. Fog Computing (FC) was used to solve these problems because of its proximity to IoT devices, while filtering data is sent to the CC. FC is a middle layer located between IoT devices and the CC layer. Due to the massive data generated by IoT devices on FC, Dynamic Weighted Round Robin (DWRR) algorithm was used, which represents a load balancing (LB) algorithm that is applied to schedule and distributes data among fog servers by reading CPU and memory values of these servers in order to improve system performance. The results proved that DWRR algorithm provides high throughput which reaches 3290 req/sec at 919 users. A lot of research is concerned with distribution of workload by using LB techniques without paying much attention to Fault Tolerance (FT), which implies that the system continues to operate even when fault occurs. Therefore, we proposed a replication FT technique called primary-backup replication based on dynamic checkpoint interval on FC. Checkpoint was used to replicate new data from a primary server to a backup server dynamically by monitoring CPU values of primary fog server, so that checkpoint occurs only when the CPU value is larger than 0.2 to reduce overhead. The results showed that the execution time of data filtering process on the FC with a dynamic checkpoint is less than the time spent in the case of the static checkpoint that is independent on the CPU status.

show abstract