Fog Computing Applications in Smart Cities: A Systematic Survey

Javadzadeh, Ghazaleh

doi:10.1007/s11276-019-02208-y

Cited by 114 publications

(60 citation statements)

References 74 publications

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“…IoT critical systems need to deal with privacy, heterogeneity, and data analysis from billions of devices [45] in real-time, as well as system failures and real-time decisionmaking [45], [46]. Consequently, trustworthiness is essential in these IoT environments [47][48][49][50].…”

Section: A Trustworthiness and Dependabilitymentioning

confidence: 99%

A Survey on Trustworthiness for the Internet of Things

Ribeiro

Kamienski

2021

IEEE Access

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IoT systems use sensors to collect data from smart environments and manage resources through data analysis. An IoT system deals with many connected devices with different network and hardware constraints in a real-world scenario. An IoT system needs to handle low-latency data analysis, security threats, internal vulnerabilities, and network disconnections, which cause data loss and incorrect decisions. Trustworthiness (also known as dependability) provides various features for an IoT end-to-end data flow, such as resilience, security, availability, reliability, scalability, maintainability, heterogeneity, hardware resources management, fault management policies, and data quality. This paper presents a survey on trustworthiness and dependability in IoT systems and proposes the Trustworthiness for IoT Framework (TW-IoT) to provide trustworthiness at the data level for mist and fog-based IoT systems. The TW-IoT framework provides data trustworthiness to ensure a continuous and uninterrupted operation of IoT data flow. We also discuss challenges and trade-offs related to data trustworthiness in IoT.

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Section: A Trustworthiness and Dependabilitymentioning

confidence: 99%

A Survey on Trustworthiness for the Internet of Things

Ribeiro

Kamienski

2021

IEEE Access

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“…The dynamic threshold is modified based on the amount of dynamic background motion, as expressed in (6). Alternatively, the static threshold is not updated during the BG/FG segmentation over frame sequences.…”

Section: F Accuracy On Dynamic Threshold Versus Static Thresholdmentioning

confidence: 99%

“…The moving FG object detection has become very important research interest in the field of computer vision and image processing for many years because of its wide area of applications: object recognition, object tracking, activity recognition, video surveillance, airport and maritime monitoring, human-computer interaction, and so on [1]- [3]. Moreover, real-time moving object detection applications are used as services in cloud computing, IoT, fog computing, edge computing, smart cities, smart environment, smart home, robotics, drones, and so on [4]- [6].…”

Section: Introductionmentioning

confidence: 99%

Fast-D: When Non-Smoothing Color Feature Meets Moving Object Detection in Real-Time

2020

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The moving object detection refers to the detection of physical moving objects from a video, which is applied in video surveillance, object recognition, object counting, human-computer interaction, and so on. Moreover, nowadays, real-time moving object detection is used as services in the cloud, edge, and fog computing. However, the existing methods do not meet the trade-off between accuracy and complexity. To address these issues, we present a background subtraction-based moving object detection method, called Fast-D. In this paper, we look at the 'non-smoothing color feature' to make the moving object detection more robust in real-time. Each color feature is given equal significance during the classification of a pixel. Background model and threshold are initialized for each pixel. And then, the background model and threshold are updated dynamically when there are changes in the background of the video. Adaptive post-processing is considered to discard salt and pepper noise and fill holes in the detected moving object silhouettes. The evaluation of our proposed method on four complex datasets exhibits the significance. INDEX TERMS Real-time moving object detection, background subtraction, object segmentation, change detection.

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“…The most efficient applications that can be used for such Fig. 5 Fog computing applications in smart cities [44] computing purposes include the power school that is an oracle 12 the databases, skyward and a pro care. It is important to ensure that the operations are carried out efficiently after evaluating all the necessary networking requirements so that it does not impact the performance.…”

Section: Fog Computing In Education Iot Systemsmentioning

confidence: 99%

Utilizing technologies of fog computing in educational IoT systems: privacy, security, and agility perspective

Adel

2020

J Big Data

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Fog computing architecture is referred to the architecture that is distributed over the geographical area. This architectural arrangement mainly focuses on physical and logical network elements, and software for the purpose of implementing proper network. Fog computing architecture allows the users to have a flexible communication and also ensures that the storage services are maintained efficiently for the purpose of managing the data. However, it has been observed that in the field of education fog computing architecture has gained huge importance due to its real time application feature. The main objective of the survey is to develop a systematic literature review for the technology of fog computing in the education IoT system. The survey will also focus on evaluating the essential factors that has a crucial role in the fields of education as well as investigating the limitation and findings associated with the fog computing technologies in educational systems from the perspective of privacy, security, and agility.

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Fog Computing Applications in Smart Cities: A Systematic Survey

Cited by 114 publications

References 74 publications

A Survey on Trustworthiness for the Internet of Things

A Survey on Trustworthiness for the Internet of Things

Fast-D: When Non-Smoothing Color Feature Meets Moving Object Detection in Real-Time

Utilizing technologies of fog computing in educational IoT systems: privacy, security, and agility perspective

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