A Fog-Based DSS Model for Driving Rule Violation Monitoring Framework on the Internet of Things

Roy, Sandip; Bose, Rajesh; Sarddar, Debabrata

doi:10.14257/ijast.2015.82.03

Cited by 43 publications

(18 citation statements)

References 19 publications

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“…[6,68], where either Fog devices are connected centrally with SDNC and Cloud or interconnected with each other in a Machineto-Machine manner. To increase road safety, a Fog-based intelligent decision support driving rule violation monitoring system [69] has also been developed. The proposed system has three layers: lower, middle and upper.…”

Section: Vehicular Network and Road Safetymentioning

confidence: 99%

Fog computing security: a review of current applications and security solutions

2017

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Fog computing is a new paradigm that extends the Cloud platform model by providing computing resources on the edges of a network. It can be described as a cloud-like platform having similar data, computation, storage and application services, but is fundamentally different in that it is decentralized. In addition, Fog systems are capable of processing large amounts of data locally, operate on-premise, are fully portable, and can be installed on heterogeneous hardware. These features make the Fog platform highly suitable for time and location-sensitive applications. For example, Internet of Things (IoT) devices are required to quickly process a large amount of data. This wide range of functionality driven applications intensifies many security issues regarding data, virtualization, segregation, network, malware and monitoring. This paper surveys existing literature on Fog computing applications to identify common security gaps. Similar technologies like Edge computing, Cloudlets and Micro-data centres have also been included to provide a holistic review process. The majority of Fog applications are motivated by the desire for functionality and end-user requirements, while the security aspects are often ignored or considered as an afterthought. This paper also determines the impact of those security issues and possible solutions, providing future security-relevant directions to those responsible for designing, developing, and maintaining Fog systems.

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Section: Vehicular Network and Road Safetymentioning

confidence: 99%

Fog computing security: a review of current applications and security solutions

2017

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“…In smart grid scenario [145], the fog devices at the edge collect the data generated by grid sensors, real-time process the data, and issue control commands to the actuators. A fog-based intelligent decision support system was proposed in [147] for driver safety and traffic violation monitoring based on the IoT. A smart city speeding traffic surveillance scheme using fog computing paradigm was provided in [148], and its effectiveness was validated by intensive experiments conducted using real-world traffic surveillance video streams.…”

Section: E Fog Data Computingmentioning

confidence: 99%

Big Data Meet Cyber-Physical Systems: A Panoramic Survey

et al. 2018

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The world is witnessing an unprecedented growth of cyber-physical systems (CPS), which are foreseen to revolutionize our world via creating new services and applications in a variety of sectors such as environmental monitoring, mobilehealth systems, intelligent transportation systems and so on. The information and communication technology (ICT) sector is experiencing a significant growth in data traffic, driven by the widespread usage of smartphones, tablets and video streaming, along with the significant growth of sensors deployments that are anticipated in the near future. It is expected to outstandingly increase the growth rate of raw sensed data. In this paper, we present the CPS taxonomy via providing a broad overview of data collection, storage, access, processing and analysis. Compared with other survey papers, this is the first panoramic survey on big data for CPS, where our objective is to provide a panoramic summary of different CPS aspects. Furthermore, CPS require cybersecurity to protect them against malicious attacks and unauthorized intrusion, which become a challenge with the enormous amount of data that is continuously being generated in the network. Thus, we also provide an overview of the different security solutions proposed for CPS big data storage, access and analytics. We also discuss big data meeting green challenges in the contexts of CPS.

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“…However, there would be problems in trying to store data gathered from the sensors entirely on the cloud server. Not only would there be physical challenges such as storage capacity but there would also be temporal limitations as large amounts of data would need to be constantly exchanged [8]. Fog computing was introduced to address exactly this problem.…”

Section: Related Researches 21 Fog Computingmentioning

confidence: 99%

“…A fog-based intelligent DSS (decision support system) is a fog-based rule violation monitoring system which may be adopted in a future driver surveillance system designed to crack down on drivers that use mobile devices using driving [6,8].…”

Section: Dss [9]mentioning

confidence: 99%

Design of Safety Assurance System of Specific Vehicle Information using ABPRE in the Fog Computing Environment

2019

Teh. vjesn.

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There would be high demand for technology that monitors illegal activity and detects risk with respect to vehicles on the road in a future urban traffic system, for accident prevention. However, there may be substantial overheads from the massive amount of data that would be required for the surveillance system as it would be automatically operated and used, as further advancements are made in sensor network technology and IoT (Internet of Things). In the transportation field, systems were mainly studied to measure the driver's drowsiness or mobile phone use. These systems will collect information from several mobile devices at the same time, resulting in overhead of data. Fog computing reduces the overhead of data because it uses distributed proxy servers. However, security aspects were not considered. Encryption can be done to ensure the privacy of the information on the moving vehicles. However, in order to track a specific vehicle the data of the moving vehicles should be provided decrypted or decrypted by the secondary user. In this paper, to solve security aspects, we provide a new DSS model that utilizes ABPRE (Attribute Based Proxy Re-Encryption). The proposed system encrypts data sensed from moving vehicles. The cipher is configured to re-encrypt quickly at the proxy. The proposed method allows only users with access authority via secondary user's properties to access data with attribute-based re-encryption. Therefore, safety is secured.

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A Fog-Based DSS Model for Driving Rule Violation Monitoring Framework on the Internet of Things

Abstract: In recent days for logistics as well as common peoples of developing and developed

Cited by 43 publications

References 19 publications

Fog computing security: a review of current applications and security solutions

Fog computing security: a review of current applications and security solutions

Big Data Meet Cyber-Physical Systems: A Panoramic Survey

Design of Safety Assurance System of Specific Vehicle Information using ABPRE in the Fog Computing Environment

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