| 1. |
|
|
| 2. |
- Alsayfi, Majed S., et al.
(författare)
-
Big Data in Vehicular Cloud Computing : Review, Taxonomy, and Security Challenges
- 2022
-
Ingår i: ELEKTRONIKA IR ELEKTROTECHNIKA. - : Kaunas University of Technology (KTU). - 1392-1215 .- 2029-5731. ; 28:2, s. 59-71
-
Forskningsöversikt (refereegranskat)abstract
- Modern vehicles equipped with various smart sensors have become a means of transportation and have become a means of collecting, creating, computing, processing, and transferring data while traveling through modern and rural cities. A traditional vehicular ad hoc network (VANET) cannot handle the enormous and complex data that are collected by modern vehicle sensors (e.g., cameras, lidar, and global positioning systems (GPS)) because they require rapid processing, analysis, management, storage, and uploading to trusted national authorities. Furthermore, the integrated VANET with cloud computing presents a new concept, vehicular cloud computing (VCC), which overcomes the limitations of VANET, brings new services and applications to vehicular networks, and generates a massive amount of data compared to the data collected by individual vehicles alone. Therefore, this study explored the importance of big data in VCC. First, we provide an overview of traditional vehicular networks and their limitations. Then we investigate the relationship between VCC and big data, fundamentally focusing on how VCC can generate, transmit, store, upload, and process big data to share it among vehicles on the road. Subsequently, a new taxonomy of big data in VCC was presented. Finally, the security challenges in big data-based VCCs are discussed.
|
|
| 3. |
- Alsayfi, Majed S., et al.
(författare)
-
Securing Real-Time Video Surveillance Data in Vehicular Cloud Computing : A Survey
- 2022
-
Ingår i: IEEE Access. - : Institute of Electrical and Electronics Engineers (IEEE). - 2169-3536. ; 10, s. 51525-51547
-
Tidskriftsartikel (refereegranskat)abstract
- Vehicular ad hoc networks (VANETs) have received a great amount of interest, especially in wireless communications technology. In VANETs, vehicles are equipped with various intelligent sensors that can collect real-time data from inside and from surrounding vehicles. These real-time data require powerful computation, processing, and storage. However, VANETs cannot manage these real-time data because of the limited storage capacity in on board unit (OBU). To address this limitation, a new concept is proposed in which a VANET is integrated with cloud computing to form vehicular cloud computing (VCC) technology. VCC can manage real-time services, such as real-time video surveillance data that are used for monitoring critical events on the road. These real-time video surveillance data include highly sensitive data that should be protected against intruders in the networks because any manipulation, alteration, or sniffing of data will affect a driver's life by causing improper decision-making. The security and privacy of real-time video surveillance data are major challenges in VCC. Therefore, this study reviewed the importance of the security and privacy of real-time video data in VCC. First, we provide an overview of VANETs and their limitations. Second, we provide a state-of-the-art taxonomy for real-time video data in VCC. Then, the importance of real-time video surveillance data in both fifth generation (5G), and sixth generation (6G) networks is presented. Finally, the challenges and open issues of real-time video data in VCC are discussed.
|
|
| 4. |
- Berne, Olivier, et al.
(författare)
-
PDRs4All : A JWST Early Release Science Program on Radiative Feedback from Massive Stars
- 2022
-
Ingår i: Publications of the Astronomical Society of the Pacific. - : IOP Publishing. - 0004-6280 .- 1538-3873. ; 134:1035
-
Tidskriftsartikel (refereegranskat)abstract
- Massive stars disrupt their natal molecular cloud material through radiative and mechanical feedback processes. These processes have profound effects on the evolution of interstellar matter in our Galaxy and throughout the universe, from the era of vigorous star formation at redshifts of 1-3 to the present day. The dominant feedback processes can be probed by observations of the Photo-Dissociation Regions (PDRs) where the far-ultraviolet photons of massive stars create warm regions of gas and dust in the neutral atomic and molecular gas. PDR emission provides a unique tool to study in detail the physical and chemical processes that are relevant for most of the mass in inter- and circumstellar media including diffuse clouds, proto-planetary disks, and molecular cloud surfaces, globules, planetary nebulae, and star-forming regions. PDR emission dominates the infrared (IR) spectra of star-forming galaxies. Most of the Galactic and extragalactic observations obtained with the James Webb Space Telescope (JWST) will therefore arise in PDR emission. In this paper we present an Early Release Science program using the MIRI, NIRSpec, and NIRCam instruments dedicated to the observations of an emblematic and nearby PDR: the Orion Bar. These early JWST observations will provide template data sets designed to identify key PDR characteristics in JWST observations. These data will serve to benchmark PDR models and extend them into the JWST era. We also present the Science-Enabling products that we will provide to the community. These template data sets and Science-Enabling products will guide the preparation of future proposals on star-forming regions in our Galaxy and beyond and will facilitate data analysis and interpretation of forthcoming JWST observations.
|
|
| 5. |
|
|
| 6. |
|
|
