Cloud and IoT-Based Vehicular Ad Hoc Networks. Группа авторов

Чтение книги онлайн.

СКАЧАТЬ the Ethernet port to connect the internet to this standalone system.

3.8 ITS Challenges and Opportunities

      Numerous difficulties occur related to transportation frameworks while addressing a different type of data. Artificial Intelligence can be integrated with data fusion techniques that allow humans to react according to the current state of the vehicle [30, 31, 37]. The usage of extra devices, similar to AI and information combination, can create more information to expand the ITS exhibition, utilizing applications that take in transportation conduct from continuous and recorded information [32]. AI is utilized to acknowledge the information on helpful examples and patterns between various traffic information sources bolstered affiliation rules [33]. Exploring innovative solutions for ensuring sustainable transportation systems is an increasing challenge identified with the advancement of secure and safe ITS applications. The eventual fate of transportation lies in cement and steel as well as to a great extent in IT. It engages parts inside the transportation system vehicles, traffic lights, message signs, etc. to get insightful by embedding them with microchips and sensors empowering to pass on through outlying developments. Intelligent Transport System (ITS) gets a huge improvement in transportation framework execution with lesser congestion, high safety, and traveler accommodation.

      ITS usage will improve the vehicle condition in different modes, for example, decreased road mishaps, better driver data, and better lane boundary intensification. The first aspect of the challenge is the elements of ITS, most extreme misuse of every single significant capacity is yet to be completely accomplished, which prompts hesitance to put resources into the ITS order. Second is in the region of better legislation of strategy for ITS activity, which is a dispute to ITS and improved harmonization of government with the private business [38, 39]. Some of the areas for future utilization and examination consist of evenness identification of most reasonable advancements and foundation for a vehicle to vehicle and vehicle to the side of the road correspondences just as further improvement of a portion of the wireless communication expertise.

ITS and IoT got to be brought into mainstream getting to make sure that transport challenges are addressed within the most effective way. National strategies for ITS need to be developed to determine the direction, policies, and framework to realize the gains from using ITS. By executing this advanced vehicle framework in real practice, the legislatures acquire benefits towards road safety as well as bring our national vehicle framework towards worldwide guidelines. Since it is received with ITS–IoT and Embedded Controller Technology, it is conceivable to spare over 90% of the traveler’s life.

References

      1. Texas A&M Transportation Institute, Technical Report 2015 Urban Mobility Score card, INRIX, Available online: https://static.tti.tamu.edu/tti.tamu.edu/documents/mobility-scorecard-2015.pdf (accessed on 11 October 2017).

      2. United Nations Population Fund (UNFPA), State of World Population 2011: People and Possibilities in a World of 7 Billion, Technical Report, United Nations Population Fund, New York, NY, USA, 2011.

      3. Population Reference Bureau, 2016 World Population Datasheet, Inform Empower Advance, 2016. http://www.prb.org/pdf16/prb-wpds2016-web-2016.pdf (accessed on 11 October 2017).

      4. USA Today, NHTSA to Require Backup Cameras on All Vehicles. https.Com/story/money/cars/2014/03/31/nhtsa-rear-view-cameras/7114531/(accessed on 11 October 2017).

      5. Automotive Sensors and Electronics Expo. Available online: http://www.Automotivesensors2017.com (accessed on 11 October 2017).

      6. Abdelhamid, S., Hassanein, H.S., Takahara, G., Vehicle as a Mobile Sensor. Procedia Comput. Sci., 34, 286–295, 2014.

      7. Silva, C., Masini, B., Ferrari, G., Thibault, I., A Survey on Infrastructure-Based Vehicular Networks. Mob. Inf. Syst., 2017, 28, 2017.

      8. Omar, H., Zhuang, W., Li, L., VeMAC: A TDMA based MAC protocol for reliable broadcast in VANETs. IEEE Trans. Mob. Comput., 12, 1724–1736, 2012.

      9. Bharati, S. and Zhuang, W., CAH-MAC: Cooperative AD HOC MAC for vehicular networks. IEEE J. Sel. Areas Commun., 13, 470–479, 2013.

      10. Wu, X., Subramanian, S., Guha, R., White, R., Li, J., Lu, K., Zhang, T., Vehicular Communications Using DSRC: Challenges, Enhancements, and Evolution. IEEE J. Sel. Areas Commun., 31, 399–408, 2013.

      11. Wang, L., Iida, R.F., Wyglinski, A.M., Performance Analysis of EDCA for IEEE 802.11p/DSRC Based V2V Communication in Discrete Event System, in: Proceedings of the 2017 IEEE 86th Vehicular Technology Conference (VTC-Fall), pp. 1–5, Toronto, ON, Canada, 24–27 September 2017.

12. Dhanya, V. and Martin, R., An efficient computerized error control transceiver system for DSRC applications, in: Proceedings of the 2017 International Conference on Signal Processing and Communication (ICSPC), pp. 112–116, Coimbatore, India, 28–29 July 2017.

      13. Gambhir, N. and Sharma, P., A hybrid approach for intelligent communication and performance analysis over DSRC VANET, in: Proceedings of the 2017 International Conference on Information, Communication, Instrumentation and Control (ICICIC), pp. 1–5, Indore, India, 17–19 August 2017.

      14. Zhou, H., Cheng, N., Yu, Q., Sherman Shen, X., Shan, D., Bai, F., Toward Multi-Radio Vehicular Data Piping for Dynamic DSRC/TVWS Spectrum Sharing. IEEE J. Sel. Areas Commun., 34, 2575–2588, 2016.

      15. Ihara, Y., Kremo, H., Altintas, O., Tanaka, H., Ohtake, M., Fujii, T., Tsuru, M., Distributed Autonomous multi-hop vehicle-to-vehicle communications over TV white space, in: Proceedings of the IEEE Consumer Communications and Networking Conference (CCNC), pp. 336–344, Las Vegas, NV, USA, 11–14 January 2013.

      16. Wang, T., Song, L., Han, Z., Coalitional graph games for popular content distribution in cognitive radioVANETs. IEEE Trans. Veh. Technol., 62, 4010–4019, 2013.

      17. Altintas, O., Ihara, Y., Kremo, H., Tanaka, H., Ohtake, M., Fujii, T., Yoshimura, C., Ando, K., Sukamoto, K., Tsuru, M. et al., Field tests and indoor emulation of distributed autonomous multi-hop vehicle-to-vehicle communications over TV white space. ACM SIGMOBILE Mob. Comput. Commun. Rev., 16, 54–57, 2013.

      18. Tiwari, P. and Kushwah, R., Traffic analysis for VANET using WAVE and WiMAX, in: Proceedings of the International Conference on Communication Networks (ICCN), pp. 343–346, Gwalior, India, 19–21 November 2015.

      19. Hsu, Y., Wang, K., Tseng, Y., Efficient cooperative access class barring with load balancing and traffic adaptive radio resource management for M2M communications over LTE-A. Comput. Netw., 73, 268–281, 2014.

      20. Doyle, N., Jaber, N., Tepe, K., Complete architecture and demonstration design for a new combined WiMAX/DSRC system with improved vehicular networking efficiency. Ad Hoc Netw., 11, 2026–2048, 2013.

      21. Mitra, R.N. and Agrawal, D.P., 5G mobile technology: A survey. ICT Express, 1, 132–137, 2015.

      22. Mohseni-Ejiyeh, A. and Ashouri-Talouki, M., SeVR+: Secure and privacy-aware cloud-assisted video reporting service for 5G vehicular networks, in: Proceedings of the Iranian Conference on Electrical Engineering (ICEE), pp. 2159–2164, Tehran, Iran, 2–4 СКАЧАТЬ