Fog Computing. Группа авторов
Читать онлайн книгу.
et al., Fog Computing Conceptual Model: Recommendations of the National Institute of Standards and Technology, Special Publication NIST SP 500-325, National Institute of Standards and Technology, March 2018.
11 11 Satyanarayanan, M. (2017). The emergence of edge computing. Computer 50 (1): 30–39.
12 12 Fahmy, H.M., El Ghany, M.A., and Baumann, G. (2018). Vehicle risk assessment and control for lane-keeping and collision avoidance at low-speed and high-speed scenarios. IEEE Transactions on Vehicular Technology 57 (6): 4805–4818.
13 13 Hafner, M.R., Cunningham, D., Caminiti, L., and Del Vecchio, D. (2013). Cooperative collision avoidance at intersections: algorithms and experiments. IEEE Transactions on Intelligent Transportation Systems 14 (3): 1162–1175.
14 14 Nguyen, V., Kim, O.T.T., Pham, C. et al. (2018). A survey on adaptive multichannel mac protocols in vanets using markov models. IEEE Access 6: 16493–16514.
15 15 Vinel, A., Lyamin, N., and Isachenkov, P. (2018). Modeling of V2V communications for C-ITS safety applications: a CPS perspective. IEEE Communications Letters 22 (8): 1600–1603.
16 16 Kalatzis, N., Avgeris, M., Dechouniotis, D. et al. (2018). Edge computing in IoT ecosystems for UAV-enabled early fire detection. In: 2018 IEEE International Conference on Smart Computing (SMARTCOMP), 106–114. IEEE.
17 17 Chang, C., Srirama, S.N., and Buyya, R. (2017). Indie fog: an efficient fog-computing infrastructure for the Internet of Things. Computer 50 (9): 92–98.
18 18 Soo, S., Chang, C., Loke, S.W., and Srirama, S.N. (2017). Proactive mobile fog computing using work stealing: data processing at the edge. International Journal of Mobile Computing and Multimedia Communications (IJMCMC) 8 (4): 1–19.
19 19 Soto, V., De Grande, R.E., and Boukerche, A. (2017). REPRO: time-constrained data retrieval for edge offloading in vehicular clouds. In: Proceedings of the 14th ACM Symposium on Performance Evaluation of Wireless Ad Hoc, Sensor, & Ubiquitous Networks, 47–54. ACM.
20 20 Zhu, C., Tao, J., Pastor, G. et al. (2018). Folo: latency and quality optimized task allocation in vehicular fog computing. IEEE Internet of Things Journal 6 (3): 4150–4161.
21 21 Yao, H., Bai, C., Zeng, D. et al. (2015). Migrate or not? Exploring virtual machine migration in roadside cloudlet-based vehicular cloud. Concurrency and Computation: Practice and Experience 27 (18): 5780–5792.
22 22 Cordeschi, N., Amendola, D., and Baccarelli, E. (2015). Reliable adaptive resource management for cognitive cloud vehicular networks. IEEE Transactions on Vehicular Technology 64 (6): 2528–2537.
23 23 Chen, Y.-A., Walters, J.P., and Crago, S.P. (2017). Load balancing for minimizing deadline misses and total runtime for connected car systems in fog computing. In: 2017 IEEE International Symposium on Parallel and Distributed Processing with Applications and 2017 IEEE International Conference on Ubiquitous Computing and Communications (ISPA/IUCC), 683–690. IEEE.
24 24 Fan, X., He, X., Puthal, D. et al. (2018). CTOM: collaborative task offloading mechanism for mobile cloudlet networks. In: 2018 IEEE International Conference on Communications (ICC), 1–6. IEEE.
25 25 Marshall, W.E. and Dumbaugh, E. (2018). Revisiting the relationship between traffic congestion and the economy: a longitudinal examination of us metropolitan areas. Transportation: 1–40.
26 26 Wang, C., Li, Y., Jin, D., and Chen, S. (2016). On the serviceability of mobile vehicular cloudlets in a large-scale urban environment. IEEE Transactions on Intelligent Transportation Systems 17 (10): 2960–2970.
27 27 Wang, Z., Zhong, Z., Zhao, D., and Ni, M. (2018). Vehicle-based cloudlet relaying for mobile computation offloading. IEEE Transactions on Vehicular Technology 67 (11): 11181–11191.
28 28 Yang, T., Cui, Z., Wang, R. et al. (2018). A multivessels cooperation scheduling for networked maritime fog-ran architecture leveraging SDN. Peer-to-Peer Networking and Applications 11 (4): 808–820.
29 29 R. Sosa, R. Sucasas, A. Queralt et al., Towards an open, secure, decentralized and coordinated fog-to-cloud management ecosystem, D5.1 mF2C reference architecture (integration IT-1), mF2C Consortium, 2018.
30 30 Xu, G., Shen, W., and Wang, X. (2014). Applications of wireless sensor networks in marine environment monitoring: a survey. Sensors 14 (9): 16932–16954.
31 31 Mohamed, N., Al-Jaroodi, J., Jawhar, I. et al. (2017). UAV fog: a UAV-based fog computing for Internet of Things. In: 2017 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computed, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI), 1–8. IEEE.
32 32 Radu, D., Cretu, A., Parrein, B. et al. (2018). Flying ad hoc network for emergency applications connected to a fog system. In: International Conference on Emerging Internetworking, Data & Web Technologies, 675–686. Springer.
33 33 451 Research, “Size and impact of fog computing market,” OpenFog Consortium, 2017.
34 34 Puliafito, C., Mingozzi, E., and Anastasi, G. (2017). Fog computing for the Internet of mobile things: issues and challenges. In: 2017 IEEE International Conference on Smart Computing (SMARTCOMP), 1–6. IEEE.
35 35 Silva, P.M.P., Rodrigues, J., Silva, J. et al. (2017). Using edge-clouds to reduce load on traditional Wi-Fi infrastructures and improve quality of experience. In: 2017 IEEE 1st International Conference on Fog and Edge Computing (ICFEC), 61–67. IEEE.
36 36 Chang, C. and Srirama, S.N. (2018). Providing context as a service using service-oriented mobile indie fog and opportunistic computing. In: European Conference on Software Architecture, –219, 235. Springer.
37 37 Siddiqui, F., Zeadally, S., and Salah, K. (2015). Gigabit wireless networking with ieee 802.11 ac: technical overview and challenges. Journal of Networks 10 (3): 164.
38 38 Rejiba, Z., Masip-Bruin, X., Jurnet, A. et al. (2018). F2C-aware: enabling discovery in Wi-Fi-powered fog-to-cloud (F2C) systems. In: 2018 6th IEEE International Conference on Mobile Cloud Computing, Services, and Engineering (MobileCloud), 113–116. IEEE.
39 39 Chowdhury, M., Steinbach, E., Kellerer, W., and Maier, M. (2018). Context-aware task migration for HART-centric collaboration over FiWi based tactile internet infrastructures. IEEE Transactions on Parallel and Distributed Systems 29 (6): 1231–1246.
40 40 Enayet, A., Razzaque, M.A., Hassan, M.M. et al. (2018). A mobility-aware optimal resource allocation architecture for big data task execution on mobile cloud in smart cities. IEEE Communications Magazine 56 (2): 110–117.
41 41 Taleb, T., Dutta, S., Ksentini, A. et al. (2017). Mobile edge computing potential in making cities smarter. IEEE Communications Magazine 55: 38–43.
42 42 Akter, M., Zohra, F.T., and Das, A.K. (2017). Q-MAC: QoS and mobility aware optimal resource allocation for dynamic application offloading in mobile cloud computing. In: International Conference on Electrical, Computer and Communication Engineering (ECCE), 803–808. IEEE.
43 43 Lei, L. (2016). Stochastic modeling of device-to-device communications for intelligent transportation systems. In: 2016 23rd International Conference on Telecommunications (ICT), 1–5. IEEE.
44 44 ITUR, Requirements related to technical performance for IMT-advanced radio interface(s), Report M.2134, International Telecommunications Union, 2008.
45 45 ITUR, Minimum requirements related to technical performance for IMT 2020 radio interface(s), Report M.2410, International Telecommunications Union, 2017 (22nd February Draft).
46 46 IMT vision – framework and overall objectives of the future development of IMT for 2020 and beyond, Recommendation ITU-R M.2083-0, pp. 2083–2090, International Telecommunications Union, 2015.
47 47 Dong, P., Zheng, T., Yu, S. et al. (2017). Enhancing vehicular communication using 5g-enabled smart collaborative networking. IEEE Wireless Communications 24: 72–79.
48 48 Yu, F., Chen, H., and Xu, J. (2018). DMPO: dynamic mobility-aware partial offloading in mobile edge computing. Future Generation Computer Systems 89: 722–735.
49 49