Fog Computing

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Summarizes the current state and upcoming trends within the area of fog computing Written by some of the leading experts in the field,
focuses on the technological aspects of employing fog computing in various application domains, such as smart healthcare, industrial process control and improvement, smart cities, and virtual learning environments. In addition, the Machine-to-Machine (M2M) communication methods for fog computing environments are covered in depth.
Presented in two parts—Fog Computing Systems and Architectures, and Fog Computing Techniques and Application—this book covers such important topics as energy efficiency and Quality of Service (QoS) issues, reliability and fault tolerance, load balancing, and scheduling in fog computing systems. It also devotes special attention to emerging trends and the industry needs associated with utilizing the mobile edge computing, Internet of Things (IoT), resource and pricing estimation, and virtualization in the fog environments.
Includes chapters on deep learning, mobile edge computing, smart grid, and intelligent transportation systems beyond the theoretical and foundational concepts Explores real-time traffic surveillance from video streams and interoperability of fog computing architectures Presents the latest research on data quality in the IoT, privacy, security, and trust issues in fog computing
provides a platform for researchers, practitioners, and graduate students from computer science, computer engineering, and various other disciplines to gain a deep understanding of fog computing.

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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 Wang, Z., Zhao, Z., Min, G. et al. (2018). User mobility aware task assignment for mobile edge computing. Future Generation Computer Systems 85: 1–8.

50 50 Nasrin, W. and Xie, J. (2018). SharedMEC: sharing clouds to support user mobility in mobile edge computing. In: 2018 IEEE International Conference on Communications (ICC), 1–6. IEEE.

51 51 Yang, J., Wen, J., Jiang, B. et al. (2018). Marine depth mapping algorithm based on the edge computing in Internet of Things. Journal of Parallel and Distributed Computing 114: 95–103.

52 52 Jeong, S., Simeone, O., and Kang, J. (2018). Mobile edge computing via a UAV-mounted cloudlet: optimization of bit allocation and path planning. IEEE Transactions on Vehicular Technology 67 (3): 2049–2063.

53 53 Salem, A., Salonidis, T., Desai, N., and Nadeem, T. (2017). Kinaara: distributed discovery and allocation of mobile edge resources. In: 2017 IEEE 14th International Conference on Mobile Ad Hoc and Sensor Systems (MASS), 153–161. IEEE.

54 54 Liyanage, M., Chang, C., and Srirama, S.N. (2018). Adaptive mobile web server framework for mist computing in the Internet of Things. International Journal of Pervasive Computing and Communications.

55 55 Sucipto, K., Chatzopoulos, D., Kosta±, S., and Hui, P. (2017). Keep your nice friends close, but your rich friends closer — computation offloading using nfc. In: IEEE INFOCOM 2017 IEEE Conference on Computer Communications, 1–9.

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57 57 Al-Zaidi, R., Woods, J., Al-Khalidi, M. et al. (2017). Next generation marine data networks in an iot environment. In: 2017 Second International Conference on Fog and Mobile Edge Computing (FMEC), 50–55. IEEE.

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59 59 Bardram, A.V.T., Larsen, M.D., Malarski, K.M. et al. (2018). Lorawan capacity simulation and field test in a harbour environment. In: 2018 Third International Conference on Fog and Mobile Edge Computing (FMEC), 193–198.

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