publication . Preprint . 2017

IoT Stream Processing and Analytics in The Fog

Yang, Shusen;
Open Access English
  • Published: 16 May 2017
The emerging Fog paradigm has been attracting increasing interests from both academia and industry, due to the low-latency, resilient, and cost-effective services it can provide. Many Fog applications such as video mining and event monitoring, rely on data stream processing and analytics, which are very popular in the Cloud, but have not been comprehensively investigated in the context of Fog architecture. In this article, we present the general models and architecture of Fog data streaming, by analyzing the common properties of several typical applications. We also analyze the design space of Fog streaming with the consideration of four essential dimensions (sy...
free text keywords: Computer Science - Networking and Internet Architecture
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[1] M. Satyanarayanan, P. Simoens, Y. Xiao, P. Pillai, Z. Chen, K. Ha, W. Hu, and B. Amos, “Edge analytics in the internet of things,” IEEE Pervasive Comput., vol. 14, no. 2, pp. 24-31, 2015.

[2] M. Chiang and T. Zhang, “Fog and iot: An overview of research opportunities,” IEEE Internet Things J., vol. 3, no. 6, pp. 854-864, 2016.

[3] O. Diallo, J. J. Rodrigues, M. Sene, and J. Lloret, “Distributed database management techniques for wireless sensor networks,” IEEE Trans. Parallel Distrib. Syst., vol. 26, no. 2, pp. 604-620, 2015.

[4] L. Canzian and M. Van Der Schaar, “Real-time stream mining: online knowledge extraction using classifier networks,” IEEE Netw., vol. 29, no. 5, pp. 10-16, 2015.

[5] R. A. Gupta and M.-Y. Chow, “Networked control system: overview and research trends,” IEEE Trans. Ind. Electron., vol. 57, no. 7, pp. 2527-2535, 2010.

[6] H. Zhang, G. Chen, B. C. Ooi, K.-L. Tan, and M. Zhang, “In-memory big data management and processing: A survey,” IEEE Trans. Knowl. Data Eng., vol. 27, no. 7, pp. 1920-1948, 2015.

[7] G. Zhang, Y. Li, and T. Lin, “Caching in information centric networking: A survey,” Computer Networks, vol. 57, no. 16, pp. 3128-3141, 2013.

[8] S. Yang, Y. Tahir, P.-y. Chen, M. Alan, and J. McCann, “Distributed optimization in energy harvesting sensor networks with dynamic innetwork data processing,” in Proc. IEEE Infocom, 2016, pp. 1-9.

[9] L. Yang, J. Cao, Y. Yuan, T. Li, A. Han, and A. Chan, “A framework for partitioning and execution of data stream applications in mobile cloud computing,” ACM SIGMETRICS Performance Evaluation Review, vol. 40, no. 4, pp. 23-32, 2013. [OpenAIRE]

[10] S. Wang, R. Urgaonkar, M. Zafer, T. He, K. Chan, and K. K. Leung, “Dynamic service migration in mobile edge-clouds,” in Proc. IFIP Networking, 2015, pp. 1-9.

[11] L. Wang, D. Zhang, A. Pathak, C. Chen, H. Xiong, D. Yang, and Y. Wang, “Ccs-ta: quality-guaranteed online task allocation in compressive crowdsensing,” in Proc. ACM Ubicomp, 2015, pp. 683-694.

[12] P. Chen, S. Yang, and J. A. McCann, “Distributed real-time anomaly detection in networked industrial sensing systems,” IEEE Trans. Ind. Electron., vol. 62, no. 6, pp. 3832-3842, 2015.

[13] W. S. Lasecki, C. D. Miller, and J. P. Bigham, “Warping time for more effective real-time crowdsourcing,” in Proc. ACM SIGCHI, 2013, pp. 2033-2036.

[14] I.-H. Hou, T. Zhao, S. Wang, and K. Chan, “Asymptotically optimal algorithm for online reconfiguration of edge-clouds,” in Proc. ACM Mobihoc, 2016, pp. 291-300.

[15] J. Ghaderi, S. Shakkottai, and R. Srikant, “Scheduling storms and streams in the cloud,” ACM Transactiosn on Modeling and Performance Evaluation of Computing Systems, vol. 1, no. 4, pp. 1-28, 2016.

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