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Title: From Schedules to Programs — Reimagining Networking Infrastructure for Future Cyber-Physical Systems
Future cyber-physical systems will require higher capacity, meet more stringent real-time requirements, and adapt quickly to a broader range of network dynamics. However, the traditional approach of using fixed schedules to drive the operation of wireless networks has inherent limitations that make it unsuitable for these systems. As an alternative, we propose to replace schedules with domain-specific programs that coordinate the operation of the network. Our idea is that nodes in the network will run automatically generated programs that make informed decisions about flows at run time rather than using an a priori fixed schedule. We will sketch a domain-specific language that uses this additional flexibility to increase network capacity significantly. Furthermore, the constructed programs are also sufficiently simple to efficiently analyze key performance metrics such as flow response time and reliability. We conclude with future research directions.
Authors:
; ; ; ;
Award ID(s):
1750155
Publication Date:
NSF-PAR ID:
10337861
Journal Name:
8th NSysS 2021: 8th International Conference on Networking, Systems and Security
Page Range or eLocation-ID:
130 to 137
Sponsoring Org:
National Science Foundation
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We used a variety of techniques such as the file locking mechanism, multithreading, circular buffers, real-time event decoding, and signal-decision plotting to realize the system. A video demonstrating the system is available at: https://www.isip.piconepress.com/projects/nsf_pfi_tt/resources/videos/realtime_eeg_analysis/v2.5.1/video_2.5.1.mp4. The final conference submission will include a more detailed analysis of the online performance of each module. ACKNOWLEDGMENTS Research reported in this publication was most recently supported by the National Science Foundation Partnership for Innovation award number IIP-1827565 and the Pennsylvania Commonwealth Universal Research Enhancement Program (PA CURE). Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the official views of any of these organizations. REFERENCES [1] A. Craik, Y. He, and J. L. 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New York City, New York, USA: Demos Medical Publishing, 2007. [9] D. P. Bovet and C. Marco, Understanding the Linux Kernel, 3rd ed. O’Reilly Media, Inc., 2005. https://www.oreilly.com/library/view/understanding-the-linux/0596005652/. [10] V. Shah et al., “The Temple University Hospital Seizure Detection Corpus,” Front. Neuroinform., vol. 12, pp. 1–6, 2018. https://doi.org/10.3389/fninf.2018.00083. [11] F. Pedregosa et al., “Scikit-learn: Machine Learning in Python,” J. Mach. Learn. Res., vol. 12, pp. 2825–2830, 2011. https://dl.acm.org/doi/10.5555/1953048.2078195. [12] J. Gotman, D. Flanagan, J. Zhang, and B. Rosenblatt, “Automatic seizure detection in the newborn: Methods and initial evaluation,” Electroencephalogr. Clin. Neurophysiol., vol. 103, no. 3, pp. 356–362, 1997. https://doi.org/10.1016/S0013-4694(97)00003-9.« less
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