Journal of KIISE (정보과학회 논문지)

Korean Institute of Information Scientists and Engineers (한국정보과학회)
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Cooperative Detection of Moving Source Signals in Sensor Networks

센서 네트워크 환경에서 움직이는 소스 신호의 협업 검출 기법

  • Received : 2017.01.23
  • Accepted : 2017.04.20
  • Published : 2017.07.15
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Abstract

In practical distributed sensing and prediction applications over wireless sensor networks (WSN), environmental sensing activities are highly dynamic because of noisy sensory information from moving source signals. The recent distributed online convex optimization frameworks have been developed as promising approaches for solving approximately stochastic learning problems over network of sensors in a distributed manner. Negligence of mobility consequence in the original distributed saddle point algorithm (DSPA) could strongly affect the convergence rate and stability of learning results. In this paper, we propose an integrated sliding windows mechanism in order to stabilize predictions and achieve better convergence rates in cooperative detection of a moving source signal scenario.

무선 센서 네트워크의 분산 센싱 및 예측에 대한 실제 Application에서 네트워크 환경 센싱 기능은 움직이는 소스 신호의 잡음 및 많은 센싱 정보들 때문에 매우 동적인 기능을 요구한다. 최근의 Distributed Online Convex Optimization 프레임워크는 분산된 방식으로 센서 네트워크를 통해 확률적인 학습 문제를 해결하기 위한 유망한 접근법으로 개발되었다. 기존의 Distributed Saddle Point Algorithm (DSPA)의 학습 결과에서 수렴 속도와 안정성은 이동성의 영향을 받을 수 있다. 이에 본 논문에서는 움직이는 소스 신호 시나리오의 동시 검출에서 예측을 안정화하고 보다 나은 수렵 속도를 달성하기 위해 통합 Sliding Windows 메커니즘을 제안한다.

Keywords

Acknowledgement

Grant : Resilient/Fault-Tolerant Autonomic Networking Based on Physicality, Relationship and Service Semantic of IoT Devices

Supported by : Institute for Information & communications Technology Promotion(IITP)

References

  1. J. Yick, B. Mukherjee, and D. Ghosal, "Wireless sensor network survey," Computer networks, Vol. 52, No. 12, pp. 2292-2330, 2008. https://doi.org/10.1016/j.comnet.2008.04.002
  2. A. Koppel, F. Y. Jakubiec, and A. Ribeiro, "A saddle point algorithm for networked online convex optimization," IEEE Transactions on Signal Processing, Vol. 63, No. 19, pp. 5149-5164, 2015. https://doi.org/10.1109/TSP.2015.2449255
  3. A. Koppel; B. Sadler; A. Ribeiro, "Proximity Without Consensus in Online Multi-Agent Optimization," IEEE Transactions on Signal Processing, Vol. 65, No. 12, pp. 3062-3077, 2017. https://doi.org/10.1109/TSP.2017.2686368
  4. Boyd, Stephen, et al., "Distributed optimization and statistical learning via the alternating direction method of multipliers," Foundations and Trends(R) in Machine Learning 3.1, pp. 1-122, 2011.
  5. W. Shi, Q. Ling, G. Wu, and W. Yin, "Extra: An exact first-order algorithm for decentralized consensus optimization," SIAM Journal on Optimization, Vol. 25, No. 2, pp. 944-966, 2015. https://doi.org/10.1137/14096668X
  6. Elad Hazan, "Introduction to Online Convex Optimization," Foundations and Trends(R) in Optimization, Vol. 2, No. 3-4, pp. 157-325, 2016. https://doi.org/10.1561/2400000013
  7. A. S. Tanenbaum et al., Computer networks, 4th Ed., Prentice Hall, 2003.
  8. I. Navarro and F. Matia, "An introduction to swarm robotics," ISRN Robotics, Vol. 2013, 2013.