Communication and sensing trade-offs in decentralized mobile sensor networks: a cross-layer design approach

In this paper we characterize the impact of imperfect communication on the performance of a decentralized mobile sensor network. We first examine and demonstrate the trade-offs between communication and sensing objectives, by determining the optimal sensor configurations when introducing imperfect communication. We further illustrate the performance degradation caused by non-ideal communication links in a decentralized mobile sensor network. To address this, we propose a decentralized motion-planning algorithm that considers communication effects. The algorithm is a cross-layer design based on the proper interface of physical and application layers. Simulation results will show the performance improvement attained by utilizing this algorithm.

[1]  Stephen P. Boyd,et al.  Joint optimization of communication rates and linear systems , 2003, IEEE Trans. Autom. Control..

[2]  Yasamin Mostofi,et al.  Effect of time-varying fading channels on control performance of a mobile sensor , 2004, 2004 First Annual IEEE Communications Society Conference on Sensor and Ad Hoc Communications and Networks, 2004. IEEE SECON 2004..

[3]  S. Mitter,et al.  Control of LQG systems under communication constraints , 1998, Proceedings of the 37th IEEE Conference on Decision and Control (Cat. No.98CH36171).

[4]  R. Murray,et al.  Robust connectivity of networked vehicles , 2004, 2004 43rd IEEE Conference on Decision and Control (CDC) (IEEE Cat. No.04CH37601).

[5]  K. V. Ramachandra,et al.  Kalman Filtering Techniques for Radar Tracking , 2000 .

[6]  Andrea Goldsmith,et al.  Wireless communication tradeoffs in distributed control , 2003, 42nd IEEE International Conference on Decision and Control (IEEE Cat. No.03CH37475).

[7]  Dit-Yan Yeung,et al.  A decentralized approach to the motion planning problem for multiple mobile robots , 1987, Proceedings. 1987 IEEE International Conference on Robotics and Automation.

[8]  A. Goldsmith,et al.  Kalman filtering with partial observation losses , 2004, 2004 43rd IEEE Conference on Decision and Control (CDC) (IEEE Cat. No.04CH37601).

[9]  Jun Ota,et al.  Fusion of multiple ultrasonic sensor data and imagery data for measuring moving obstacle's motion , 1996, 1996 IEEE/SICE/RSJ International Conference on Multisensor Fusion and Integration for Intelligent Systems (Cat. No.96TH8242).

[10]  Bruno Sinopoli,et al.  Distributed control applications within sensor networks , 2003, Proc. IEEE.

[11]  Ali H. Sayed,et al.  Linear Estimation (Information and System Sciences Series) , 2000 .

[12]  Chee-Yee Chong,et al.  Sensor networks: evolution, opportunities, and challenges , 2003, Proc. IEEE.

[13]  Yi Guo,et al.  A distributed and optimal motion planning approach for multiple mobile robots , 2002, Proceedings 2002 IEEE International Conference on Robotics and Automation (Cat. No.02CH37292).

[14]  Camillo J. Taylor,et al.  Dynamic Sensor Planning and Control for Optimally Tracking Targets , 2003, Int. J. Robotics Res..

[15]  W. C. Jakes,et al.  Microwave Mobile Communications , 1974 .

[16]  Sailes K. Sengijpta.  Fundamentals of Statistical Signal Processing: Estimation Theory , 1995 .

[17]  R.M. Murray,et al.  On a decentralized active sensing strategy using mobile sensor platforms in a network , 2004, 2004 43rd IEEE Conference on Decision and Control (CDC) (IEEE Cat. No.04CH37601).

[18]  Y. Mostofi,et al.  On dropping noisy packets in Kalman filtering over a wireless fading channel , 2005, Proceedings of the 2005, American Control Conference, 2005..