Sensing uncertainty reduction using low complexity actuation

The performance of a sensor network may be best judged by the quality of application specific information return. The actual sensing performance of a deployed sensor network depends on several factors which cannot be accounted at design time, such as environmental obstacles to sensing. We propose the use of mobility to overcome the effect of unpredictable environmental influence and to adapt to run time dynamics. Now, mobility with its dependencies such as precise localization and navigation is expensive in terms of hardware resources and energy constraints, and may not be feasible in compact, densely deployed and widespread sensor nodes. We present a method based on low complexity and low energy actuation primitives which are feasible for implementation in sensor networks. We prove how these primitives improve the detection capabilities with theoretical analysis, extensive simulations and real world experiments. The significant coverage advantage recurrent in our investigation justifies our own and other parallel ongoing work in the implementation and refinement of self-actuated systems.

[1]  Jennifer C. Hou,et al.  Maintaining Sensing Coverage and Connectivity in Large Sensor Networks , 2005, Ad Hoc Sens. Wirel. Networks.

[2]  Ruzena Bajcsy,et al.  Occlusions as a Guide for Planning the Next View , 1993, IEEE Trans. Pattern Anal. Mach. Intell..

[3]  Mani B. Srivastava,et al.  System design of Smart Table , 2003, Proceedings of the First IEEE International Conference on Pervasive Computing and Communications, 2003. (PerCom 2003)..

[4]  Jeff Frolik,et al.  Effects of natural propagation environments on wireless sensor network coverage area , 2003, Proceedings of the 35th Southeastern Symposium on System Theory, 2003..

[5]  Zygmunt J. Haas,et al.  The shared wireless infostation model: a new ad hoc networking paradigm (or where there is a whale, there is a way) , 2003, MobiHoc '03.

[6]  Srdjan Capkun,et al.  Mobility helps security in ad hoc networks , 2003, MobiHoc '03.

[7]  William H. Bares,et al.  A Model for Constraint-Based Camera Planning , 2000 .

[8]  Miodrag Potkonjak,et al.  Smart kindergarten: sensor-based wireless networks for smart developmental problem-solving environments , 2001, MobiCom '01.

[9]  Gaurav S. Sukhatme,et al.  Robomote: a tiny mobile robot platform for large-scale ad-hoc sensor networks , 2002, Proceedings 2002 IEEE International Conference on Robotics and Automation (Cat. No.02CH37292).

[10]  Yong Wang,et al.  Energy-efficient computing for wildlife tracking: design tradeoffs and early experiences with ZebraNet , 2002, ASPLOS X.

[11]  Gaurav S. Sukhatme,et al.  Studying the feasibility of energy harvesting in a mobile sensor network , 2003, 2003 IEEE International Conference on Robotics and Automation (Cat. No.03CH37422).

[12]  Gaurav S. Sukhatme,et al.  Tracking Targets Using Multiple Robots: The Effect of Environment Occlusion , 2002, Auton. Robots.

[13]  Deborah Estrin,et al.  Embedding the Internet: introduction , 2000, Commun. ACM.

[14]  Jeff Burke,et al.  Networking with knobs and knats ? Towards ubiquitous computing for artists , 2002 .

[15]  Vivek A. Sujan.  Task Directed Imaging in Unstructured Environments by Cooperating Robots , 2002, ICVGIP.

[16]  Xiang-Yang Li,et al.  Coverage in wireless ad-hoc sensor networks , 2002, 2002 IEEE International Conference on Communications. Conference Proceedings. ICC 2002 (Cat. No.02CH37333).

[17]  Waylon Brunette,et al.  Data MULEs: modeling and analysis of a three-tier architecture for sparse sensor networks , 2003, Ad Hoc Networks.

[18]  Yu-Chee Tseng,et al.  The Coverage Problem in a Wireless Sensor Network , 2003, WSNA '03.

[19]  Shashi Phoha,et al.  Surveillance coverage of sensor networks under a random mobility strategy , 2003, Proceedings of IEEE Sensors 2003 (IEEE Cat. No.03CH37498).

[20]  E. Adelson,et al.  The Plenoptic Function and the Elements of Early Vision , 1991 .

[21]  Ashutosh Sabharwal,et al.  Using Predictable Observer Mobility for Power Efficient Design of Sensor Networks , 2003, IPSN.

[22]  Deborah Estrin,et al.  Habitat monitoring: application driver for wireless communications technology , 2001, CCRV.

[23]  Harry Shum,et al.  Plenoptic sampling , 2000, SIGGRAPH.

[24]  Miodrag Potkonjak,et al.  Coverage problems in wireless ad-hoc sensor networks , 2001, Proceedings IEEE INFOCOM 2001. Conference on Computer Communications. Twentieth Annual Joint Conference of the IEEE Computer and Communications Society (Cat. No.01CH37213).

[25]  Guoliang Xing,et al.  Integrated coverage and connectivity configuration in wireless sensor networks , 2003, SenSys '03.

[26]  David Tse,et al.  Mobility increases the capacity of ad-hoc wireless networks , 2001, Proceedings IEEE INFOCOM 2001. Conference on Computer Communications. Twentieth Annual Joint Conference of the IEEE Computer and Communications Society (Cat. No.01CH37213).

[27]  J. Rabaey,et al.  PicoRadio: Ad-hoc wireless networking of ubiquitous low-energy sensor/monitor nodes , 2000, Proceedings IEEE Computer Society Workshop on VLSI 2000. System Design for a System-on-Chip Era.

[28]  Héctor H. González-Baños,et al.  Real-time combinatorial tracking of a target moving unpredictably among obstacles , 2002, Proceedings 2002 IEEE International Conference on Robotics and Automation (Cat. No.02CH37292).

[29]  Gaurav S. Sukhatme,et al.  An Incremental Self-Deployment Algorithm for Mobile Sensor Networks , 2002, Auton. Robots.

[30]  Theodore S. Rappaport,et al.  Propagation and radio system design issues in mobile radio systems for the glomo project , 1997 .

[31]  M. Magdon-Ismail,et al.  Power Optimal Connectivity and Coverage in Wireless Sensor Networks , 2003 .

[32]  Gregory J. Pottie,et al.  Wireless integrated network sensors , 2000, Commun. ACM.

[33]  Mani B. Srivastava,et al.  A distributed computation platform for wireless embedded sensing , 2002, Proceedings. IEEE International Conference on Computer Design: VLSI in Computers and Processors.