DREAM: On the reaction delay in large scale wireless networks with mobile sensors

In this work, we present a monitor and rescue system utilizing hybrid networks which is a integration of stationary sensor networks and mobile sensor networks: stationary sensor networks comprised of large numbers of small, simple, and inexpensive wireless sensors, and the mobile sensor network contains a set of mobile sensors (robots). The static sensors in our network have “monitoring” ability, i.e., any activated static sensor can detect the event as long as its sensing range intersects the event region. And the mobile sensors have “moving” and “rescuing” ability, e.g., they can move toward the event region with limited speed and further perform certain rescuing/processing operations on the event. We can consider the event as a hazard, e.g., wild fire, and the mobile sensors as fireman robots. As soon as the fire is detected by the static sensors, the fireman robots are expected to move from its initial location to the hazard region within minimum latency. We define the reaction delay of the system as the delay from the occurrence of event till at least one mobile sensor reaches the event. In order to satisfy certain reaction delay requirement while minimizing the total cost, we propose a number of deployment strategies for the stationary sensor network and mobile sensor network respectively. We further design a random wake-up scheduling for the static sensors for the sake of energy efficiency. Finally, we propose a pure distributed motion strategy for mobile sensors without reliance on localization services such as GPS, focusing on simple algorithms for distributed decision making and information propagation. We demonstrate the efficacy of our system in simulation, providing empirical results.

[1]  Alhussein A. Abouzeid,et al.  Stochastic Event Capture Using Mobile Sensors Subject to a Quality Metric , 2006, IEEE Transactions on Robotics.

[2]  Wei Wang,et al.  Trade-offs between mobility and density for coverage in wireless sensor networks , 2007, MobiCom '07.

[3]  Parameswaran Ramanathan,et al.  Analytic modeling of detection latency in mobile sensor networks , 2006, IPSN.

[4]  Francesca Cuomo,et al.  Funneling-MAC: a localized, sink-oriented MAC for boosting fidelity in sensor networks , 2006, SenSys '06.

[5]  Piyush Gupta,et al.  Critical Power for Asymptotic Connectivity in Wireless Networks , 1999 .

[6]  Wolfgang Maass,et al.  Approximation Schemes for Covering and Packing Problems in Robotics and VLSI , 1984, STACS.

[7]  Yunhao Liu,et al.  Contour map matching for event detection in sensor networks , 2006, SIGMOD Conference.

[8]  Subhash Suri,et al.  Catching elephants with mice: sparse sampling for monitoring sensor networks , 2007, SenSys '07.

[9]  Peter Corke,et al.  Localization and navigation assisted by cooperating networked sensors and robots , 2005 .

[10]  K. J. Ellis,et al.  Cattle health monitoring using wireless sensor networks , 2004 .

[11]  Vinayak S. Naik,et al.  A line in the sand: a wireless sensor network for target detection, classification, and tracking , 2004, Comput. Networks.

[12]  Peter I. Corke,et al.  Localization and Navigation Assisted by Networked Cooperating Sensors and Robots , 2005, Int. J. Robotics Res..

[13]  Valerie King,et al.  Connectivity of Wireless Sensor Networks with Constant Density , 2004, ADHOC-NOW.

[14]  Wei Hong,et al.  A macroscope in the redwoods , 2005, SenSys '05.

[15]  Charles J. Colbourn,et al.  Unit disk graphs , 1991, Discret. Math..

[16]  John Anderson,et al.  Wireless sensor networks for habitat monitoring , 2002, WSNA '02.

[17]  Xiaohua Jia,et al.  Mobility-Assisted Spatiotemporal Detection in Wireless Sensor Networks , 2008, 2008 The 28th International Conference on Distributed Computing Systems.

[18]  Vladimir Vapnik,et al.  Chervonenkis: On the uniform convergence of relative frequencies of events to their probabilities , 1971 .

[19]  Wolfgang Maass,et al.  Approximation schemes for covering and packing problems in image processing and VLSI , 1985, JACM.

[20]  Subhash Suri,et al.  Catching elephants with mice: Sparse sampling for monitoring sensor networks , 2009, TOSN.

[21]  Donald F. Towsley,et al.  Mobility improves coverage of sensor networks , 2005, MobiHoc '05.

[22]  Yixin Chen,et al.  Fast Sensor Placement Algorithms for Fusion-Based Target Detection , 2008, 2008 Real-Time Systems Symposium.

[23]  W. Kern,et al.  A robust PTAS for maximum independent sets in unit disk graphs , 2004 .