Adaptive Optimizations for Surveillance Sensor Network Longevity

Abstract

Sensor networks are typically wireless networks composed of resource-constrained battery powered devices. In this paper, we present a criterion for determining whether or not a surveillance sensor network is viable. We use this criterion to compare methods for extending the effective lifetime of the sensor network. The life extension methods we consider are local adaptations that reduce the energy drain on individual nodes. They are communications range management, node repositioning, and data agreement. Simulations of a surveillance scenario quantify the utility of these methods. Our results indicate that data agreement provides the most improvement in network longevity, and communications range management is also useful. Repositioning nodes to reduce the power needed for communications is dependent on the amount of attenuation experienced by the node's communications signal and the volume of traffic between nodes. When these factors are considered, node repositioning is an effective strategy for network life extension. Synergies between the energy conservation approaches are also explored.

Keywords:

• Sensor Network
• Power Conservation
• Distributed Adaptation
• Surveillance

This work was supported by, or in part by, the U.S. Army Research Laboratory and the U.S. Army Research Office (W911NF-05-1-0226).

Notes

A preliminary version of this paper appeared as R. R. Brooks and H. Siddul, “On Adaptation to Extend the Lifetime of Surveillance Sensor Networks,” Innovations and Commercial Applications of Distributed Sensor Networks Symposium, Bethesda, MD (October 2005).

S. Phoha and R. Brooks, “Emergent Surveillance Plexus MURI Annual Report,” The Pennsylvania State University Applied Research Laboratory, Report 1, Defense Advanced Research Projects Agency and Army Research Office, (March 2002).

S. Phoha and R. Brooks, “Emergent Surveillance Plexus MURI Annual Report,” The Pennsylvania State University Applied Research Laboratory, Report 2, Defense Advanced Research Projects Agency and Army Research Office (March 2003).

E. Slavin, R. R. Brooks, and E. Keller, “A comparison of tracking algorithms using beamforming and CPA methods with an emphasis on resource consumption vs. performance,” PSU/ARL ESP MURI Technical Report, 2002.

Mengxia Zhu, Song Ding, R.R. Brooks, Qishi Wu, S.S. Iyengar, Nageswara S.V. Rao, “Decision making-based multiple sensor data fusion,” Submitted for review, 2005.