The objective of this research is to study network issues critical for building self-sustainable outdoors sensor networks. The proposed approach is to introduce environmental survivability-heterogeneity to sensor networks, and to exploit it to holistically and efficiently deal with hazardous environmental conditions that are hard to predict. The approach is motivated by the genetic phenomenon, by which an offspring from a large gene pool can improve its survival in light of environmental threats. The main research thrusts include: designing environmental survivability-aware duty-cycle scheduling, routing, MAC and tasking algorithms to sustainable network, designing schemes to improve network fault tolerance by leveraging environmentally robust nodes, and establishing models to characterize environmental conditions and nodes of different modalities and to provide guidelines for network planning. The proposed methodology will be validated through a combination of simulation, analysis, and prototype implementation.

The project will result in an extensive understanding of the conjoining of sensor networks and the physical world, in a new approach of incorporating the heterogeneity in environmental survivability to construct robust sensor networks, in theoretical and practical advances in network planning, duty-cycle scheduling, and fault-tolerance in sensor networks, and in a new set of environmental survivability-aware MAC, routing, and tasking algorithms.

The project is expected to promote the adoption and application of sensor networks through enhancing their survivability in the outdoors environment and, thus, providing better solutions for disaster rescue missions, military surveillance, and intelligent transportation systems. The project will offer an excellent opportunity to broaden the participation of under-represented groups, to boost the effectiveness of networking education, and to engage high-school, undergraduate and graduate students.

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Rutgers University
United States
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