NSF Award
0615132
Truly mobile, adaptive and autonomous sensor networks, including mobile health monitoring and environmental monitoring systems, have extremely constrained resources. For extended operation, such systems require advanced real-time control methodologies with minimal usage of power, communication or computing resources. DEFT (Distributed Embedded Fault-Tolerant Control of Resource Constrained Sensor Networks) involves the development of real-time distributed control algorithms for use in embedded wireless sensor networks that are severely constrained by size, computation, power, storage and bandwidth.<br/>DEFT uses a novel information theoretic Kalman Filter (KF) approach to express the uncertainty in the information extracted through the sensors as a function of the sampling rate of each sensor and the cross-correlations between sensory observations. This is used within a distributed Markov Decision Process (DMDP) Controller to derive the optimal sampling rates while ensuring that system constraints are not violated. Fault tolerance is built into the DMDP to handle intermittent or permanent sensor degradation. The DMDP formulation with the KF estimator very efficiently handles and controls multiple variables, including sensor sampling rate and deciding when information should be exchanged with other sensors (via wireless communication). <br/>The DEFT system is applicable to a host of applications that use sensor networks, ranging from monitoring firefighters, soldiers in the field, police, diabetics, to environmental monitoring sensor nets for oceanography, space habitats, and multitarget tracking in battlefields. The software and algorithms are expected to have potential in a variety of reactive, low power systems, including reactive sensor networks for vibration analysis and compensation in buildings and structures.
