Embedded systems technology has reached the level at which it is practical to deploy numerous low-power wireless sensing devices with the capacity to execute sophisticated algorithms and to selectively allocate resources to the most critical situations. The plan is to develop a robust platform for distributed, pervasive, small sensing and computing devices to implement a cognitive information processing system that understands its surroundings and responds to changes in its environment by constantly evaluating and adapting its behavior. The novelty of the approach lies in the use of (1) multiple sensor fusion based on a dynamic Bayesian network (DBN) which provides a powerful, intelligent, and cognitive learning framework, and (2) collaborative and distributed Monte Carlo (CDMC) tracking methods for establishing the collaboration among multiple computational units. Once the DBN model is learned, solutions to the problems under consideration are provided by the probabilistic inference of the model, which provides a self-evaluation of the tracker. The scientific objective will be to develop a framework and algorithms for multi-perspective, multi-modal fusion of sensor information subject to bandwidth, energy, reliability,and location-uncertainty constraints associated with low-cost distributed sensors. The proposed research covers topics from sensor calibration and feature extraction, to data fusion and collaborative processing, to resource/performance optimizations at the local and system level. While many of the individual problems have been studied on their own, the challenge is to bring them all together in a new context under tight constraints on power consumption for computing and sensor communication, as well as latency constraints and sensor failure considerations.
Distributed, collaborative sensing will play an increasingly critical role ina variety of applications involving security and surveillance, environmental monitoring, industrial automation, and remote exploration. The techniques can also be usedin a number of other applications, such as human tracking and behavior analysis for medical applications such as telemonitoring of elderly people. The broader impacts of the proposed activity also include improvements and integration of the signal processing and computer vision curricula at Northwestern and the opportunity for undergraduates to perform research on related topics.
