EAGER: Bio-Inspired Smart Sensor Networks for Adaptive Emergency Response

University Of Illinois At Urbana-Champaign, Urbana IL

Investigators

Abstract

Recent seismic calamities in Pakistan (>79,000 deaths), China (>68,000 deaths), and Haiti (>170,000 deaths) have demonstrated the tremendous vulnerability of civil infrastructure, as well as the need for improved emergency response. What is not widely known is that disasters of this magnitude could occur in the USA: recent studies estimate that a major rupture along the New Madrid fault could result in casualties exceeding 70,000 in Tennessee and Missouri alone. Combining techniques from civil engineering, computer science, and neurobiology, this research seeks to transform the effectiveness of emergency response personnel in dealing with such disasters--ensuring rapid assessment of the stability of physical structures, location and rescue of trapped and non-ambulatory victims, identification and provision of emergency medical treatment, and safe evacuation of ambulatory survivors. This research focuses on developing a bio-inspired smart sensing and communications framework for rapid emergency response. The system utilizes enhanced mobile phone technology to create a neurally-informed, sensor-rich, information-processing network. The research builds on the similarities in biological systems and adaptive sensing and communication frameworks. Like biological neurons, each cell phone node has both communication and computation capabilities. Individual nodes are capable of establishing local connections with their neighbors through ad-hoc networking (much as neurons connect with synapses), as well as longer-distance global communication via cell tower infrastructure (similar to diffuse neuromodulatory and neuroendocrine signaling). Cell phones also have a variety of sensor input capabilities (e.g. sound, video, motion sensing, and GPS) that have parallels with the sensory capabilities of biological systems. These analogies facilitate translation of information processing principles from neurobiology to engineering implementations for efficient acquisition, filtering and transmission of task-relevant information in emergency response scenarios.