The SensorFly is a novel, low-cost, miniature (29g) controlledmobile aerial sensor networking platform. Mobility permits a network of SensorFly nodes, unlike fixed networks, to be autonomous in deployment, maintenance and adapting to the environment, as required for emergency response situations such as fire monitoring or survivor search.We demonstrate the ability of the SensorFly system to collaboratively sense the environment (floor temperature) in a demonstration scenario. The SensorFly nodes are tasked to explore the area and transmit sensed data back to a base station. The system partitions tasks among SensorFly nodes based on their capabilities (location, sensors, energy) to achieve concurrent and faster coverage. The real-time sensor data is presented to the user on a display terminal at the base station.
Indoor emergency response situations, such as urban fire, are characterized by dangerous constantly changing operating environments with little access to situational information for first responders.
In situ
information about the conditions, such as the extent and evolution of an indoor fire, can augment rescue efforts and reduce risk to emergency personnel. Static sensor networks that are pre-deployed or manually deployed have been proposed but are less practical due to need for large infrastructure, lack of adaptivity, and limited coverage. Controlled-mobility in sensor networks, that is, the capability of nodes to move as per network needs can provide the desired autonomy to overcome these limitations.
In this article, we present SensorFly, a controlled-mobile aerial sensor network platform for indoor emergency response application. The miniature, low-cost sensor platform has capabilities to self deploy, achieve three-dimensional sensing, and adapt to node and network disruptions in harsh environments. We describe hardware design trade-offs, the software architecture, and the implementation that enables limited-capability nodes to collectively achieve application goals. Through the indoor fire monitoring application scenario, we validate that the platform can achieve coverage and sensing accuracy that matches or exceeds static sensor networks and provide higher adaptability and autonomy.
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