HumAnS Lab


Sensor webs (or networks) have been shown to be a powerful tool for in-situ science applications ranging from earthquake forecasting to understanding climate change. These networks capitalize on their ability to deploy cheap nodes throughout a region of interest in order to gather information relevant for scientific analysis. Recently, there has been growing interest in mobile networks to deal with the limitations of static networks, including issues of network deployment, coverage, and fault tolerance. However, a number of issues still exist in deploying mobile sensor networks for science applications, including the effectiveness of adapting to the environment and to changing science requirements, balancing power usage, and selecting between communication and control strategies.

In this work, we employ a natural extension to the sensor web concept that enables controlled reconfiguration of sensor assets for fault-tolerant in-situ sampling. The main motivation behind our approach is to apply decentralized (i.e. local) control algorithms for network adaptation and deployment while establishing the global sensing capability required for science investigations. The integrated sensing platform combines hardware, in the form of communication/sensor devices, and simple mobility platforms for re-positioning sensor devices in response to changes in science demand, sensor failure, and/or communication dropout. This system of mobile sensors is conceptually described as a decentralized network of in-situ sensors to which scientists define objectives by identifying specific formations (or topologies) over specified regions that the sensor network should assume. To realize this science formation, software control is instituted for adaptive reconfiguration of the network that changes network topology, in effect establishing a self-adapting sensor network. This occurs in order to maintain the desired science-driven configuration in spite of changes in science demand, sensor failures or communication drop-outs.


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