Computational and Compressed Sensing Optimizations for Information Processing in Sensor Network

In 1996, Iyengars group, in collaboration with Brooks and with funding from Oak Ridge National Laboratory, invented a method of fault tolerance modeling that offered a computationally inspired real-time task management solution. The algorithm referred to as BrooksIyengar algorithm or BrooksIyengar hybrid algorithm is a distributed algorithm, that improves both the precision and accuracy of the measurements taken by a distributed sensor network, even in the presence of faulty sensors. The algorithm does this by exchanging the measured value and accuracy value at every node with every other node. In addition, it computes the accuracy range and a measured value for the whole network from all of the values collected. The algorithm demonstrated that even if some of the data from some of the sensors is faulty, the sensor network does not malfunction. The group, in their investigation observed that mapping a group of sensor nodes to estimate it value accurately needs all the sensors to exchange the values to each other making it computationally expensive. In their framework the sensors array that are highly distributed containing individual sensors measure a common phenomenon. The real-time sensor stream is sent to a virtual sensor which aggregates the setpoint only from good sensors. If the sensors are able to communicate within themselves then some of the redundant information can be eliminated, but such corporation would be highly energy inefficient. Design of such distributed systems would not know how many faulty sensors are present, so the use of Byzantine algorithm allows to solve this gap.