Brooks–Iyengar algorithm

[3] The Brooks–Iyengar hybrid algorithm for distributed control in the presence of noisy data combines Byzantine agreement with sensor fusion.

It bridges the gap between sensor fusion and Byzantine fault tolerance.

The algorithm assumes N processing elements (PEs), t of which are faulty and can behave maliciously.

The algorithm has applications in distributed control, software reliability, High-performance computing, etc.

[7] The Brooks–Iyengar algorithm is executed in every processing element (PE) of a distributed sensor network.

The "fused" measurement is a weighted average of the midpoints of the regions found.

We draw a Weighted Region Diagram (WRD) of these intervals, then we can determine

1983 Approximate Consensus:[10] The method removes some values from the set consists of scalars to tolerant faulty inputs.

2013 Multidimensional Agreement:[12] The method also use vectors as the input while the measure of distance is different.

We could use Approximate Consensus (scalar-based), Brooks-Iyengar Algorithm (interval-based) and Byzantine Vector Consensus (vector-based) to deal with interval inputs, and the paper [3] proved that Brooks–Iyengar algorithm is the best here.

Brooks–Iyengar algorithm is a seminal work and a major milestone in distributed sensing, and could be used as a fault tolerant solution for many redundancy scenarios.

[14] In 1996, the algorithm was used in MINIX to provide more accuracy and precision, which leads to the development of the first version of RT-Linux.

Acoustic, seismic and motion detection readings from multiple sensors are combined and fed into a distributed tracking system.

Besides, it was used to combine heterogeneous sensor feeds in the application fielded by BBN Technologies, BAE systems, Penn State Applied Research Lab(ARL), and USC/ISI.

The Thales Group, a UK Defense Manufacturer, used this work in its Global Operational Analysis Laboratory.

Also, the research in developing this algorithm results in the tools used by the US Navy in its maritime domain awareness software.

In addition to the area of sensor network, other fields such as time-triggered architecture, safety of cyber-physical systems, data fusion, robot convergence, high-performance computing, software/hardware reliability, ensemble learning in artificial intelligence systems could also benefit from Brooks–Iyengar algorithm.