The concrete steps of Brooks–Iyengar algorithm are shown in this section. The "fused" measurement is a weighted average of the midpoints of the regions found.
Each PE exchanges their measured interval with all other PEs in the network. The Brooks–Iyengar algorithm is executed in every processing element (PE) of a distributed sensor network.
The algorithm has applications in distributed control, software reliability, High-performance computing, etc.
It is possible to modify this algorithm to correspond to Crusader's Convergence Algorithm (CCA), however, the bandwidth requirement will also increase. The algorithm runs in O( Nlog N) where N is the number of PEs. The output of the algorithm is a real value with an explicitly specified accuracy. It takes as input either real values with inherent inaccuracy or noise (which can be unknown), or a real value with apriori defined uncertainty, or an interval. The algorithm assumes N processing elements (PEs), t of which are faulty and can behave maliciously. Essentially, it combines Dolev's algorithm for approximate agreement with Mahaney and Schneider's fast convergence algorithm (FCA). This seminal algorithm unified these disparate fields for the first time. It bridges the gap between sensor fusion and Byzantine fault tolerance. The Brooks–Iyengar hybrid algorithm for distributed control in the presence of noisy data combines Byzantine agreement with sensor fusion.