Split Distributed Computing in Wireless Sensor Networks

Distributed Aggregate Function Estimation by Biphasically Configured Metropolis-Hasting Weight Model

“…The state consensus is achieved by the non-linear term, while the performance optimization is ensured by the linear term to some degree. The authors of paper [29] present an optimization mechanism based on a division of the computation process into two phases. The first one is the phase of reaching the local consensuses and the second one is the phase of reaching the global consensus.…”

The Distributed Convergence Classifier Using the Finite Difference

“…It is a distributed converging algorithm whose purpose is that each node in a WSN converges to the value counted as the average from all the initial values [11]. Thanks to its simplicity, it is widely implemented into WSNs [12].…”

“…Its higher value ensures a higher computation rate, however, it might cause the divergence of the algorithm. The interval in which the algorithm converges is determined as follows [11], [13]:…”

“…The validity of (11) indicates that the algorithm converges, as that of (12) indicates that the algorithm diverges. Here, Δk equals 1, which is the minimal change within the set of the integers.…”

“…Thus, in order to detect the convergence/the divergence, each node has to store the information about the change of its inner values (last two changes). The used mathematical tool in (11) and (12) is called the forward finite difference of the first order and is defined as follows [33]:…”