Network Size Estimation Using Distributed Orthogonalization

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

“…[17], [18]. In this paper, we focus on the Metropolis-Hasting model, which requires only the locallyavailable information for the initial configuration and therefore, it is one of the most preferred solutions for a real implementation into battery-constrained systems [19].…”

“…In [19], [22][23][24][25], the authors' attention focuses on the Metropolis-Hasting weight model (also in various applications as a complementary mechanism), which our optimized weight model is derived from. It was developed by Metropolis, Rosenbluth and Teller in 1953 and generalized by Hastings in 1970.…”

“…The Maximum Degree weight model is based on the setting of the parameter ε to the value equaled to the reciprocal of the number of the neighbors of the best-connected node in a network. The initial configuration requires the knowledge about this value and therefore, it is necessary to implement a supplementary mechanism to determine it [19].…”

“… The first examined model is the Metropolis-Hasting weight model whose weight matrix is defined as follows [19], [22][23][24][25]:…”

“…In order to get this information in a distributed manner, it is necessary to implement a complementary algorithm [19].…”