On Comparative Study of Deterministic Linear Consensus-based Algorithms for Distributed Summing

Application of data aggregation mechanisms is supposed to ensure high confidence of measurements and low energy demands in wireless sensor networks. Therefore, many modern applications utilize distributed algorithms for aggregate function estimation in order to minimize negative factors affecting the operation of the wireless sensor networks. This paper is concerned with deterministic linear consensus-based algorithms for distributed summing or more specifically, a comparative study of five frequently applied algorithms from this algorithm category over random graphs and random geometric graphs. The selected algorithms are examined using various methodologies and metrics.