Hierarchical Decentralized Averaging for Wireless Packet Network

Describing and analyzing a hierarchical algorithm, for solving the distributed average consensus problem in wireless sensor networks and overhead of message. The algorithm deals the problem by recursively partitioning a given network into sub networks. Initially, nodes at the finest scale gossip to compute local averages. Using multi-hop communication and geographic routing to gossip between nodes that are not directly connected, local averages are used to compute global average. To attain hierarchical scheme with k levels this is competitive with state-of-the-art randomized gossip algorithms in terms of accuracy, message complexity, node memory. In networks this results in less congestion and resource usage by reducing message retransmissions. Simulations of the proposed scheme compared with theory and existing algorithms based on averaging along paths. Characterizing scaling laws or the rate at which the communication cost increases as a function of network size and to achieve two goals the longest distance a message travels should be much shorter than previous methods and also Distributing the computation evenly across network.