Data aggregation in partially connected networks

With the diverse new capabilities that sensor and ad-hoc networks can provide, applicability of data aggregation is growing. Data aggregation is useful in dealing with multi-value domain information, which often requires approximate agreement decisions among nodes. In contrast to fully connected networks, the research on data aggregation for partially connected networks is very limited. This is due to the complexity of formal proofs and the fact that a node may not have a global view of the entire network, which makes it difficult to attain the convergence properties. The complexity of the problem is compounded in the presence of message dropouts, faults, and orchestrated attacks. By exploiting the properties of Discrete Markov Chains, this study investigates the data aggregation problem for partially connected networks to obtain: the number of rounds of message-exchanges needed to reach a network-convergence, the average convergence rate in a round of message exchange, and the number of rounds required to reach a stationary-convergence.