On the benefits of saturating information in consensus networks with noise Abstract   In a consensus network subject to nonzero mean noise, the system state may be driven away even when the disagreement exhibits a bounded response. This is unfavourable in applications since the nodes may not work properly and even be faulty outside their operating region. In this paper, we propose a new control algorithm to mitigate this issue by assigning each node a favourite interval that characterizes the nodes desired convergence region. The algorithm is implemented in a selftriggered fashion. If the nodes do not share a global clock, the network operates in a fully asynchronous mode. By this algorithm, we show that the state evolution is confined around the favourite interval and the node disagreement is bounded by a simple linear function of the noise magnitude, without requiring any priori information on the noise. We also show that if the nodes share some global information, then the algorithm can be adjusted to make the nodes evolve into the favourite interval, improve on the disagreement bound and achieve asymptotic consensus in the noiseless case.

Userpreferenceaware Privatepreserving Average Consensus Privacypreserving average consensus is important for multiagent systems and has been extensively studied. Considering the more practical scenario that different users may have different privacy preferences, there exists a tradeoff between the convergence time and the data privacy protection. In this paper, we design the added noises with the optimal variances considering the heterogeneous user preferences. We develop a novel definition on the convergence time for ensuring (xcexb1, xcexb3) accuracy, which depicts how fast the consensus protocol can converge to the average with a bounded deviation, xcexb1, with a probability no smaller than 1 xe2x80x93 xcexb3. We obtain the analytical expression for the upper bound of the convergence time. We design the utility of each agent which is inversely proportional to the privacy preference, such that some user may prefer a lower privacy for a higher utility. With the introduction of the reward incentive mechanism, we then formulate optimization problems to optimize the distribution and the variance of the added noises. Simulations were conducted to verify the analysis.