Optimization flow control—I: basic algorithm and convergence
IEEE/ACM Transactions on Networking (TON)
Energy-Efficient Communication Protocol for Wireless Microsensor Networks
HICSS '00 Proceedings of the 33rd Hawaii International Conference on System Sciences-Volume 8 - Volume 8
Optimal Resource Allocation in Wireless Ad Hoc Networks: A Price-Based Approach
IEEE Transactions on Mobile Computing
Distributed network utility maximization using event-triggered augmented Lagrangian methods
ACC'09 Proceedings of the 2009 conference on American Control Conference
Event-triggered distributed optimization in sensor networks
IPSN '09 Proceedings of the 2009 International Conference on Information Processing in Sensor Networks
Distributed network utility maximization using event-triggered augmented Lagrangian methods
ACC'09 Proceedings of the 2009 conference on American Control Conference
Almost sure stability of networked control systems under exponentially bounded bursts of dropouts
Proceedings of the 14th international conference on Hybrid systems: computation and control
A class of distributed optimization methods with event-triggered communication
Computational Optimization and Applications
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Many problems associated with networked systems can be formulated as network utility maximization (NUM) problems. Dual decomposition is a widely used distributed algorithm that solves the NUM problem. This approach, however, uses a step size that is inversely proportional to measures of network size such as maximum path length or maximum neighborhood size. As a result, the number of messages exchanged between nodes by dual decomposition scales poorly with respect to these measures. This paper investigates the use of an event-triggered communication scheme in distributed NUM algorithms. Under event triggering, each agent broadcasts to its neighbors when a local "error" signal exceeds a state dependent threshold. In particular, this paper proposes an event-triggered distributed NUM algorithm based on the augmented Lagrangian methods. The algorithm converges to the optimal solution. Simulation results show that the proposed algorithm reduces the number of message exchanges by two orders of magnitude, and is scale-free with respect to the above two measures of network size.