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
Distributed optimization in sensor networks
Proceedings of the 3rd international symposium on Information processing in sensor networks
An energy-aware data-centric generic utility based approach in wireless sensor networks
Proceedings of the 3rd international symposium on Information processing in sensor networks
The price of being near-sighted
SODA '06 Proceedings of the seventeenth annual ACM-SIAM symposium on Discrete algorithm
Optimal Resource Allocation in Wireless Ad Hoc Networks: A Price-Based Approach
IEEE Transactions on Mobile Computing
Hop-by-hop congestion control over a wireless multi-hop network
IEEE/ACM Transactions on Networking (TON)
Distributed network utility maximization using event-triggered augmented Lagrangian methods
ACC'09 Proceedings of the 2009 conference on American Control Conference
Energy optimization in wireless medical systems using physiological behavior
WH '10 Wireless Health 2010
Self-triggered coordination of robotic networks for optimal deployment
Automatica (Journal of IFAC)
Behavior-oriented data resource management in medical sensing systems
ACM Transactions on Sensor Networks (TOSN)
Distributed convergence to Nash equilibria in two-network zero-sum games
Automatica (Journal of IFAC)
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Many problems in sensor networks can be formulated as optimization problems. Existing distributed optimization algorithms typically rely on choosing a step size to ensure convergence. In this case, the communication between sensor nodes occurs each time the computations are carried out. Since in sensor networks, the energy required for communication can be significantly greater than the energy required to perform computation, it would be beneficial if we can somehow separate communication and computation. This paper presents such a distributed algorithm called the event-triggered algorithm. Under event triggering, each agent broadcasts to its neighbors when a local “error” signal exceeds a state dependent threshold. We give a general class of problems in sensor networks where the event-triggered algorithm can be used. In particular, this paper uses the data gathering problem as an example. We propose an event-triggered distributed algorithm and prove its convergence. Simulation results show that the proposed algorithm reduces the number of message exchanges by two orders of magnitude compared to commonly used dual decomposition algorithms. It also enjoys better scalability with respect to the depth of the tree and the maximum branch number of the tree.