Event-triggered distributed optimization in sensor networks

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 ldquoerrorrdquo 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.