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We consider a dynamic average consensus problem where a group of agents is required to track the average of their time-varying inputs. We assume that the inputs are sinusoidal with a single unknown frequency. We develop a distributed two-time-scale estimator that estimates the unknown frequency and achieves average consensus of the inputs. We establish input-to-state (ISS) properties of the estimator...
We consider a scenario in which leaders are required to recruit teams of followers. Each leader cannot recruit all followers, but interaction is constrained according to a bipartite network. The objective for each leader is to reach a state of local stability in which it controls a team whose size is equal to a given constraint. We focus on distributed strategies, in which agents have only local information...
Network utility maximization provides an important method for network architecture and distributed algorithm design. This method is often used in previous work as a determining utility model, but in practice the utility model of users in networks is stochastic. In this paper, stochastic network utility maximization is transform into determining model by optimizing time averages, then it is easy to...
This paper deals with clustering of spatially distributed data using wireless sensor networks. A distributed low-complexity clustering algorithm is developed that requires one-hop communications among neighboring nodes only, without local data exchanges. The algorithm alternates iterations over the variables of a consensus-based version of the global clustering problem. Using stability theory for...
Designing efficient scheduling algorithms is an important problem in a general class of networks with resource-sharing constraints, such as wireless networks and stochastic processing networks [7]. In [5], we proposed a distributed scheduling algorithm that can achieve the maximal throughput in such networks under certain conditions. This algorithm was inspired by CSMA (Carrier Sense Multiple Access)...
We present a distributed learning algorithm for optimising transit prices in a negotiation problem in the inter-domain routing framework. We present a combined game theoretic and distributed algorithmic analysis, where the notion of Nash equilibrium with the first approach model meets the notion of stability in the second. We show that minimum cost providers can learn how to strategically set their...
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