Decentralized Clustering and Linking by Networked Agents

We consider the problem of decentralized clustering and estimation over multi-task networks, where agents infer and track different models of interest. The agents do not know beforehand which model is generating their own data. They also do not know which agents in their neighborhood belong to the same cluster. We propose a decentralized clustering algorithm aimed at identifying and forming clusters of agents of similar objectives, and at guiding cooperation to enhance the inference performance. One key feature of the proposed technique is the integration of the learning and clustering tasks into a single strategy. We analyze the performance of the procedure and show that the error probabilities of types I and II decay exponentially to zero with the step-size parameter. While links between agents following different objectives are ignored in the clustering process, we nevertheless show how to exploit these links to relay critical information across the network for enhanced performance. Simulation results illustrate the performance of the proposed method in comparison to other useful techniques

Modeling and Simulation of Multi-Task Networks Using Adaptation and Learning

This PhD thesis focuses on cooperative multi-task networks. Cooperative networks consist of a collection of agents with adaptation and learning abilities. The idea of sharing data among the neighboring agents is the basic tool for designing distributed algorithms for cooperative networks without a fusion center. This key technique is inspired by the collective behavior of some animal groups such as bee swarms, bacteria colonies, starling flocks, and fish schools. In these cases and in many more, the group of individuals as a whole exhibits a behavior that cannot be accessed at the individual members. This organized behavior can be understood by considering the large amount of interactions among agents. There arises the need in many network applications to infer and track different models of interest in an environment. In our research, we focus on multi-task networks where the individual agents might be interested in different objectives. One challenge in these networks is that the agents do not know beforehand which models are being observed by their neighbors. Furthermore, the total number of the observed models and their indices are not available to them, either. We propose a distributed clustering technique that allows the agents to learn and form their clusters from streaming data in a robust manner. Once clusters have been formed, cooperation among agents with similar objectives can increase the performance of the inference task. Based on the cluster formation, the unused links among the agents that track different models are exploited to link the agents that are interested in the same model but do not have direct links between each other. We analyze the performance of the clustering scheme and show that the clustering error probabilities decay exponentially to zero. In addition, we examine the mean-square performance of the proposed clustering scheme. Furthermore, we propose a distributed labeling system, which ensures that each cluster has a unique index for its observed model. Certain types of animal groups, such as bee swarms, consist of informed and uninformed agents where only the informed agents collect information about the environment. We consider a network where the informed agents observe different models and send information about them to the uninformed ones. Each uninformed agent responds to one informed agent and joins its group. We suggest an adaptive and distributed clustering and partitioning approach that allows the informed agents in the network to be clustered into different groups according to the observed models; then we apply a decentralized strategy to split the uninformed agents into groups of approximately equal size around the informed agents. In some other situations, the agents in the network need to decide between multiple options, for example,to track only one of multiple food sources. We propose a distributed decision-making approach over adaptive networks where agents in the network collect data generated by different models. The agents need to decide which model to estimate and track.Once the network reaches an agreement on one desired model, the cooperation among the agents enhances the performance of the estimation task by relaying data throughout the network. We investigate all scenarios and approaches in both cases: static and mobile networks.The simulations illustrate the performance of the proposed strategies and compare them with state-of-the-art approaches

Decentralized clustering over adaptive networks

Cooperation among agents across the network leads to better estimation accuracy. However, in many network applications the agents infer and track different models of interest in an environment where agents do not know beforehand which models are being observed by their neighbors. In this work, we propose an adaptive and distributed clustering technique that allows agents to learn and form clusters from streaming data in a robust manner. Once clusters are formed, cooperation among agents with similar objectives then enhances the performance of the inference task. The performance of the proposed clustering algorithm is discussed by commenting on the behavior of probabilities of erroneous decision. We validate the performance of the algorithm by numerical simulations, that show how the clustering process enhances the mean-square-error performance of the agents across the net work