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Abstract-We present an efficient distributed online learning scheme to classify data captured from distributed, heterogeneous, and dynamic data sources. Our scheme consists of multiple distributed local learners, that analyze different streams of data that are correlated to a common event that needs to be classified. Each learner uses a local classifier to make a local prediction. The local predictions are then collected by each learner and combined using a weighted majority rule to output the final prediction. We propose a novel online ensemble learning algorithm to update the aggregation rule in order to adapt to the underlying data dynamics. We rigorously determine a bound for the worst-case mis-classification probability of our algorithm which depends on the mis-classification probabilities of the best static aggregation rule, and of the best local classifier. Importantly, the worst-case mis-classification probability of our algorithm tends asymptotically to 0 if the mis-classification probability of the best static aggregation rule or the mis-classification probability of the best local classifier tend to 0. Then we extend our algorithm to address challenges specific to the distributed implementation and we prove new bounds that apply to these settings. Finally, we test our scheme by performing an evaluation study on several data sets. When applied to data sets widely used by the literature dealing with dynamic data streams and concept drift, our scheme exhibits performance gains ranging from 34% to 71% with respect to state-of-the-art solutions.

he world is increasingly information-driven. Vast amounts of data are being produced by different sources and in diverse formats, including physiological measurements [1], tweets [2], and multimedia files [3]. Many businesses and government institutions are also embracing automation, and relying on a variety of sensors and infrastructure to collect, store, and analyze data on a continuous basis. It is becoming critical to endow assessment systems with the ability to process streaming information from sensors in real time in order to better manage physical systems, derive informed decisions, tweak production processes, and optimize logistics choices.Stream mining refers to the broad class of techniques that can be used in sense-and-respond systems that continuously receive data streams from multiple sources and employ analytics aimed at detecting multiple concepts and turning the data into actionable information. An example of a stream mining application (SMA) is the surveillance application represented in Fig. 1, which is adopted as a case study throughout the article. In this application, multiple aerial and ground reconnaissance videos are collected by different cameras and processed in real time by a network of classifiers that are trained to detect different high-level semantic features. In practice, the classifiers are localized across distributed and interconnected processing nodes. Figure 2 shows a specific classifier network for a video stream acquired by a single camera, whereas Fig. 3 shows an example of how the classifiers are localized across distributed and interconnected processing nodes. The configu-

Relay sharing has been recently investigated to increase the performance of coexisting wireless multi-hop networks. In this paper, we analyze a scenario where two wireless ad hoc networks are willing to share some of their nodes, acting as relays, in order to gain benefits in terms of lower packet delivery delay and reduced loss probability. Bayesian network analysis is exploited to compute the probabilistic relationships between local parameters and overall performance, whereas the selection of the nodes to share is made by means of a game theoretic approach. Our results are then validated through the use of a system level simulator, which shows that an accurate selection of the shared nodes can significantly increase the performance gain with respect to a random selection scheme

We propose a general parametrizable model to capture the dynamic interaction among bacteria in the formation of micro-colonies. micro-colonies represent the first social step towards the formation of structured multicellular communities known as bacterial biofilms, which protect the bacteria against antimicrobials. In our model, bacteria can form links in the form of intercellular adhesins (such as polysaccharides) to collaborate in the production of resources that are fundamental to protect them against antimicrobials. Since maintaining a link can be costly, we assume that each bacterium forms and maintains a link only if the benefit received from the link is larger than the cost, and we formalize the interaction among bacteria as a dynamic network formation game. We rigorously characterize some of the key properties of the network evolution depending on the parameters of the system. In particular, we derive the parameters under which it is guaranteed that all bacteria will join micro-colonies and the parameters under which it is guaranteed that some bacteria will not join micro-colonies. Importantly, our study does not only characterize the properties of networks emerging in equilibrium, but it also provides important insights on how the network dynamically evolves and on how the formation history impacts the emerging networks in equilibrium. This analysis can be used to develop methods to influence onthe-fly the evolution of the network, and such methods can be useful to treat or prevent biofilm-related diseases.