This article demonstrates how to use multiple channels to improve communication performance in Wireless Sensor Networks (WSNs). We first investigate multichannel realities in WSNs through intensive empirical experiments with Micaz motes. Our study shows that current multichannel protocols are not suitable for WSNs because of the small number of available channels and unavoidable time errors found in real networks. With these observations, we propose a novel tree-based, multichannel scheme for data collection applications, which allocates channels to disjoint trees and exploits parallel transmissions among trees. In order to minimize interference within trees, we define a new channel assignment problem that is proven NP-complete. Then, we propose a greedy channel allocation algorithm that outperforms other schemes in dense networks with a small number of channels. We implement our protocol, called the Tree-based, Multichannel Protocol (TMCP), in a real testbed. To adjust to networks with link quality heterogeneity, an extension of TMCP is also proposed. Through both simulation and real experiments, we show that TMCP can significantly improve network throughput and reduce packet losses. More important, evaluation results show that TMCP better accommodates multichannel realities found in WSNs than other multichannel protocols.

In this paper we propose new membership attacks and new attack methods against deep generative models including Variational Autoencoders (VAEs) and Generative Adversarial Networks (GANs). Specifically, a membership attack is to check whether a given instance x was used in the training data or not. And a co-membership attack is to check whether the given bundle n instances were in the training, with the prior knowledge that the bundle was either entirely used in the training or none at all. Successful membership attacks can compromise privacy of training data when the generative model is published. Our main idea is to cast membership inference of target data x as the optimization of another neural network (called the attacker network) to search for the seed to reproduce x. The final reconstruction error is used directly to conclude whether x is in the training data or not. We show through experiments on a variety of data sets and a suite of training parameters that our attacker network can be more successful than prior membership attacks; co-membership attack can be more powerful than single attacks; and VAEs are more susceptible to membership attacks compared to GANs in general. We also discussed membership attack with model generalization, overfitting, and diversity of the model.

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Abstract. We present a fully closed-loop design for an artificial pancreas (AP) which regulates the delivery of insulin for the control of Type I diabetes. Our AP controller operates in a fully automated fashion, without requiring any manual interaction (e.g. in the form of meal announcements) with the patient. A major obstacle to achieving closedloop insulin control is the uncertainty in those aspects of a patient's daily behavior that significantly affect blood glucose, especially in relation to meals and physical activity. To handle such uncertainties, we develop a data-driven robust model-predictive control framework, where we capture a wide range of individual meal and exercise patterns using uncertainty sets learned from historical data. These sets are then used in the controller and state estimator to achieve automated, precise, and personalized insulin therapy. We provide an extensive in silico evaluation of our robust AP design, demonstrating the potential of this approach, without explicit meal announcements, to support high carbohydrate disturbances and to regulate glucose levels in large clusters of virtual patients learned from population-wide survey data.

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