Abstract-The paper examines the performance as well as energy consumption issues of a wireless sensor network providing periodic data from a sensing field to a remote receiver. The sensors are assumed to be randomly deployed. We distinguish between two types of sensor organizations, one with a single layer of identical sensors (homogeneous) and one with an additional overlay of fewer but more powerful sensors (heterogeneous). We formulate the energy consumption and study their estimated lifetime based on a clustering mechanism with varying parameters related to the sensing field, e.g., size, and distance. We quantify the optimal number of clusters based on our model and show how to allocate energy between different layers.

Alternating direction method of multiplier (ADMM) is a powerful method to solve decentralized convex optimization problems. In distributed settings, each node performs computation with its local data and the local results are exchanged among neighboring nodes in an iterative fashion. During this iterative process the leakage of data privacy arises and can accumulate significantly over many iterations, making it difficult to balance the privacy-accuracy tradeoff. We propose Recycled ADMM (R-ADMM), where a linear approximation is applied to every even iteration, its solution directly calculated using only results from the previous, odd iteration. It turns out that under such a scheme, half of the updates incur no privacy loss and require much less computation compared to the conventional ADMM. Moreover, R-ADMM can be further modified (MR-ADMM) such that each node independently determines its own penalty parameter over iterations. We obtain a sufficient condition for the convergence of both algorithms and provide the privacy analysis based on objective perturbation. It can be shown that the privacyaccuracy tradeoff can be improved significantly compared with conventional ADMM.

Abstract-In this study, we consider optimal opportunistic spectrum access (OSA) policies for a transmitter in a multichannel wireless system, where a channel can be in one of multiple states. In such systems, the transmitter typically does not have complete information on the channel states, but can learn by probing individual channels at the expense of certain resources, e.g., energy and time. The main goal is to derive optimal strategies for determining which channels to probe, in what sequence, and which channel to use for transmission. We consider two problems within this context and show that they are equivalent to different data maximization and throughput maximization problems. For both problems, we derive key structural properties of the corresponding optimal strategy. In particular, we show that it has a threshold structure and can be described by an index policy. We further show that the optimal strategy for the first problem can only take one of three structural forms. Using these results, we first present a dynamic program that computes the optimal strategy within a finite number of steps, even when the state space is uncountably infinite. We then present and examine a more efficient, but suboptimal, two-step look-ahead strategy for each problem. These strategies are shown to be optimal for a number of cases of practical interest. We examine their performance via numerical studies.

Deep Neural Networks (DNNs) have been widely applied in various recognition tasks. However, recently DNNs have been shown to be vulnerable against adversarial examples, which can mislead DNNs to make arbitrary incorrect predictions. While adversarial examples are well studied in classification tasks, other learning problems may have different properties. For instance, semantic segmentation requires additional components such as dilated convolutions and multiscale processing. In this paper, we aim to characterize adversarial examples based on spatial context information in semantic segmentation. We observe that spatial consistency information can be potentially leveraged to detect adversarial examples robustly even when a strong adaptive attacker has access to the model and detection strategies. We also show that adversarial examples based on attacks considered within the paper barely transfer among models, even though transferability is common in classification. Our observations shed new light on developing adversarial attacks and defenses to better understand the vulnerabilities of DNNs.

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